@conference {cJimenezb, title = {Segmenting Invasive and In Situ Carcinoma in Breast WSIs with a Pretrained Histopathology Transformer}, booktitle = {2nd Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in breast care - MICCAI2025}, year = {In Press}, author = {Lauren Jimenez and Felip Vilardel and Ana Petit and Eduard Dorca and Teresa Soler-Mons{\'o} and Irma Ramos-Oliver and Laura Pons and Pedro L. Fern{\'a}ndez and Laia Adalid-Llansa and Daniel Mata and Lourdes Salazar-Huayna and Casas, J. and Ver{\'o}nica Vilaplana} } @article {aSole25, title = {A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid Estimation}, journal = {Digital Discovery}, year = {2025}, month = {01/2025}, doi = {10.1039/D4DD00352G}, author = {Sol{\'e}, A. and Mosella-Montoro, Albert and Cardona, J. and G{\'o}mez-Coca, S. and Aravena, D. and Ruiz, E. and Ruiz-Hidalgo, J.} } @conference {cSaez25, title = {Closing the Loop: Continuous AI Model Improvement Through Pathologist-Guided Feedback}, booktitle = {21st European Congress on Digital Pathology}, year = {2025}, month = {06/2025}, publisher = {The European Society of Digital and Integrative Pathology}, organization = {The European Society of Digital and Integrative Pathology}, address = {Barcelona}, abstract = {

AI integration in digital pathology requires continuous refinement, not just initial deployment. Annotating large datasets is time-consuming, and training an optimal model upfront is impractical. As part of the DigiPatICS project, our approach enables ongoing improvement of deployed algorithms through collaboration between pathologists and data scientists in a structured feedback loop with minimal workflow disruption. This accelerates database growth with task-specific annotations. Moreover, involving different end users reduces bias and enhances diversity and generalizability.

}, url = {https://www.ecdp2025.org/ecdp2025-program.pdf$\#$?page=181}, author = {Laura S{\'a}ez and Sonia Rabanaque and Casas, J. and Marqu{\'e}s, F. and Salembier, P.} } @article {aHernandeza, title = {Comparative Analysis and Interpretability of Survival Models for Melanoma Prognosis}, journal = {Computers in Biology and Medicine}, volume = {190}, year = {2025}, month = {05/2025}, abstract = {

The increasing incidence of cutaneous melanoma highlights the critical need for precision medicine to optimize treatment strategies and improve patient outcomes. This study evaluated and compared the performance and interpretability of various survival models for melanoma patients. We analyzed survival outcomes, including overall survival (OS), specific survival (SS), and disease-free survival (DFS), using the Xarxa Melanoma database, which comprises data from over 9,000 patients across 19 hospitals collected over a decade. The performance of Cox Proportional Hazards, Random Survival Forest, XGBoost, DeepSurv, and DeepHit models was assessed using the Concordance Index (C-Index), time-dependent Area Under the Curve (tdAUC), ROC-AUC, and Inverse Brier score. Our analysis showed that the Random Survival Forest outperformed the other methods, achieving C-Index scores of 0.846 for OS, 0.869 for SS, and 0.846 for DFS. Furthermore, by applying an interpretability method specifically designed for survival analysis, we identified histological subtypes associated with poor prognoses. Additionally, this study reaffirms the established importance of the Breslow index as a key prognostic factor in melanoma survival prediction.

}, issn = {0010-4825}, doi = {https://doi.org/10.1016/j.compbiomed.2025.110027}, url = {https://www.sciencedirect.com/science/article/pii/S0010482525003786?via\%3Dihub}, author = {Carlos Hernandez and Sebastian Podlipnik and Joan Ficapal and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana} } @article {aCardona25, title = {Exploring Hyperfine Coupling in Molecular Qubits}, journal = {Chemical Science}, year = {2025}, month = {05/2025}, pages = {11291 - 11303}, abstract = {

Molecular qubits represent a promising avenue for advancing quantum sensing and computing technologies, yet significant challenges remain in optimising their performance. Hyperfine coupling has a critical influence on molecular qubit properties. While previous studies have exhaustively investigated this phenomenon, a comprehensive understanding of the underlying mechanisms across different systems remains elusive. A benchmark test was performed using DFT to assess which methodology worked best to accurately predict hyperfine coupling constants in molecular qubits predominantly composed of VIV and CuII. We systematically analysed the decomposition of hyperfine coupling and examined how variations in coordination sphere and molecular geometry impact dipolar, isotropic and spin-orbit contributions. By modelling diverse systems, we demonstrate how molecular design can fine-tune hyperfine coupling contributions, either minimising overall interaction or enhancing coupling along specific axes. This study provides useful insights into the structure-property relationships governing hyperfine coupling mechanisms and assesses the accuracy of different choices of density functional, basis sets and relativistic corrections in the prediction of hyperfine coupling constants.

}, doi = {https://doi.org/10.1039/D5SC02500A}, author = {Cardona, J. and Sol{\'e}, A. and Mella, P. and Aravena, D. and Ruiz-Hidalgo, J. and G{\'o}mez-Coca, S. and Ruiz, E.} } @article {aCumplido-Mayorala, title = {Neuroimaging-derived biological brain age and its associations with glial reactivity and synaptic dysfunction cerebrospinal fluid biomarkers}, journal = {Molecular psychiatry}, year = {2025}, month = {04/2025}, abstract = {

Magnetic resonance Imaging (MRI)-derived brain-age prediction is a promising biomarker of biological brain aging. Accelerated brain aging has been found in Alzheimer{\textquoteright}s disease (AD) and other neurodegenerative diseases. However, no previous studies have investigated the relationship between specific pathophysiological pathways in AD and biological brain aging. Here, we studied whether glial reactivity and synaptic dysfunction are associated with biological brain aging in the earliest stages of the Alzheimer{\textquoteright}s\ continuum, and if these mechanisms are differently associated with AD-related cortical atrophy. We further evaluated their effects on cognitive decline. We included 380 cognitively unimpaired individuals from the ALFA+ study, for which we computed their brain-age deltas by subtracting chronological age from their brain age predicted by machine learning algorithms. We studied the cross-sectional linear associations between brain-age delta and cerebrospinal fluid (CSF) biomarkers of synaptic dysfunction (neurogranin, GAP43, synaptotagmin-1, SNAP25, and α-synuclein), glial reactivity (sTREM2, YKL-40, GFAP, and S100b) and inflammation (interleukin-6). We also studied the cross-sectional linear associations between AD signature and these CSF biomarkers, We further evaluated the mechanisms linking baseline brain-age delta and longitudinal cognitive decline by performing mediation analyses. To reproduce our findings on an independent cohort, we included 152 cognitively unimpaired and 310 mild cognitive impaired (MCI) individuals from the ADNI study. We found that higher CSF sTREM2 was associated with a younger brain-age after adjusting for AD pathology, both in ALFA+ cognitively unimpaired and in ADNI MCI individuals. Furthermore, we found that CSF sTREM2 fully mediated the link between older brain-age and cognitive decline in ALFA+. In summary, we showed that the protective microglial state reflected by higher CSF sTREM2 has a beneficial impact on biological brain aging that may partly explains the variability in cognitive decline in early AD stages, independently of AD pathology.

}, issn = {1359-4184}, doi = {https://doi.org/10.1038/s41380-025-02961-x}, url = {https://www.nature.com/articles/s41380-025-02961-x}, author = {Irene Cumplido-Mayoral and Gonzalo S{\'a}nchez-Benavides and Marta Mil{\`a} Alom{\`a} and Carles Falcon and Raffaele Cacciaglia and Carolina Minguillon and Jose L. Molinuevo and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @article {aManjabacas25, title = {Twenty years of AFORO: New developments and connections enhancing otolith research}, journal = {Fisheries Research}, volume = {281}, year = {2025}, month = {01/2025}, abstract = {

Since 2003, with the aim of addressing different issues related to otoliths and the advancement of technologies, AFORO acronym of An{\`a}lisi de FORmes d{\textquoteright}Ot{\`o}lits (otolith shape analysis), a computational environment with a set of tools including a website for this purpose, has gradually expanded its functionality. This implies different ways to measure otoliths in order to obtain different types of information of the same individual. The 2D otoliths shape description using different methods permits the comparison between classical methods and the Wavelet transform, used in the automatic classification system of AFORO. The 3D otoliths description in a few images opens the possibility of classifications using volumetric information, which they have performed well. The data of relationships between otolith and fish length in AFORO is used in predator-prey studies. Our website offers the possibility to display otolith georeferencing information interactively. Furthermore, the automatic classification system has been improved by incorporating geographic filters based on georeferenced data. The diverse information that AFORO offers on the otoliths opens the possibility to approach new studies or to improve the existing ones, combining this information or applying the most appropriate one to the specific problem. Classification cases with Wavelets that have been improved by introducing geographical area information will be presented. In addition, Deep Learning algorithms will be introduced by performing classifications with a small subset of otoliths of species which contain a sufficient number of specimens (\>8).

}, keywords = {artificial intelligence, computer Vision, deep learning, neural networks, Otoliths}, issn = {0165-7836}, doi = {https://doi.org/10.1016/j.fishres.2024.107242}, author = {A. Manjabacas and O. Chic and E. Garc{\'\i}a-Ladona and V.M. Tuset and Morros, J.R. and Elisa Sayrol and P. Mart{\'\i}-Puig and J. Forest and J. Vasconcelos and A. Lombarte} } @conference {cLazaro25, title = {Verifying Key Performance Indicators across Multiple 6G-EWOC Demonstrations}, booktitle = {European Conference on Networks and Communications (EuCNC) }, year = {2025}, month = {06/2025}, publisher = {IEEE}, organization = {IEEE}, address = {Poznan, Poland}, abstract = {

Testing, measuring, and validating Key Performance Indicators (KPIs) are crucial tasks across SNS-JU projects. These activities foster collaboration at the TMV Working Group, enabling the sharing of methods, definitions, target values, and best practices within the 6G vision. Thanks to collaboration of multiple agents, objectives can be different although the main goal of the WG is to develop and mature 6G technology. The 6G-EWOC project aims to describe how KPIs will be verified in different categories through multiple demonstrations. These categories include data rate and capacity in several technologies, sensing, positioning and localization, and energy efficiency. By addressing these different KPIs, the 6G-EWOC project ensures comprehensive evaluation and validation, contributing to the progress of the 6G vision.

}, url = {European\%20Conference\%20on\%20Networks\%20and\%20Communications\%20(EuCNC)}, author = {Jos{\'e} Antonio L{\'a}zaro and Casas, J. and Santiago Royo and Ruiz-Hidalgo, J. and F{\`a}brega, J.M. and Ricardo Mart{\'\i}nez and Renaudier, J. and Mardoyan, H. and Jensen, J.B. and Altab{\'a}s, J.A. and Pablo Garc{\'\i}a-G{\'o}mez and Jordi Riu and Baskopoulos, P. and Kalavrouziotis, D. and Syrivelis, D. and Op de Beeck, C. and Theodoropoulou, E. and Lyberopoulos, G. and Thor, J.} } @conference {cLazaro24, title = {6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving}, booktitle = {IEEE Future Networks World Forum: Symposium on Future Networks for Connected and Automated Mobility }, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Dubai}, abstract = {

The development of transport infrastructures for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles operating efficiently and safely in congestion-free traffic flows is a major challenge for telecommunications technologies.\  Simultaneous Localization and Mapping (SLAM) plays a crucial role in ensuring uninterrupted journeys for emergency vehicles and increasing the safety of vulnerable road users in complex traffic scenarios. Accurate SLAM mapping for ADAS systems requires data from different sensor technologies {\textendash}such as high-resolution cameras or Radio/Light Detection and Ranging (RaDAR/LiDAR){\textendash} to be effectively combined or fused. Sensor fusion results in high data throughput and low latency requirements. However, optimal mapping outcomes occur when processing systems fuse data from sensors positioned at different locations within the traffic scene. By crowdsourcing diverse sensors, we can multiply the view angles, mitigate occlusions and improve the overall scene coverage. Yet, this approach introduces additional challenges for communication systems within both the vehicles and the infrastructure. Addressing these challenges is essential for seamless development of safe and efficient ADAS driving techniques.

}, doi = {10.1109/FNWF63303.2024.11028711}, url = {https://ieeexplore.ieee.org/document/11028711}, author = {Jos{\'e} Antonio L{\'a}zaro and Casas, J. and Ruiz-Hidalgo, J. and Cortada-Garcia, M. and Mart{\'\i}n-Pey, G. and Salavedra-Pujol, J. and Wang, M. and Theodoropoulou, E. and Lyberopoulos, G. and Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Jordi Riu and Marcus, C. and Eriksson, O. and Schrenk, B. and F{\`a}brega, J.M.} } @conference {cSchrenk24, title = {6G-EWOC: Optical Wireless Communication in Support of Autonomous Driving}, booktitle = {IEEE Future Networks World Forum: Symposium on Future Networks for Connected and Automated Mobility }, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Dubai}, abstract = {

The road towards an efficient transport infrastructure, which safely operates fully autonomous vehicles in congestion-free traffic flows, is subject to several technological challenges that reach also into the realm of telecommunications. Future 6G networks are required to handle huge amounts of sensor information, both in terms of low-latency communication and real-time data processing. The 6G-EWOC project contributes to this challenge through a concerted use of optical wireless communication and sensing with radio-frequency based cellular technology. Three optical-wireless scenarios are discussed, including (i) instant data exchange between vehicles leveraging their lighting assets for low-latency communication, (ii) the provision of vehicular data offloading to a fiber-based 6G infrastructure through beamsteering-assisted optical remote radio heads to enable data fusion at the edge cloud, and (iii) the seamless wireless extension of fiber-grade capacity to isolated network segments situated in fiber-scarce brownfields, which is accomplished through transparent optical air interfaces that ensure fiber-grade continuity by preventing a translucent electro-optic termination of the single-mode fiber channel. Another important contribution aims to advance light- and radio-based sensing technologies towards networked versions that offer simultaneous communication and detecting capabilities.

}, doi = {10.1109/FNWF63303.2024.11028710}, url = {https://ieeexplore.ieee.org/document/11028710}, author = {Schrenk, B. and Theodoropoulou, E. and Lyberopoulos, G. and Marcus, C. and Eriksson, O. and Jos{\'e} Antonio L{\'a}zaro and Casas, J. and Federico Dios and Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Jordi Riu and F{\`a}brega, J.M.} } @article {aBarrabes, title = {Advances in Biomedical Missing Data Imputation: A Survey}, journal = {IEEE Access}, year = {2024}, month = {12/2024}, abstract = {

Ensuring data quality in biomedical sciences is crucial for reliable research outcomes, particularly as precision medicine continues to gain prominence. Missing values compromise data quality and can make it difficult to perform data-based studies. The origins of missing values in biomedical datasets are diverse, including experimental errors, equipment malfunctions, and variations in data collection protocols tailored to individual patient conditions. To address the complex nature of missing values and the unique characteristics of biomedical data, a diverse spectrum of computational imputation techniques has emerged. These methods range from traditional statistical analysis to more modern approaches such as discriminative machine learning models and deep generative networks. This survey paper provides a comprehensive overview of the extensive literature on missing data imputation techniques, with a specific focus on applications in genomics, single-cell RNA sequencing, health records, and medical imaging. We outline the fundamental principles underlying each imputation technique and present a detailed analysis of their advantages and disadvantages, categorized by missing data patterns. To aid practitioners in method selection, we offer practical recommendations based on critical factors such as dataset size, data type, and missingness rate. By synthesizing insights from existing literature, we provide a holistic perspective on the effectiveness of various imputation methods under different biomedical contexts, thereby facilitating informed decision-making for researchers and practitioners in applying imputation techniques to biomedical data processing.

}, issn = {2169-3536}, doi = {10.1109/ACCESS.2024.3516506}, url = {http://hdl.handle.net/2117/422141}, author = {Barrab{\'e}s, M{\'\i}riam and Perera, Maria and Novelle, Victor and Moriano, Xavier and Xavier Gir{\'o}-i-Nieto and Mas-Montserrat, Daniel and Ioannidis, Alexander G.} } @article {aGene-Molad, title = {AmodalAppleSize_RGB-D dataset: RGB-D images of apple trees annotated with modal and amodal segmentation masks for fruit detection, visibility and size estimation}, journal = {Data in Brief}, volume = {52}, year = {2024}, month = {02/2024}, abstract = {

The present dataset comprises a collection of RGB-D apple tree images that can be used to train and test computer vision-based fruit detection and sizing methods. This dataset encompasses two distinct sets of data obtained from a Fuji and an Elstar apple orchards. The Fuji apple orchard sub-set consists of 3925 RGB-D images containing a total of 15335 apples annotated with both modal and amodal apple segmentation masks. Modal masks denote the visible portions of the apples, whereas amodal masks encompass both visible and occluded apple regions. Notably, this dataset is the first public resource to incorporate on-tree fruit amodal masks. This pioneering inclusion addresses a critical gap in existing datasets, enabling the development of robust automatic fruit sizing methods and accurate fruit visibility estimation, particularly in the presence of partial occlusions. Besides the fruit segmentation masks, the dataset also includes the fruit size (calliper) ground truth for each annotated apple. The second sub- set comprises 2731 RGB-D images capturing five Elstar apple trees at four distinct growth stages. This sub-set includes mean diameter information for each tree at every growth stage and serves as a valuable resource for evaluating fruit sizing methods trained with the first sub-set. The present data was employed in the research papers titled "Looking behind occlusions: a study on amodal segmentation for robust on-tree apple fruit size estimation" [1] and {\textquotedblleft}Simultaneous fruit detection and size estimation using multitask deep neural networks{\textquotedblright} [2].

}, keywords = {Agricultural robotics, amodal segmentation, depth image, Fruit measurement, Fruit visibility, Instance Segmentation, modal segmentation, Yield prediction}, doi = {https://doi.org/10.1016/j.dib.2023.110000}, author = {Gen{\'e}-Mola, Jordi and Ferrer-Ferrer, M. and Hemming, J. and Dalfsen, P. and Hoog, D. and Sanz-Cortiella, R. and Rosell-Polo, Joan R. and Morros, J.R. and Ver{\'o}nica Vilaplana and Ruiz-Hidalgo, J. and Gregorio, Eduard} } @article {aHernandez, title = {Bcn20000: Dermoscopic lesions in the wild}, journal = {Nature - Scientific Data}, volume = {11}, year = {2024}, month = {06/2024}, abstract = {

Advancements in dermatological artificial intelligence research require high-quality and comprehensive datasets that mirror real-world clinical scenarios. We introduce a collection of 18,946 dermoscopic images spanning from 2010 to 2016, collated at the Hospital Cl{\'\i}nic in Barcelona, Spain. The BCN20000 dataset aims to address the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions in hard-to-diagnose locations such as those found in nails and mucosa, large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. Our dataset covers eight key diagnostic categories in dermoscopy, providing a diverse range of lesions for artificial intelligence model training. Furthermore, a ninth out-of-distribution (OOD) class is also present on the test set, comprised of lesions which could not be distinctively classified as any of the others. By providing a comprehensive collection of varied images, BCN20000 helps bridge the gap between the training data for machine learning models and the day-to-day practice of medical practitioners. Additionally, we present a set of baseline classifiers based on state-of-the-art neural networks, which can be extended by other researchers for further experimentation.

}, doi = {https://doi.org/10.1038/s41597-024-03387-w}, author = {Carlos Hernandez and Marc Combalia and Sebastian Podlipnik and Noel CF Codella and Veronica Rotemberg and Allan C Halpern and Ofer Reiter and Cristina Carrera and Alicia Barreiro and Brian Helba and Susana Puig and Ver{\'o}nica Vilaplana and Josep Malvehy} } @article {aPerez-Cano24, title = {Combining graph neural networks and computer vision methods for cell nuclei classification in lung tissue}, journal = {Heliyon}, volume = {10}, year = {2024}, month = {03/2024}, issn = {2405-8440}, doi = {https://doi.org/10.1016/j.heliyon.2024.e28463}, author = {Jose Perez-Cano and Irene Sansano Valero and D. Anglada and Oscar Pina and Salembier, P. and Marqu{\'e}s, F.} } @conference {cAnglada24, title = {Enhancing Ki-67 Cell Segmentation with Dual U-Net Models: A Step Towards Uncertainty-Informed Active Learning}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, year = {2024}, month = {06/2024}, publisher = {IEEE}, organization = {IEEE}, author = {D. Anglada and J. Sala and Marqu{\'e}s, F. and Salembier, P. and M. Pard{\`a}s} } @conference {cReixach, title = {Fast unsupervised tensor restoration via low-rank deconvolution}, booktitle = {IEEE International Conference on Image Processing (ICIP)}, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Abu Dhabi}, abstract = {

Low-rank Deconvolution (LRD) has appeared as a new multi-dimensional representation model that enjoys important efficiency and flexibility properties. In this work we ask ourselves if this analytical model can compete against Deep Learning (DL) frameworks like Deep Image Prior (DIP) or Blind-Spot Networks (BSN) and other classical methods in the task of signal restoration. More specifically, we propose to extend LRD with differential regularization. This approach allows us to easily incorporate Total Variation (TV) and integral priors to the formulation leading to considerable performance tested on signal restoration tasks such image denoising and video enhancement, and at the same time benefiting from its small computational cost.

}, keywords = {De-noising, Enhancement, Restoration, Tensors, Total Variation}, doi = {https://doi.org/10.1109/ICIP51287.2024.10647407}, url = {https://arxiv.org/abs/2406.10679}, author = {Reixach, David and Morros, J.R.} } @conference {cBonet23, title = {HyperFast: Instant Classification for Tabular Data}, booktitle = {38th Annual AAAI Conference on Artificial Intelligence (AAAI)}, year = {2024}, month = {01/2024}, abstract = {

Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming. Meanwhile, traditional machine learning methods like gradient-boosting algorithms remain the preferred choice for most tabular data applications, while neural network alternatives require extensive hyperparameter tuning or work only in toy datasets under limited settings. In this paper, we introduce HyperFast, a meta-trained hypernetwork designed for instant classification of tabular data in a single forward pass. HyperFast generates a task-specific neural network tailored to an unseen dataset that can be directly used for classification inference, removing the need for training a model. We report extensive experiments with OpenML and genomic data, comparing HyperFast to competing tabular data neural networks, traditional ML methods, AutoML systems, and boosting machines. HyperFast shows highly competitive results, while being significantly faster. Additionally, our approach demonstrates robust adaptability across a variety of classification tasks with little to no fine-tuning, positioning HyperFast as a strong solution for numerous applications and rapid model deployment. HyperFast introduces a promising paradigm for fast classification, with the potential to substantially decrease the computational burden of deep learning.

}, author = {Bonet, David and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @phdthesis {dSchurholt24, title = {Hyper-Representations: Learning from Populations of Neural Networks}, volume = {PhD}, year = {2024}, month = {05/2024}, abstract = {

Neural Networks (NNs) have undergone a remarkable evolution, transitioning from academic labs to key technologies across various domains. This proliferation underscores their capability and versatility. As these models become integral to critical decision-making processes, the demand for methods to understand their inner workings likewise becomes more pronounced. This thesis addresses the challenge of understanding NNs through the lens of their most fundamental component: the weights, which encapsulate the learned information and determine the model behavior.

The NN weight space contains complex local and global structures which makes it a challenging domain. Addressing these challenges, this thesis develops innovative representation learning methods for the domain of weight spaces. The proposed methods embed and disentangle model weights in a representation space. The representation space allows not only to analyze existing models but also to generate new models with specified characteristics. Such an analysis builds on populations of models, to develop a nuanced understanding of the structure of NN weights.

At the core of this thesis is a fundamental question: Can we learn general, task-agnostic representations from populations of Neural Network models? The key contribution of this thesis to answer that question are hyper-representations, a self-supervised method to learn representations of NN weights. Work in this thesis finds that trained NN models indeed occupy meaningful structures in the weight space, that can be learned and used. Through extensive experiments, this thesis demonstrates that hyper-representations uncover model properties, such as their performance, state of training, or hyperparameters.

Moreover, the identification of regions with specific properties in hyper-representation space allows to sample and generate model weights with targeted properties. This thesis demonstrates applications for fine-tuning, and transfer learning to great success. Lastly, it presents methods that allow hyper-representations to generalize beyond model sizes, architectures, and tasks. The practical implications of that are profound, as it opens the door to foundation models of Neural Networks, which aggregate and instantiate their knowledge across models and architectures.

Ultimately, this thesis contributes to the deeper understanding of Neural Networks by investigating structures in their weights which leads to more interpretable, efficient, and adaptable models. By laying the groundwork for representation learning of NN weights, this research demonstrates the potential to change the way Neural Networks are developed, analyzed, and used.

[My tweet]

Congratulatuon to Dr Konstantin Sch{\

}, author = {Sch{\"u}rholt, Konstantin}, editor = {Mahoney, Michael and Xavier Gir{\'o}-i-Nieto and Borth, Damian} } @conference {ca, title = {Lossless and Lossy Compression for Distributed Acoustic Sensing Using Inter-channel Predictions}, booktitle = {SSA Photonic Seismology 2024}, year = {2024}, month = {10/2024}, address = {Vancouver}, abstract = {

Distributed Acoustic Sensing (DAS) offer unique advantages to extensive monitoring initiatives due to its long-distance, high-density arrays of real-time acoustic sensors. However, high-resolution and long-term applications generate a large amount of data, on the order of hundreds of terabytes per year, presenting challenges for processing and storage. As a result, leveraging innovative data compression techniques is essential for scaling up DAS monitoring. DAS arrays are composed of multiple channels carrying highly correlated and coherent signals. These redundancies allow to exploit inter-channel compression, which use predictions from consecutive channel signals and achieve a higher compression ratio compared to compressing each channel separately.

In this work, we propose a novel coding scheme for DAS data that improves state-of-the-art compression. Encoding follows a pipeline composed of intra-prediction, inter-prediction, transform and entropy coding. For lowly correlated channels, intra-channel prediction is realized by means of Linear Prediction Coding. For inter-channel prediction, we propose three different methods: (1) prediction by warping and scaling the signal from the consecutive channel, (2) a linear predictor is learned over many channels and (3) wavelet transforms are used to disentangle data signals of different frequency, allowing fine-grained prediction. Entropy coding is done by a combination of adaptive arithmetic coding and run-length coding. The implementation is divided into an encoder and a decoder. The encoder uses bitrate optimization for decisions on the prediction methods and parameter values and can be tuned for either speed or high-compression modes. In addition, we explore lossy quantization by adding tunable quantization of the transformed signal, which we observe to achieve significantly higher compression at the expense of quantization noise. The designed algorithms and the provided implementation facilitate the deployment of long-term DAS monitoring.

}, keywords = {Distributed Acoustic Sensing, Signal compression}, url = {https://ssafallmeeting.secure-platform.com/a/gallery/rounds/39/details/12071}, author = {Segu{\'\i}, Aleix and Ugalde, Arantza and Ventosa, Sergi and Morros, J.R.} } @article {aCumplido-Mayoral, title = {The mediating role of neuroimaging-derived biological brain age between risk factors for dementia and cognitive decline in middle/late-aged asymptomatic individuals: a cohort study}, journal = {The Lancet Healthy Longevity}, year = {2024}, month = {04/2024}, abstract = {

Summary Background Neuroimaging-based brain-age delta has been shown to be a mediator linking cardiovascular risk factors to cognitive function. We aimed to assess the mediating role of brain-age delta in the association between modifiable risk factors of dementia and longitudinal cognitive decline in middle-aged and older individuals who are asymptomatic, stratified by Alzheimer{\textquoteright}s disease pathology. We also explored whether the mediation effect is specific to cognitive domain. Methods In this cohort study, we included participants from the ALFA+ cohort aged between 45 years and 65 years who were cognitively unimpaired and who had available structural MRI, cerebrospinal fluid β-amyloid (Aβ)42 and Aβ40 measurements obtained within 1 year of each other, modifiable risk factors assessment, and cognitive evaluation over 3 years. Participants were recruited from the Barcelonaβeta Brain Research Center (Barcelona, Spain). Included individuals underwent a first assessment between Oct 25, 2016, and Jan 28, 2020, and a follow-up cognitive assessment 3{\textperiodcentered}28 (SD 0{\textperiodcentered}27) years later. We computed brain-age delta and composites of different cognitive function domains (preclinical Alzheimer{\textquoteright}s cognitive composite [PACC], attention, executive function, episodic memory, visual processing, and language). We used partial least squares path modelling to explore mediation effects in the associations between modifiable risk factors (including cardiovascular, mental health, mood, metabolic or endocrine history, and alcohol use) and changes in cognitive composites. To assess the role of Alzheimer{\textquoteright}s disease pathology, we computed separate models for Aβ-negative and Aβ-positive individuals. Findings Of the 419 participants enrolled in ALFA+, 302 met our inclusion criteria, of which 108 participants were classified as Aβ-positive and 194 as Aβ-negative. In Aβ-positive individuals, brain-age delta partially mediated (percent mediation proportion 15{\textperiodcentered}73\% [95\% CI 14{\textperiodcentered}22{\textendash}16{\textperiodcentered}66]) the association between modifiable risk factors and decline in overall cognition (across cognitive domains). Brain-age delta fully mediated (mediation proportion 28{\textperiodcentered}03\% [26{\textperiodcentered}25{\textendash}29{\textperiodcentered}21]) the effect of modifiable risk factors on the PACC, wherein increased values for risk factors correlated with an older brain-age delta, and, consequently, an older brain-age delta was linked to greater PACC decline. This effect appears to be primarily driven by memory decline. Mediation was not significant in Aβ-negative individuals (3{\textperiodcentered}52\% [0{\textperiodcentered}072{\textendash}4{\textperiodcentered}17]) on PACC, although path coefficients were not significantly different from those in the Aβ-positive group. Interpretation Our findings suggest that brain-age delta captures the association between modifiable risk factors and longitudinal cognitive decline in middle-aged and older people. In asymptomatic middle-aged and older individuals who are Aβ-positive, the pathology might be the strongest driver of cognitive decline, whereas the effect of risk factors is smaller. Our results highlight the potential of brain-age delta as an objective outcome measure for preventive lifestyle interventions targeting cognitive decline.

}, doi = {10.1016/S2666-7568(24)00025-4}, author = {Irene Cumplido-Mayoral and Anna Brugulat-Serra and Gonzalo S{\'a}nchez-Benavides and Jose Luis Molinuevo and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan Domingo Gispert} } @conference {cTheodoropoulou24, title = {Towards AI-Enhanced Fibre-Wireless Optical 6G Network in Support of Connected Mobility}, booktitle = {IEEE 29th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)}, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Athens}, abstract = {

The 6G-EWOC project seeks to create an AI-enhanced fiber-wireless optical 6G network to support advanced use cases for Fully Autonomous Vehicles (FAVs) by establishing a new access network incorporating the integration of both fixed/wireless optical and wireless/RF technologies. The access network will be supported by a fast, reconfigurable, highly dynamic and customizable optical fiber-based fronthaul infrastructure which will minimize optoelectronic transitions through tunable and programmable devices, as well as low energy photonic switching of (packet/optical) spectrum and spatial resources, managed by AI-enabled SDN. End-to-end connectivity between AI-based edge computation units will enable advanced Connected and Automated Mobility (CAM) services over a rapid, adaptable network architecture.

}, doi = {10.1109/CAMAD62243.2024.10942755}, url = {https://ieeexplore.ieee.org/document/10942755}, author = {Theodoropoulou, E. and Lyberopoulos, G. and Setaki, Foteini and Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Marcus, C. and Eriksson, O. and F{\`a}brega, J.M. and Ricardo Mart{\'\i}nez and Ramon Casellas and Schrenk, B. and Casas, J. and Jos{\'e} Antonio L{\'a}zaro} } @conference {cBarrabes, title = {Adversarial Learning for Feature Shift Detection and Correction}, booktitle = {Neural Information Processing Systems (NeurIPS)}, year = {2023}, month = {12/2023}, address = {New Orleans, USA}, abstract = {

TL;DR: We introduce a framework inspired in adversarial learning to detect and correct features originating a distribution shift between datasets.

Data shift is a phenomenon present in many real-world applications, and while there are multiple methods trying to detect shifts, the task of localizing and correcting the features originating such shifts has not been studied in depth. Feature shifts can occur in many datasets, including in multi-sensor data, where some sensors are malfunctioning, or in tabular and structured data, including biomedical, financial, and survey data, where faulty standardization and data processing pipelines can lead to erroneous features. In this work, we explore using the principles of adversarial learning, where the information from several discriminators trained to distinguish between two distributions is used to both detect the corrupted features and fix them in order to remove the distribution shift between datasets. We show that mainstream supervised classifiers, such as random forest or gradient boosting trees, combined with simple iterative heuristics, can localize and correct feature shifts outperforming current statistical and neural network-based techniques.

}, author = {Barrab{\'e}s, M{\'\i}riam and Mas-Montserrat, Daniel and Geleta, Margarita and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {aCumplido-Mayoral22, title = {Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer{\textquoteright}s Disease and Neurodegeneration stratified by sex}, journal = {eLife}, volume = {12}, year = {2023}, month = {04/2023}, abstract = {

Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta) as a marker of biological brain aging. Accelerated aging has been found in neurodegenerative disorders like Alzheimer9s disease (AD), but its validation against markers of neurodegeneration and AD is lacking. Here, imaging-derived measures from the UK Biobank dataset (N=22,661) were used to predict brain-age in 2,314 cognitively unimpaired (CU) individuals at higher risk of AD and mild cognitive impaired (MCI) patients from four independent cohorts with available biomarker data: ALFA+, ADNI, EPAD and OASIS. Brain-age delta was associated with abnormal amyloid-β, more advanced stages (AT) of AD pathology and APOE-ε4 status. Brain-age delta was positively associated with plasma neurofilament light, a marker of neurodegeneration, and sex differences in the brain effects of this marker were found. These results validate brain-age delta as a non-invasive marker of biological brain aging related to markers of AD and neurodegeneration.

}, issn = {2050-084X}, doi = {https://doi.org/10.7554/eLife.81067}, author = {Irene Cumplido-Mayoral and Marina Garc{\'\i}a-Prat and Gregory Operto and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Marta Mila-Aloma and Luigi Lorenzini and Carolina Minguillon and Jose Luis Molinuevo and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan Domingo Gispert} } @conference {cCumplido-Mayoral23a, title = {Brain-age mediates the association between modifiable risk factors and cognitive decline early in the AD continuum}, booktitle = {Alzheimer{\textquoteright}s Association International Conference (AAIC)}, year = {2023}, month = {07/2023}, address = {Amsterdam, Netherlands}, author = {Irene Cumplido-Mayoral and Anna Brugulat-Serrat and Gonzalo S{\'a}nchez-Benavides and Armand Gonz{\'a}lez-Escalante and Federica Anastasi and Marta Mila-Aloma and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Carolina Minguillon and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cCumplido-Mayoral23, title = {Brain-age prediction and its associations with glial and synaptic CSF markers}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2023}, month = {07/2023}, address = {Amsterdam, Netherlands}, author = {Irene Cumplido-Mayoral and Marta Mila-Aloma and Carles Falcon and Raffaele Cacciaglia and Carolina Minguillon and Karine Fauria and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @article {aGeleta, title = {Deep Variational Autoencoders for Population Genetics}, year = {2023}, abstract = {

Motivation Modern biobanks provide numerous high-resolution genomic sequences of diverse populations. These datasets enable a better understanding of genotype-phenotype interactions with genome-wide association studies (GWAS) and power a new personalized precision medicine with polygenic risk scores (PRS). In order to account for diverse and admixed populations, new algorithmic tools are needed in order to properly capture the genetic composition of populations. Here we explore deep learning techniques, namely variational autoencoders (VAEs), to process genomic data from a population perspective. We hope this work will encourage the adoption of deep neural networks in the population genetics community.

Results In this paper, we show the power of VAEs for a variety of tasks relating to the interpretation, classification, simulation, and compression of genomic data with several worldwide whole genome datasets from both humans and canids and evaluate the performance of the proposed applications with and without ancestry conditioning. The unsupervised setting of autoencoders allows for the detection and learning of granular population structure and inferring of informative latent factors. The learned latent spaces of VAEs are able to capture and represent differentiated Gaussian-like clusters of samples with similar genetic composition on a fine-scale from single nucleotide polymorphisms (SNPs), enabling applications in dimensionality reduction, data simulation, and imputation. These individual genotype sequences can then be decomposed into latent representations and reconstruction errors (residuals) which provide a sparse representation useful for lossless compression. We show that different population groups have differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, its effect on compression across populations, its relation to historical migrations, and we show how to introduce autoencoders into existing compression pipelines.

}, doi = {https://doi.org/10.1101/2023.09.27.558320}, url = {https://www.biorxiv.org/content/10.1101/2023.09.27.558320v1}, author = {Geleta, Margarita and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @phdthesis {dMosella-Montoro23, title = {Graph Convolutional Neural Networks for 3D Data Analysis}, volume = {Phd}, year = {2023}, school = {Universitat Polit{\`e}cnica de Catalunya}, address = {Barcelona}, abstract = {

Deep Learning allows the extraction of complex features directly from raw input data, eliminating the need for hand-crafted features from the classical Machine Learning pipeline. This new paradigm brought a boost in the performance across several domains, including computer vision, natural language processing and audio processing. However, there are still challenges when dealing with unorganized structures. This thesis addresses this challenge using Graph Convolutional Neural Networks, a new set of techniques capable of managing graph structures that can be used for processing 3D data.\ \ 

The first part of the thesis focuses on the Graph Analysis task, in which we study the capabilities of Graph Convolutional Neural Networks to capture the intrinsic geometric information of 3D data. We propose the Attention Graph Convolution layer that learns to infer the kernel used during the convolution, taking into account the particularities of each neighbourhood of the graph. We explore two variants of the Attention Graph Convolution layer, one that explores a residual approach and another one that allows the convolution to combine different neighbourhood domains. Furthermore, we propose a set of 3D pooling layers that mimics the behaviour of the pooling layers found in common 2D Convolutional Neural Networks architectures. Finally, we present a 2D-3D Fusion block capable of merging the 3D geometric information that we get from a Graph Convolutional Neural Network with the texture information obtained by a 2D Convolutional Neural Network. We evaluate the presented contributions on the RGB-D Scene Classification task.\ 

The second part of this thesis focuses on the Node Analysis task, which consists of extracting features on a node level, taking into account the neighbourhood structure. We present the Multi-Aggregator Graph Convolution layer that uses a multiple aggregator approach to better generalize for unseen topologies and learn better local representations. In addition, it reduces the memory footprint with respect to the Attention Graph Convolution layer. Finally, we analyze the capabilities of our proposed Graph Convolution layers to deal with heterogeneous graphs where the nodes of the graph may belong to different modalities. We evaluate the presented contributions with the Computer Graphics process of skinning a character mesh. Specifically, we propose a Two-Stream Graph Neural Network capable of predicting the skinning weights of a 3D character.

}, url = {http://hdl.handle.net/10803/689400}, author = {Mosella-Montoro, Albert}, editor = {Ruiz-Hidalgo, J.} } @phdthesis {dFernandez23, title = {Knowledge graph population from news streams}, volume = {Doctorate}, year = {2023}, month = {10/2023}, school = {Universitat Polit{\`e}cnica de Catalunya}, type = {Industrial}, address = {Barcelona, Catalonia}, abstract = {

Media producers publish large amounts of multimedia content online - both text, audio, image and video.\  As the online media market grows, the management and delivery of contents becomes a challenge. Semantic and linking technologies can be used to organize and exploit these contents through the use of knowledge graphs. This industrial doctorate dissertation addresses the problem of constructing knowledge resources and integrating them into a system used by media producers to manage and explore their contents. For that purpose, knowledge graphs and their maintenance through Information Extraction (IE) from news streams is studied. This thesis presents solutions for multimedia understanding and knowledge extraction from online news, and their exploitation in real product applications, and it is structured in three parts.

The first part consists on the construction of IE tools that will be used for knowledge graph population. For that, we built an holistic Entity Linking (EL) system capable of combining multimodal data inputs to extract a set of semantic entities that describe news content.\  The EL system is followed by a Relation Extraction (RE) model that predicts relations between pairs of entities with a novel method based on entity-type knowledge. The final system is capable of extracting triples describing the contents of a news article.

The second part focuses on the automatic construction of a news event knowledge graph. We present an online multilingual system for event detection and comprehension from media feeds, called VLX-Stories. The system retrieves information from news sites, aggregates them into events (event detection), and summarizes them by extracting semantic labels of its most relevant entities (event representation) in order to answer four Ws from journalism: who, what, when and where.\  This part of the thesis deals with the problems of Topic Detection and Tracking (TDT), topic modeling and event representation.

The third part of the thesis builds on top of the models developed in the two previous parts to populate a knowledge graph from aggregated news.
The system is completed with an emerging entity detection module, which detects mentions of novel people appearing on the news and creates new knowledge graph entities from them. Finally, data validation and triple classification tools are added to increase the quality of the knowledge graph population.

This dissertation addresses many general knowledge graph and information extraction problems, like knowledge dynamicity, self-learning, and quality assessment. Moreover, as an industrial work, we provide solutions that were deployed in production and verify our methods with real customers.

}, keywords = {Entity Linking, Information Extraction, Knowledge Graph Population, Named Entity Disambiguation, Named Entity Recognition, Natural Language Processing, Relation Extraction, Topic Detection and Tracking, Topic Modeling, Triple Validation}, author = {Fern{\`a}ndez, D{\`e}lia}, editor = {Marqu{\'e}s, F. and Xavier Gir{\'o}-i-Nieto and Bou-Balust, Elisenda} } @article {aGene-Mola23, title = {Looking behind occlusions: A study on amodal segmentation for robust on-tree apple fruit size estimation}, journal = {Computers and Electronics in Agriculture}, volume = {209}, year = {2023}, month = {04/2023}, abstract = {

The detection and sizing of fruits with computer vision methods is of interest because it provides relevant information to improve the management of orchard farming. However, the presence of partially occluded fruits limits the performance of existing methods, making reliable fruit sizing a challenging task. While previous fruit segmentation works limit segmentation to the visible region of fruits (known as modal segmentation), in this work we propose an amodal segmentation algorithm to predict the complete shape, which includes its visible and occluded regions. To do so, an end-to-end convolutional neural network (CNN) for simultaneous modal and amodal instance segmentation was implemented. The predicted amodal masks were used to estimate the fruit diameters in pixels. Modal masks were used to identify the visible region and measure the distance between the apples and the camera using the depth image. Finally, the fruit diameters in millimetres (mm) were computed by applying the pinhole camera model. The method was developed with a Fuji apple dataset consisting of 3925 RGB-D images acquired at different growth stages with a total of 15,335 annotated apples, and was subsequently tested in a case study to measure the diameter of Elstar apples at different growth stages. Fruit detection results showed an F1-score of 0.86 and the fruit diameter results reported a mean absolute error (MAE) of 4.5\ mm and R2\ =\ 0.80 irrespective of fruit visibility. Besides the diameter estimation, modal and amodal masks were used to automatically determine the percentage of visibility of measured apples. This feature was used as a confidence value, improving the diameter estimation to MAE\ =\ 2.93\ mm and R2\ =\ 0.91 when limiting the size estimation to fruits detected with a visibility higher than 60\%. The main advantages of the present methodology are its robustness for measuring partially occluded fruits and the capability to determine the visibility percentage. The main limitation is that depth images were generated by means of photogrammetry methods, which limits the efficiency of data acquisition. To overcome this limitation, future works should consider the use of commercial RGB-D sensors. The code and the dataset used to evaluate the method have been made publicly available at\ https://github.com/GRAP-UdL-AT/Amodal_Fruit_Sizing.

}, keywords = {deep learning, Fruit detection, Fruit measurement, Fruit visibility, Precision agriculture, Yield estimation}, issn = {ISSN 0168-1699}, doi = {https://doi.org/10.1016/j.compag.2023.107854}, url = {https://authors.elsevier.com/sd/article/S0168-1699(23)00242-9}, author = {Gen{\'e}-Mola, Jordi and Ferrer-Ferrer, M. and Gregorio, Eduard and Blok, P. M. and Hemming, J. and Morros, J.R. and Rosell-Polo, Joan R. and Ver{\'o}nica Vilaplana and Ruiz-Hidalgo, J.} } @article {aDominguez, title = {Neural ADMIXTURE: rapid population clustering with autoencoders}, journal = {Nature Computational Science}, year = {2023}, month = {07/2023}, abstract = {

Characterizing the genetic substructure of large cohorts has become increasingly important as genetic association and prediction studies are extended to massive, increasingly diverse, biobanks. ADMIXTURE and STRUCTURE are widely used unsupervised clustering algorithms for characterizing such ancestral genetic structure. These methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA marker frequencies. The assignments, and clusters, provide an interpretable representation for geneticists to describe population substructure at the sample level. However, with the rapidly increasing size of population biobanks and the growing numbers of variants genotyped (or sequenced) per sample, such traditional methods become computationally intractable. Furthermore, multiple runs with different hyperparameters are required to properly depict the population clustering using these traditional methods, increasing the computational burden. This can lead to days of compute. In this work we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as ADMIXTURE, providing similar (or better) clustering, while reducing the compute time by orders of magnitude. Indeed, the equivalent of one month of continuous compute can be reduced to hours. In addition, Neural ADMIXTURE can include multiple outputs, providing the equivalent results as running the original ADMIXTURE algorithm many times with different numbers of clusters. Our models can also be stored, allowing later cluster assignment to be performed with a linear computational time.

}, author = {Dominguez, Albert and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cMorros, title = {Real-time lane classification and accident detection for safer micromobility}, booktitle = {11th Internationa Congress on Transportation Research}, year = {2023}, month = {09/2023}, address = {Heraklion, Crete}, abstract = {

The lack of knowledge of the micromobility regulations by e-scooter users is an important factor behind some of\ the accidents involving these vehicles. We present two modules that can increase the safety of the users and\ pedestrians: First, a computer vision model that analyses the video feed captured with a smartphone attached to\ the e-scooter, and predicts in real-time the type of lane in which the user is riding. This knowledge is used by an\ application which combines this information with GSNN location information and a database of mobility\ regulations, and informs the user when he/she is not complying with these regulations. Second, an accident\ detection system, using the smartphone accelerometer, that detects if there is a fall during the riding, so that the\ app can contact the authorities to determine the appropriate response. The experimental results show excellent\ results for both modules.

}, keywords = {accident detection, computer Vision, deep learning, lane classification, Micromobility, RideSafeUM}, author = {Morros, J.R. and Broquetas, A. and Mateo, A. and Puig, J. and Davins, M.} } @article {aFerrer-Ferrer, title = {Simultaneous Fruit Detection and Size Estimation Using Multitask Deep Neural Networks }, journal = {Biosystems Engineering}, volume = {233}, year = {2023}, month = {09/2023}, pages = {63-75}, abstract = {

The measurement of fruit size is of great interest to estimate the yield and predict the harvest resources in advance. This work proposes a novel technique for in-field apple detection and measurement based on Deep Neural Networks. The proposed\ \ framework was trained with RGB-D data and consists of an end-to-end multitask Deep Neural Network architecture 13 specifically designed to perform the following tasks: 1) detection and segmentation of each fruit from its surroundings; 2) estimation of the diameter of each detected fruit. The methodology was tested with a total of 15335 annotated apples at different growth stages, with diameters varying from 27 mm to 95 mm. Fruit detection results reported an F1-score for apple detection of 0.88 and a mean absolute error of diameter estimation of 5.64 mm. These are state-of-the-art results with the additional advantages of: a) using an end-to-end multitask trainable network; b) an efficient and fast inference speed; and c) being based on RGB-D data which can be acquired with affordable depth cameras. On the contrary, the main disadvantage is the need of annotating a large amount of data with fruit masks and diameter ground truth to train the model. Finally, a fruit visibility analysis showed an improvement in the prediction when limiting the measurement to apples above 65\% of visibility (mean absolute error of 5.09 mm). This suggests that future works should develop a method for automatically identifying the most visible apples and discard the prediction of highly occluded fruits.

}, keywords = {deep learning, Fruit measurement, Fruit visibility, Precision agriculture, Yield estimation}, doi = {https://doi.org/10.1016/j.biosystemseng.2023.07.010}, author = {Ferrer-Ferrer, M. and Ruiz-Hidalgo, J. and Gregorio, Eduard and Ver{\'o}nica Vilaplana and Morros, J.R. and Gen{\'e}-Mola, Jordi} } @conference {cCaselles, title = {SIRA: Relightable Avatars from a Single Image}, booktitle = {Winter Conference on Applications of Computer Vision (WACV)}, year = {2023}, abstract = {

Recovering the geometry of a human head from a single image, while factorizing the materials and illumination is a severely ill-posed problem that requires prior information to be solved. Methods based on 3D Morphable Models (3DMM), and their combination with differentiable renderers, have shown promising results. However, the expressiveness of 3DMMs is limited, and they typically yield over-smoothed and identity-agnostic 3D shapes limited to the face region. Highly accurate full head reconstructions have recently been obtained with neural fields that parameterize the geometry using multilayer perceptrons. The versatility of these representations has also proved effective for disentangling geometry, materials and lighting. However, these methods require several tens of input images. In this paper, we introduce SIRA, a method which, from a single image, reconstructs human head avatars with high fidelity geometry and factorized lights and surface materials. Our key ingredients are two data-driven statistical models based on neural fields that resolve the ambiguities of single-view 3D surface reconstruction and appearance factorization. Experiments show that SIRA obtains state of the art results in 3D head reconstruction while at the same time it successfully disentangles the global illumination, and the diffuse and specular albedos. Furthermore, our reconstructions are amenable to physically-based appearance editing and head model relighting.

}, author = {Caselles, Pol and Ramon, Eduard and Garcia, Jaime and Xavier Gir{\'o}-i-Nieto and Moreno, Francesc and Triginer, Gil} } @article {aPardas23, title = {Stromal tissue segmentation in Ki67 histology images based on cytokeratin-19 stain translation}, journal = {JOURNAL OF MEDICAL IMAGING}, volume = {10}, year = {2023}, month = {06/2023}, abstract = {

Purpose

The diagnosis and prognosis of breast cancer relies on histopathology image analysis. In this context, proliferation markers, especially Ki67, are increasingly important. The diagnosis using these markers is based on the quantification of proliferation, which implies the counting of Ki67 positive and negative tumoral cells in epithelial regions, thus excluding stromal cells. However, stromal cells are often very difficult to distinguish from negative tumoral cells in Ki67 images and often lead to errors when automatic analysis is used.

Approach

We study the use of automatic semantic segmentation based on convolutional neural networks (CNNs) to separate stromal and epithelial areas on Ki67 stained images. CNNs need to be accurately trained with extensive databases with associated ground truth. As such databases are not publicly available, we propose a method to produce them with minimal manual labelling effort. Inspired by the procedure used by pathologists, we have produced the database relying on knowledge transfer from cytokeratin-19 images to Ki67 using an image-to-image (I2I) translation network.

Results

The automatically produced stroma masks are manually corrected and used to train a CNN that predicts very accurate stroma masks for unseen Ki67 images. An F-score value of 0.87 is achieved. Examples of effect on the KI67 score show the importance of the stroma segmentation.

Conclusions

An I2I translation method has proved very useful for building ground-truth labeling in a task where manual labeling is unfeasible. With reduced correction effort, a dataset can be built to train neural networks for the difficult problem of separating epithelial regions from stroma in stained images where separation is very hard without additional information.

}, isbn = {2329-4302}, doi = {https://doi.org/10.1117/1.JMI.10.3.037502}, author = {M. Pard{\`a}s and D. Anglada and M. Espina and F. Marques and Salembier, P.} } @conference {cGene-Mola23, title = {Video-Based Fruit Detection and Tracking for Apple Counting and Mapping}, booktitle = {IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)}, year = {2023}, month = {11/2023}, doi = {10.1109/MetroAgriFor58484.2023.10424135}, author = {Gen{\'e}-Mola, Jordi and Felip-Pom{\'e}s, M. and Net-Barn{\'e}s, F. and Morros, J.R. and Miranda, J. C. and Arn{\'o} Satorra, J. and As{\'\i}n Jones, L. and Lordan Sanahuja, J. and Ruiz-Hidalgo, J. and Gregorio, Eduard} } @article {xPerera22, title = {Ancestry-conditioned Generative Models for Genotyping}, year = {2022}, abstract = {

Local ancestry inference (LAI) identifies the ancestry of each segment of an individual{\textquoteright}s genome and it is a critical step in the analysis of human genomes with applications from pharmacogenomics and personalized medicine to increase detection of genetic associations.\ 

New LAI techniques are appearing at a fast pace in both industry and academic research and large data-sets of human genomic sequences from the ancestries of interest are required to train those methods. Usually, those data-sets are protected by privacy regulations, are proprietary or accessible only when they come with restrictions due to its nature. An interesting way to overcome those difficulties is through the generation of data samples that could be similar enough to real sequences from ancestries of interest. A generalized model can be openly shared because there is no real individual information in there.\ 

Thus, we present a class-conditional Generative adversarial Model and a Conditional Generative Moment-Matching Network intended to generate new realistic genotypes of a desired ancestry. In addition, we present a privacy mechanism that extracts features from the real data to generate new realistic genotypes by using features.

}, author = {Perera, Maria}, editor = {Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cCombaliae, title = {Artificial intelligence to predict positivity of sentinel lymph node biopsy in melanoma patients}, booktitle = {European Association of Dermato Oncology (EADO 2022)}, year = {2022}, month = {04/2022}, author = {Marc Combalia and Sebastian Podlipnik and Carlos Hernandez and Sergio Garc{\'\i}a and Joan Ficapal and Julio Burgos and Ver{\'o}nica Vilaplana and Josep Malvehy} } @conference {cCumplido-Mayoral22, title = {Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer{\textquoteright}s disease and neurodegeneration stratified by sex}, booktitle = {15th Clinical Trials on Alzheimer{\textquoteright}s Disease Conference (CTAD)}, year = {2022}, month = {11/2022}, address = {San Francisco, USA}, author = {Irene Cumplido-Mayoral and Marta Mila-Aloma and Luigi Lorenzini and Alle Meije Wink and H. Mutsaerts and Sven Haller and Ga{\"e}l Chetelat and Frederik Barkhof and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cCumplido-Mayoral, title = {Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer{\textquoteright}s Disease and Neurodegeneration}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2022}, month = {07/2022}, author = {Irene Cumplido-Mayoral and Marina Garc{\'\i}a-Prat and Greg Operto and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Marta Mila-Aloma and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cHernandez22a, title = {Contrastive and attention-based multiple instance learning for the prediction of sentinel lymph node status from histopathologies of primary melanoma tumours.}, booktitle = {Cancer Prevention through early detecTion (Caption) Workshop at 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022)}, year = {2022}, month = {09/2022}, abstract = {

Sentinel lymph node status is a crucial prognosis factor for melanomas; nonetheless, the invasive surgery required to obtain it always puts the patient at risk. In this study, we develop a Deep Learning-based approach to predict lymph node metastasis from Whole Slide Images of primary tumours. Albeit very informative, these images come with complexities that hamper their use in machine learning applications, namely their large size and limited datasets. We propose a pre-training strategy based on self-supervised contrastive learning to extract better image feature representations and an attention-based Multiple Instance Learning approach to enhance the model{\textquoteright}s performance. With this work, we quantitatively demonstrate that combining both methods improves various classification metrics and qualitatively show that contrastive learning encourages the network to output higher attention scores to tumour tissue and lower scores to image artifacts.

}, author = {Carlos Hernandez and Marc Combalia and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana} } @article {aTemprana-Salvador22, title = {DigiPatICS: Digital Pathology Transformation of the Catalan Health Institute Network of 8 Hospitals - Planification, Implementation and Preliminary Results}, journal = {Diagnostics}, volume = {12}, year = {2022}, month = {03/2022}, chapter = {852}, abstract = {

Complete digital pathology transformation for primary histopathological diagnosis is a challenging yet rewarding endeavor. Its advantages are clear with more efficient workflows, but there are many technical and functional difficulties to be faced. The Catalan Health Institute (ICS) has started its DigiPatICS project, aiming to deploy digital pathology in an integrative, holistic, and comprehensive way within a network of 8 hospitals, over 168 pathologists, and over 1 million slides each year. We describe the bidding process and the careful planning that was required, followed by swift implementation in stages. The purpose of the DigiPatICS project is to increase patient safety and quality of care, improving diagnosis and the efficiency of processes in the pathological anatomy departments of the ICS through process improvement, digital pathology, and artificial intelligence tools.

}, keywords = {artificial intelligence, computational pathology, deep learning, digital pathology, implementation, LIS, primary diagnosis, telepathology, workflow}, doi = {10.3390/diagnostics12040852}, url = {https://www.mdpi.com/2075-4418/12/4/852}, author = {Jordi Temprana-Salvador and Pau L{\'o}pez-Garc{\'\i}a and Josep Castellv{\'\i} Vives and Llu{\'\i}s de Haro and Eudald Ballesta and Matias Rojas Abusleme and Miquel Arrufat and Ferran Marques and Casas, J. and Carlos Gallego and Laura Pons and Jos{\'e} Luis Mate and Pedro Luis Fern{\'a}ndez and Eugeni L{\'o}pez-Bonet and Ramon Bosch and Salom{\'e} Mart{\'\i}nez and Santiago Ram{\'o}n y Cajal and Xavier Matias-Guiu} } @mastersthesis {xMohamed22, title = {Exploring Visual Representations for Sign Language Translation}, year = {2022}, abstract = {

Sign Language Translation (SLT) task has been addressed in multiple approaches in recent years. In this work we aim to investigate the impact of using different types of visual sign language representation for SLT. For this investigation we use the state-of-the-art in SLT, the Sign Language Transformers model. We compare the translation output performance of two types of body pose estimation models as our skeleton extractor,\  and 2D CNN features trained on the test dataset. These later perform best, and I3D features outperform the pose estimation-based ones.\ 

}, author = {Maram A. Mohamed}, editor = {Laia Tarr{\'e}s and Xavier Gir{\'o}-i-Nieto} } @phdthesis {dRamon22, title = {Few-shot 3D Reconstruction of Body Parts with Deep Neural Networks}, volume = {Excellent}, year = {2022}, month = {09/2022}, type = {Industrial}, abstract = {

In this thesis, we study the problem of reconstructing objects from a concrete category\ in 3D when few images are available as input, i.e. less than 10. We apply our findings to digitalizing human body parts such as heads and torsos for medical applications. The first part of the thesis explores systems that rely on 3D Morphable Models. When approaching a concrete task, training such systems requires expensive manual hyper-parameter tuning of both the architecture and the loss, which is time consuming. We focus on designing novel losses without hyperparameters and modular architectures that allow to train models without tuning efforts. We also aim at providing a fine alignment between the 3D space and the image space by estimating camera poses with a low re-projection error that further improve the texturing process in 3D modelling applications or the rendering process in augmented reality applications. Our findings lead to systems that are very stable and that naturally scale to different scenes.

While 3D Morphable Models are fast and robust, they are still very limited in terms\ of accuracy and expressiveness, which might be prohibitive for applications that require high fidelity. A promising alternative to 3D Morphable Models are implicit functions,which in combination with differentiable rendering techniques have shown impressive results at reconstructing 3D surfaces. However, the later require large sets of images at test time to obtain satisfactory results. In the second part of the thesis, we propose to use a probabilistic model that represents a distribution of implicit surfaces in combination with a differentiable renderer to reduce the number of images required at test time. The resulting 3D reconstruction system is highly accurate and allows to reconstruct a wide variety of human head shapes when only 3 images are available.

}, author = {Ramon, Eduard}, editor = {Moreno, Francesc and Garcia, Jaime and Xavier Gir{\'o}-i-Nieto} } @conference {cMas-Montserrat, title = {Generative Moment Matching Networks for Genotype Simulation}, booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}22)}, year = {2022}, abstract = {

The generation of synthetic genomic sequences using neural networks has potential to overcome privacy and data sharing restrictions and to mitigate potential bias within datasets due to under-representation of some population groups. However, there is not a consensus on which architectures, training procedures, and evaluation metrics should be used when simulating single nucleotide polymorphism (SNP) sequences with neural networks. In this paper, we explore the use of Generative Moment Matching Networks (GMMNs) for SNP simulation, we present some architectural and procedural changes to properly train the networks, and we introduce an evaluation scheme to qualitatively and quantitatively asses the quality of the simulated sequences.

}, author = {Mas-Montserrat, Daniel and Perera, Maria and Barrab{\'e}s, M{\'\i}riam and Geleta, Margarita and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @mastersthesis {xDomenech, title = {Hiding Images in their Spoken Narratives}, year = {2022}, abstract = {

Steganography is the technique of hiding secret data within an ordinary, non-secret, file or message in order to avoid its detection. Throughout our work, we study the case where the hidden secret data is an image and the non-secret data or cover signal is an audio. To this end, we use a recently proposed residual architecture operating on top of short-time discrete cosine transform (STDCT) audio spectrograms. In our work, we evaluate the above mentioned residual steganography architecture with the Localized Narratives dataset, explore the feasibility of using short-time fourier transform (STFT) audio spectrograms instead of STDCTs to improve the efficiency of the system, investigate the use of hidden signals permuted with the objective to spread the audio corruption of the revealed images, apply averaged audio windows to improve quality results and tested the system in real-world distortions.

}, author = {Teresa Domenech}, editor = {McGuinness, Kevin and Pons, Jordi and Xavier Gir{\'o}-i-Nieto} } @article {xBarrabes22, title = {Machine Learning for Genomic Sequence Processing}, year = {2022}, abstract = {

Introduction to Research, BSc Data Science and Engineering, Autumn 2021:

Predicting Dog Phenotypes from Genotypes

In this paper, we analyze dog genotypes {\textendash} positions of DNA sequences that often vary between different dogs {\textendash}in order to predict the corresponding phenotypes {\textendash} unique characteristics that result from different genetic code. More specifically, given chromosome data from a dog, we aim to predict its breed category, height, and weight. We explore a variety of linear and non-linear classification and regression techniques to accomplish these three tasks. We also investigate the use of a neural network (both in linear and non-linear modes) for breed classification and compare its performance to traditional statistical methods. We show that linear methods generally outperform or match the performance of non-linear methods for breed classification. However, the reverse case is true for height and weight regression. We also evaluate the results of all of these methods based on the number of input features used in the analy sis and demonstrate that phenotypes can be predicted with as few as 0.5\% of the input features, and dog breeds can be classified with 50\% balanced accuracy with as few as 0.02\% of the full genomic sequences for our analysis.

MergeGenome. A Python-based Toolkit for Merging VCF files

A challenge of genomic studies is the lack of easy to access and properly formatted datasets. When having access to more than one dataset, it seems desirable to combine them. There is a lack of tools to duly merge genomic datasets without losing all non-matching features. To fill this gap, we present the MergeGenome toolkit, designed to integrate DNA sequences from two files in variant call format (VCF) while targeting data quality. MergeGenome is a robust pipeline of comprehensive steps to standardize nomenclature, remove ambiguities, correct flips, eliminate mismatches, select important features, and filter likely erroneous features (the latter with machine learning). MergeGenome is Python-based and relies on pre-existing software for manipulation and imputation of VCF data. We evaluate the result of merging two datasets with dog DNA sequences of dissimilar lengths and notice that genotype imputation with Beagle v5.1 usually fails for low-frequency alleles. Alternatively, we explore several multi-label machine learning classifiers. Although K-Nearest Neighbors achieves competitive results, none of the methods tried outperforms Beagle v5.1.

}, author = {Barrab{\'e}s, M{\'\i}riam}, editor = {Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {xBudria22, title = {Multimodal 3D Hand Pose Enhancement for Sign Language}, year = {2022}, abstract = {

The application of recent deep learning breakthroughs to the domain of sign language has yielded very promising results. However, sign language processing systems depend on large amounts of labeled high-quality data to work properly. Current hand pose estimation methods are often unreliable and do not always produce estimations with enough quality. To mitigate this issue, we explore the applicability of the novel Body2Hands method for the obtainment of high-quality hand pose estimations.

}, author = {{\'A}lvaro Budria}, editor = {Laia Tarr{\'e}s and Moreno, Francesc and Xavier Gir{\'o}-i-Nieto} } @conference {cPinazo22, title = {Perception in the era of Autonomous Vehicles}, booktitle = {Photonics 4 Smart Cities, SCEWC 2022}, year = {2022}, month = {11/2022}, publisher = {Photonics21}, organization = {Photonics21}, address = {Barcelona}, url = {https://www.fotonica21.org/photonics-4-smart-cities}, author = {Jorge Pinazo and Adolfo Ler{\'\i}n and Francesc Xavier de Gibert and {\'A}lvaro Moliner and Daniel Sevilla and Antonio Jurado and Iv{\'a}n R{\'\i}os and Rodrigo Jerez and Jaime Santiago and {\'A}lvaro Linuesa and Antonio Cano and Federico Dios and Adolfo Comer{\'o}n and Casas, J. and Jos{\'e} Antonio L{\'a}zaro} } @conference {cGeleta21, title = {PixInWav: Residual Steganography for Hiding Pixels in Audio}, booktitle = {ICASSP}, year = {2022}, month = {06/2021}, abstract = {

Steganography comprises the mechanics of hiding data in a host media that may be publicly available. While previous works focused on unimodal setups (e.g., hiding images in images, or hiding audio in audio), PixInWav targets the multimodal case of hiding images in audio. To this end, we propose a novel residual architecture operating on top of short-time discrete cosine transform (STDCT) audio spectrograms. Among our results, we find that the residual audio steganography setup we propose allows independent encoding of the hidden image from the host audio without compromising quality. Accordingly, while previous works require both host and hidden signals to hide a signal, PixInWav can encode images offline --- which can be later hidden, in a residual fashion, into any audio signal. Finally, we test our scheme in a lab setting to transmit images over airwaves from a loudspeaker to a microphone verifying our theoretical insights and obtaining promising results.

\ Deep Learning Barcelona Symposium 2022

Presentation from the early stages of the project (January 2021):

}, author = {Geleta, Margarita and Punt{\'\i}, Cristina and McGuinness, Kevin and Pons, Jordi and Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @conference {cBartusiak, title = {Predicting Dog Phenotypes from Genotypes}, booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}22)}, year = {2022}, abstract = {

In this paper, we analyze dog genotypes - positions of DNA sequences that often vary between different dogs - in order to predict the corresponding phenotypes - unique characteristics that result from different genetic code. More specifically, given chromosome data from a dog, we aim to predict its breed category, height, and weight. We explore a variety of linear and non-linear classification and regression techniques to accomplish these three tasks. We show that linear methods generally outperform or match non-linear methods for breed classification. However, the reverse case is true for height and weight regression. We also evaluate the performance of all of these methods based on the number of input features used in the analysis. We conduct experiments using different fractions of the full genomic sequences and demonstrate that phenotypes can be predicted with as few as 0.5\% of the input features available for our analysis, and dog breeds can be classified with 50\% balanced accuracy with as few as 0.02\% of the features.

}, doi = {https://doi.org/10.1101/2022.04.13.488108 }, author = {Bartusiak, Emily and Barrab{\'e}s, M{\'\i}riam and Aigerim Rymbe and J{\'u}lia Gimbernat and Cayetana L{\'o}pez and Lorenzo Barberis and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {aa, title = {QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation--Analysis of Ranking Metrics and Benchmarking Results}, journal = {Journal of Machine Learning for Biomedical Imaging}, year = {2022}, month = {08/2022}, abstract = {

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at:\ this https URL.

}, url = {https://www.melba-journal.org/papers/2022:026.html}, author = {Raghav Metha and Angelos Filos and Ujjwal Baid and Laura Mora and Ver{\'o}nica Vilaplana and Christos Davatzikos and Bjoern Menze and Spyridon Bakas and Yarin Gal and Tar Arbel} } @article {aBernal22, title = {SALAI-Net: species-agnostic local ancestry inference network}, journal = {Bioinformatics}, volume = {38}, year = {2022}, month = {09/2022}, chapter = {ii27}, abstract = {

Motivation
Local ancestry inference (LAI) is the high resolution prediction of ancestry labels along a DNA sequence. LAI is important in the study of human history and migrations, and it is beginning to play a role in precision medicine applications including ancestry-adjusted genome-wide association studies (GWASs) and polygenic risk scores (PRSs). Existing LAI models do not generalize well between species, chromosomes or even ancestry groups, requiring re-training for each different setting. Furthermore, such methods can lack interpretability, which is an important element in each of these applications.

Results
We present SALAI-Net, a portable statistical LAI method that can be applied on any set of species and ancestries (species-agnostic), requiring only haplotype data and no other biological parameters. Inspired by identity by descent methods, SALAI-Net estimates population labels for each segment of DNA by performing a reference matching approach, which leads to an interpretable and fast technique. We benchmark our models on whole-genome data of humans and we test these models{\textquoteright} ability to generalize to dog breeds when trained on human data. SALAI-Net outperforms previous methods in terms of balanced accuracy, while generalizing between different settings, species and datasets. Moreover, it is up to two orders of magnitude faster and uses considerably less RAM memory than competing methods.

}, doi = {https://doi.org/10.1093/bioinformatics/btac464}, url = {https://academic.oup.com/bioinformatics/article/38/Supplement_2/ii27/6701999?login=false$\#$373158629}, author = {Bernal, Oriol and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {aSalgueirob, title = {SEG-ESRGAN: A multi-task network for super-resolution and semantic segmentation of remote sensing images}, journal = {Remote Sensing}, volume = {14}, year = {2022}, month = {2022}, chapter = {5862}, abstract = {

The production of highly accurate land cover maps is one of the primary challenges in remote sensing, which depends on the spatial resolution of the input images. Sometimes, high-resolution imagery is not available or is too expensive to cover large areas or to perform multitemporal analysis. In this context, we propose a multi-task network to take advantage of the freely available Sentinel-2 imagery to produce a super-resolution image, with a scaling factor of 5, and the corresponding high-resolution land cover map. Our proposal, named SEG-ESRGAN, consists of two branches: the super-resolution branch, that produces Sentinel-2 multispectral images at 2 m resolution, and an encoder{\textendash}decoder architecture for the semantic segmentation branch, that generates the enhanced land cover map. From the super-resolution branch, several skip connections are retrieved and concatenated with features from the different stages of the encoder part of the segmentation branch, promoting the flow of meaningful information to boost the accuracy in the segmentation task. Our model is trained with a multi-loss approach using a novel dataset to train and test the super-resolution stage, which is developed from Sentinel-2 and WorldView-2 image pairs. In addition, we generated a dataset with ground-truth labels for the segmentation task. To assess the super-resolution improvement, the PSNR, SSIM, ERGAS, and SAM metrics were considered, while to measure the classification performance, we used the IoU, confusion matrix and the F1-score. Experimental results demonstrate that the SEG-ESRGAN model outperforms different full segmentation and dual network models (U-Net, DeepLabV3+, HRNet and Dual_DeepLab), allowing the generation of high-resolution land cover maps in challenging scenarios using Sentinel-2 10 m bands.

}, issn = {2072-4292 }, doi = {https://doi.org/10.3390/rs142}, url = {https://doi.org/10.3390/rs14225862 }, author = {Luis Salgueiro and Javier Marcello and Ver{\'o}nica Vilaplana} } @conference {cHernandeza, title = {Sentinel lymph node status prediction using self-attention networks and contrastive learning from routine histology images of primary tumours}, booktitle = {Medical Imaging with Deep Learning MIDL 2022}, year = {2022}, month = {07/2022}, abstract = {

Deep learning-based computational pathology approaches are becoming increasingly prominent in histopathology image analysis. However, these images typically come with drawbacks that hamper automatic analysis, which include: labeled sample scarcity or the extremely large size of the images (ranging from\ \ to\ \ pixels). Nonetheless, they have proven to be a powerful tool for diagnosis and risk prevention. One such prevention is reducing the number of patients who undergo surgeries that do not benefit them. This study develops a pipeline for predicting sentinel lymph node (SLN) metastasis non-invasively from digitised Whole Slide Images (WSI) of primary melanoma tumours. Furthermore, we combine the use of a weakly supervised architecture with self-supervised contrastive pre-training. We experimentally demonstrate that 1) the use of self-attention improves sentinel lymph node status prediction and 2) self-supervised contrastive learning improves the quality of the learned representations compared to a standard ImageNet pre-training, which boosts the model{\textquoteright}s performance.

}, author = {Carlos Hernandez and Marc Combalia and Josep Malvehy and Ver{\'o}nica Vilaplana} } @conference {cHernandez22, title = {Sentinel lymph node status prediction with self-attention neural networks using histologies of primary melanoma tumours}, booktitle = {European Association of Dermato Oncology (EADO 2022)}, year = {2022}, month = {04/2022}, author = {Carlos Hernandez and Ver{\'o}nica Vilaplana and Marc Combalia and Sergio Garc{\'\i}a and Sebastian Podlipnik and Julio Burgos and Susana Puig and Josep Malvehy} } @conference {cMosella-Montoro22, title = {SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters}, booktitle = {IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR)}, year = {2022}, month = {06/2022}, address = {New Orleans, USA}, abstract = {

This work presents SkinningNet, an end-to-end Two-Stream Graph Neural Network architecture that computes skinning weights from an input mesh and its associated skeleton, without making any assumptions on shape class and structure of the provided mesh. Whereas previous methods pre-compute handcrafted features that relate the mesh and the skeleton or assume a fixed topology of the skeleton, the proposed method extracts this information in an end-to-end learnable fashion by jointly learning the best relationship between mesh vertices and skeleton joints. The proposed method exploits the benefits of the novel Multi-Aggregator Graph Convolution that combines the results of different aggregators during the summarizing step of the Message-Passing scheme, helping the operation to generalize for unseen topologies. Experimental results demonstrate the effectiveness of the contributions of our novel architecture, with SkinningNet outperforming current state-of-the-art alternatives.

https://imatge-upc.github.io/skinningnet/

}, url = {https://imatge-upc.github.io/skinningnet/}, author = {Mosella-Montoro, Albert and Ruiz-Hidalgo, J.} } @phdthesis {dSalgueiro22, title = {Super-resolution and semantic segmentation of remote sensing images using deep learning techniques}, volume = {PhD}, year = {2022}, month = {10/2022}, abstract = {

Remote sensing for Earth observation is a growing scientific field essential for many human activities. Among the different applications in the Remote Sensing domain, the production of thematic maps, such as Land Cover and Land Use, are among the most relevant, as this information plays a critical role in management, planning and monitoring activities at different levels. In this context, the Sentinel-2 satellites are of great importance since they provide open data on land and coastal areas at different spatial resolutions (10, 20, and 60 m), democratizing usability, and creating a high potential for the generation of valuable information, useful in many scenarios, such as agriculture, forestry, land cover and urban planning, among others.

In this thesis, we aim to exploit the usability of Sentinel-2 data by applying deep learning techniques, which are revolutionizing the world of computer vision and, recently, remote sensing. First, we propose super-resolution models to improve the spatial details of the different Sentinel-2 bands, and second, we propose the conjunction of semantic segmentation with super-resolution to generate improved land cover maps that benefit from the enhanced spatial details of the bands.

We first address super-resolution by proposing two different models, one for the 10 m/pixel bands to reach 2 m/pixel and another for the 20 and 60 m/pixel bands to achieve 10 m/pixel. Then, we propose two different multitasking models to derive land cover maps. The first one extending a semantic segmentation model to produce an additional super-resolution image and the second, improving our first super-resolution approach, to provide a semantic segmentation map, as well. We combine features of the different tasks within a single model to improve performance and to generate a high-resolution image with the corresponding highquality land cover map. All models developed were evaluated, quantitatively and qualitatively, using different datasets, showing excellent performance in diverse complex scenarios.

}, author = {Luis Salgueiro}, editor = {Ver{\'o}nica Vilaplana and Javier Marcello} } @conference {cBudria, title = {Topic Detection in Continuous Sign Language Videos}, booktitle = {Accessibility, Vision, and Autonomy Meet (AVA) CVPR Workshop}, year = {2022}, month = {06/2022}, abstract = {

Significant progress has been made recently on challenging tasks in automatic sign language understanding, such as sign language recognition, translation and production. However, these works have focused on datasets with relatively few samples, short recordings and limited vocabulary and signing space.\ In this work, we introduce the novel task of sign language topic detection. We base our experiments on How2Sign, a large-scale video dataset spanning multiple semantic domains. We provide strong baselines for the task of topic detection,\ \ and present a comparison between different visual features commonly used in the domain of sign language.

}, author = {{\'A}lvaro Budria and Laia Tarr{\'e}s and Gerard I. G{\'a}llego and Moreno, Francesc and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @article {aMosella-Montoro21, title = {2D{\textendash}3D Geometric Fusion network using Multi-Neighbourhood Graph Convolution for RGB-D indoor scene classification}, journal = {Information Fusion}, volume = {76}, year = {2021}, month = {12/2021}, chapter = {46-54}, abstract = {

Multi-modal fusion has been proved to help enhance the performance of scene classification tasks. This paper presents a 2D-3D Fusion stage that combines 3D Geometric Features with 2D Texture Features obtained by 2D Convolutional Neural Networks. To get a robust 3D Geometric embedding, a network that uses two novel layers is proposed. The first layer, Multi-Neighbourhood Graph Convolution, aims to learn a more robust geometric descriptor of the scene combining two different neighbourhoods: one in the Euclidean space and the other in the Feature space. The second proposed layer, Nearest Voxel Pooling, improves the performance of the well-known Voxel Pooling. Experimental results, using NYU-Depth-V2 and SUN RGB-D datasets, show that the proposed method outperforms the current state-of-the-art in RGB-D indoor scene classification task.\ 

https://imatge-upc.github.io/munegc/

}, doi = {10.1016/j.inffus.2021.05.002}, url = {https://imatge-upc.github.io/munegc/}, author = {Mosella-Montoro, Albert and Ruiz-Hidalgo, J.} } @conference {cMayoral21a, title = {Brain structural alterations in cognitively unimpaired individuals with discordant amyloid-β PET and CSF Aβ42 status: findings using Machine Learning}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Mahnaz Shekari and Gemma Salvad{\'o} and Greg Operto and Raffaele Cacciaglia and Carles Falcon and Aida Ni{\~n}erola Baiz{\'a}n and Andr{\'e}s Perissinotti and Carolina Minguillon and Karine Fauria and Ivonne Suridjan and Gwendlyn Kollmorgen and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cGeleta, title = {Deep variational autoencoders for population genetics: applications in classification, imputation, dimensionality reduction, and novel lossless data compression}, booktitle = {American Society of Human Genetics (ASHG)}, year = {2021}, month = {10/2021}, publisher = {ASHG}, organization = {ASHG}, address = {Virtual}, abstract = {

In this study we show the power of variational autoencoders (VAEs) for a variety of tasks relating to the interpretation and compression of genomic data. The unsupervised setting allows for detecting and learning of granular population structure and inferring of new informative latent factors, opening up an avenue for applications in dimensionality reduction, data simulation, population classification, imputation, and lossless genomic data compression. The latent spaces of VAEs are able to capture and represent clearly differentiated Gaussian-like clusters of similar genetic composition on a fine-scale with a relatively small number of SNPs as input. Furthermore, sequences can be decomposed into latent representations and reconstruction errors (residuals) providing a sparse representation that provides a means for efficient lossless compression.

Identifying genetic clusters can be important when performing genome-wide association studies and provides an alternative to self-reported ethnic labels, which are culturally constructed and vary according to the location and individual. A variety of unsupervised dimensionality reduction methods have been explored in the past for such applications, including PCA, MDS, t-SNE, and UMAP. Our proposed VAE can represent the population structure as a Gaussian-distributed continuous multi-dimensional representation and as classification probabilities providing flexible and interpretable population descriptors.

We train our VAE method with several worldwide whole genome datasets from both humans and canids and evaluate the performance of the different proposed applications with networks with and without ancestry conditioning. Our experiments show that different population groups have significantly differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, noting its effect on compression across populations and connect these patterns to historical migrations and ancestral relationships.

Video from the related BSc thesis at UPC Data Science Engineering (2021):

}, author = {Geleta, Margarita and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {aAbadal, title = {A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 imagery}, journal = {Remote Sensing}, volume = {13}, year = {2021}, month = {2021}, pages = {4547}, abstract = {

There is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. Taking into account the free availability of Sentinel-2 satellite data, in this work we propose a deep learning model to generate high-resolution segmentation maps from low-resolution inputs in a multi-task approach. Our proposal is a dual-network model with two branches: the Single Image Super-Resolution branch, that reconstructs a high-resolution version of the input image, and the Semantic Segmentation Super-Resolution branch, that predicts a high-resolution segmentation map with a scaling factor of 2. We performed several experiments to find the best architecture, training and testing on a subset of the S2GLC 2017 dataset. We based our model on the DeepLabV3+ architecture, enhancing the model and achieving an improvement of 5\% on IoU and almost 10\% on the recall score. Furthermore, our qualitative results demonstrate the effectiveness and usefulness of the proposed approach.

}, author = {Sa{\"u}c Abadal and Luis Salgueiro and Javier Marcello and Ver{\'o}nica Vilaplana} } @conference {cTarres, title = {GAN-based Image Colourisation with Feature Reconstruction Loss}, booktitle = {CVPR 2021 Women in Computer Vision Workshop}, year = {2021}, month = {06/2021}, address = {Virtual}, abstract = {

Image colourisation is the task of adding plausible colour to grayscale images. This transformation requires obtaining a three dimensional colour-valued mapping from a real-valued grayscale image, which leads to an undetermined problem because the gray-scale semantics and texture provide cues for multiple possible colour mappings. The goal of image colourisation in not to recover the ground truth colour in a manner that it is perceived as natural by a human observer.\ Our work takes as a baseline a scheme based on an end-to-end trainable convolutional neural network (CNN) trained with a smooth L1 loss to predict the $ab$ channels of a colour image given the $L$ channel. We introduce an extra perceptual reconstruction loss during training to improve the capabilities of a adversarial adversarial model, that we adopt as a baseline.

}, author = {Laia Tarr{\'e}s and G{\'o}rriz, Marc and Xavier Gir{\'o}-i-Nieto and Mrak, Marta} } @mastersthesis {xTarres21, title = {GAN-based Image Colourisation with Feature Reconstruction Loss}, year = {2021}, abstract = {

Automatic image colourisation is a complex and ambiguous task due to having multiple correct solutions. Previous approaches have resulted in desaturated results unless relying on significant user interaction.\ In this thesis we study the state of the art for colourisation and we propose an automatic colourisation approaches based on generative adversarial networks that incorporates a feature reconstruction loss during training. The generative network is framed in an adver- sarial model that learns how to colourise by incorporating a perceptual understanding of the colour. Qualitative and quantitative results show the capacity of the proposed method to colourise images in a realistic way, boosting the colourfulness and perceptual realism of previous GAN-based methodologies.\ We also study and propose a second approach that incorporates segmentation information in the GAN framework and obtain quantitative and qualitative results.

}, author = {Laia Tarr{\'e}s}, editor = {Mrak, Marta and Xavier Gir{\'o}-i-Nieto} } @conference {cRamonb, title = {H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2021}, publisher = {IEEE/CVF}, organization = {IEEE/CVF}, address = {Virtual}, abstract = {

Recent learning approaches that implicitly represent surface geometry using coordinate-based neural representations have shown impressive results in the problem of multi-view 3D reconstruction. The effectiveness of these techniques is, however, subject to the availability of a large number (several tens) of input views of the scene, and computationally demanding optimizations. In this paper, we tackle these limitations for the specific problem of few-shot full 3D head reconstruction, by endowing coordinate-based representations with a probabilistic shape prior that enables faster convergence and better generalization when using few input images (down to three). First, we learn a shape model of 3D heads from thousands of incomplete raw scans using implicit representations. At test time, we jointly overfit two coordinate-based neural networks to the scene, one modeling the geometry and another estimating the surface radiance, using implicit differentiable rendering. We devise a two-stage optimization strategy in which the learned prior is used to initialize and constrain the geometry during an initial optimization phase. Then, the prior is unfrozen and fine-tuned to the scene. By doing this, we achieve high-fidelity head reconstructions, including hair and shoulders, and with a high level of detail that consistently outperforms both state-of-the-art 3D Morphable Models methods in the few-shot scenario, and non-parametric methods when large sets of views are available.

}, author = {Ramon, Eduard and Triginer, Gil and Escur, Janna and Pumarola, Albert and Garcia, Jaime and Xavier Gir{\'o}-i-Nieto and Moreno, Francesc} } @conference {cDuarte20, title = {How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language}, booktitle = {CVPR 2021}, year = {2021}, month = {06/2021}, abstract = {

Sign Language is the primary means of communication for the majority of the Deaf community. One of the factors that has hindered the progress in the areas of automatic sign language recognition, generation, and translation is the absence of large annotated datasets, especially continuous sign language datasets, i.e. datasets that are annotated and segmented at the sentence or utterance level. Towards this end, in this work we introduce How2Sign, a work-in-progress dataset collection. How2Sign consists of a parallel corpus of 80 hours of sign language videos (collected with multi-view RGB and depth sensor data) with corresponding speech transcriptions and gloss annotations. In addition, a three-hour subset was further recorded in a geodesic dome setup using hundreds of cameras and sensors, which enables detailed 3D reconstruction and pose estimation and paves the way for vision systems to understand the 3D geometry of sign language.





}, author = {Amanda Duarte and S. Palaskar and Lucas Ventura and Ghadiyaram, Deepti and DeHaan, Kenneth and F. Metze and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cHernandez, title = {Implementation of personalized medicine in cutaneous melanoma patients aided by artificial intelligence}, booktitle = {10th World Congress of2 Melanoma / 17th EADO Congress}, year = {2021}, month = {04/2021}, author = {Carlos Hernandez and Anil Kiroglu and Sergio Garc{\'\i}a and Joan Ficapal and Julio Burgos and Sebastian Podlipnik and Neus Calbet and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana and Marc Combalia} } @conference {cMayoral21, title = {Machine learning on combined neuroimaging and plasma biomarkers for triaging participants of secondary prevention trials in Alzheimer{\textquoteright}s Disease}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Gemma Salvad{\'o} and Mahnaz Shekari and Carles Falcon and Marta Mil{\`a} Alom{\`a} and Aida Ni{\~n}erola Baiz{\'a}n and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @inbook {bMora, title = {MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures}, booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020}, volume = {12658}, number = {Lecture Notes in Computer Science}, year = {2021}, pages = {376-390}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

Automation of brain tumor segmentation in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural networks (CNNs) have shown improved results in the task. However, high memory consumption is still a problem in 3D-CNNs. Moreover, most methods do not include uncertainty information, which is especially critical in medical diagnosis. This work studies 3D encoder-decoder architectures trained with patch-based techniques to reduce memory consumption and decrease the effect of unbalanced data. The different trained models are then used to create an ensemble that leverages the properties of each model, thus increasing the performance. We also introduce voxel-wise uncertainty information, both epistemic and aleatoric using test-time dropout (TTD) and data-augmentation (TTA) respectively. In addition, a hybrid approach is proposed that helps increase the accuracy of the segmentation. The model and uncertainty estimation measurements proposed in this work have been used in the BraTS{\textquoteright}20 Challenge for task 1 and 3 regarding tumor segmentation and uncertainty estimation.

}, isbn = {978-3-030-72083-4}, issn = {978-3-030-72084-1}, doi = {https://doi.org/10.1007/978-3-030-72084-1_34}, author = {Laura Mora and Ver{\'o}nica Vilaplana} } @conference {cGirbau21, title = {Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation}, booktitle = {CVPR 2021 Robust Video Scene Understanding: Tracking and Video Segmentation (RVSU) Workshop}, year = {2021}, abstract = {

Multiple object tracking faces several challenges that may be alleviated with trajectory information. Knowing the posterior locations of an object helps disambiguating and solving situations such as occlusions, re-identification, and identity switching. In this work, we show that trajectory estimation can become a key factor for tracking, and present TrajE, a trajectory estimator based on recurrent mixture density networks, as a generic module that can be added to existing object trackers. To provide several trajectory hypotheses, our method uses beam search. Also, relying on the same estimated trajectory, we propose to reconstruct a track after an occlusion occurs. We integrate TrajE into two state of the art tracking algorithms, CenterTrack [63] and Tracktor [3]. Their respective performances in the MOTChallenge 2017 test set are boosted 6.3 and 0.3 points in MOTA score, and 1.8 and 3.1 in IDF1, setting a new state of the art for the CenterTrack+TrajE configuration.

}, url = {https://arxiv.org/abs/2106.10950}, author = {Girbau, A. and Xavier Gir{\'o}-i-Nieto and Rius, Ignasi and Marqu{\'e}s, F.} } @conference {cDominguez, title = {Neural ADMIXTURE: rapid population clustering with autoencoders}, booktitle = {2021 Society for Molecular Biology \& Evolution meeting (SMBEv2021)}, year = {2021}, month = {06/2021}, address = {Virtual}, abstract = {

Characterizing the genetic substructure of large cohorts has become increasingly important as genetic association and prediction studies are extended to massive, increasingly diverse, biobanks. ADMIXTURE and STRUCTURE are widely used unsupervised clustering algorithms for characterizing such ancestral genetic structure. These methods decompose individual genomes into fractional cluster assignments with each cluster representing a vector of DNA marker frequencies. The assignments, and clusters, provide an interpretable representation for geneticists to describe population substructure at the sample level. However, with the rapidly increasing size of population biobanks and the growing numbers of variants genotyped (or sequenced) per sample, such traditional methods become computationally intractable. Furthermore, multiple runs with different hyperparameters are required to properly depict the population clustering using these traditional methods, increasing the computational burden. This can lead to days of compute. In this work we present Neural ADMIXTURE, a neural network autoencoder that follows the same modeling assumptions as ADMIXTURE, providing similar (or better) clustering, while reducing the compute time by orders of magnitude. In addition, this network can include multiple outputs, providing the equivalent results as running the original ADMIXTURE algorithm many times with different numbers of clusters. These models can also be stored, allowing later cluster assignment to be performed with a linear computational time.

}, author = {Dominguez, Albert and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @article {xEscobar21, title = {Object Model Adaptation for Multiple Object Tracking}, year = {2021}, abstract = {

Multiple object tracking is a broadly used task in multi- ple applications, all the way from bioengineering to security applications. In this paper we propose a variation of RVOS by adding the center estimation of detected instances, by means of a second head in the decoder which is assigned the task of detecting the corresponding object{\textquoteright}s bounding box arithmetic center. We have trained the model using three variants of the cross-entropy loss, which has been adapted to tackle the class imbalance caused by the fact that the center of an object is represented by only one pixel of the image, and have obtained some promising results.

}, author = {Escobar, Miquel}, editor = {Girbau, A. and Ventura, C. and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cPodlipnik21, title = {Personalized medicine in melanoma patients aided by artificial intelligence}, booktitle = {Clinical Translation of Medical Image Computing and Computer Assisted Interventions (CLINICCAI) Workshop at MICCAI}, year = {2021}, month = {09/2021}, abstract = {

The 8th Edition of the American Joint Committee on Cancer (AJCC) staging system1 is the current standard for classifying patients into prognostic and treatment groups. This classification is used to predict the evolution of the patient, and therefore treatment actions provided to the individual. However, patients at the same stage behave differently, indicating that the current classification system is often insufficient to provide a customized prognosis for each patient2. It is, therefore, necessary to improve patient classification into prognostic groups. Furthermore, patients{\textquoteright} systemic and surgical treatments often involve significant toxicities and morbidities that impact their quality of life (i.e., sentinel node biopsy is not needed for 80\% of the melanoma patients, 50\% of patients do not benefit from adjuvant treatment)3. Therefore, melanoma patients should benefit from a more precise risk estimation.

We create a survival dataset for melanoma risk estimation and train survival XGBoost algorithms4 to predict the mortality, relapse, and metastasis risk. We compare their performance to the AJCC 2018 risk stratification system. Furthermore, we train classifiers to predict the risk of a positive lymph node biopsy and distant metastasis on melanoma patients and compare the performance of the proposed system to the clinical practice.

}, author = {Sebastian Podlipnik and Carlos Hernandez and Anil Kiroglu and Sergio Garc{\'\i}a and Joan Ficapal and Julio Burgos and Neus Calbet and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana and Marc Combalia} } @conference {cMayoral21b, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using structural MRI}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Silvia Ingala and Luigi Lorenzini and Alle Meije Wink and Sven Haller and Jose Luis Molinuevo and Robin Wolz and Alessandro Palombit and Adam J Schwarz and Ga{\"e}l Chetelat and Pierre Payoux and Pablo Martinez-Lage and Giovanni Frisoni and Nick C Fox and Craig W Ritchie and Joanna M Wardlaw and Adam Waldman and Frederik Barkhof and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cPuig-Sitjes21a, title = {Real-time detection of overloads on the PFCs of W7-X}, booktitle = {European Conference on Plasma Diagnostics (ECPD 2021)}, year = {2021}, month = {06/2021}, publisher = {CLPU}, organization = {CLPU}, address = {online}, abstract = {

Wendelstein 7-X (W7-X) is the leading experiment on the path of demonstrating that stellarators are a feasible concept for a future power plant. One of its main goals is to prove quasi-steady-state operation in a reactor-relevant parameter regime. To this end, the surveillance and protection of the water-cooled plasma facing components (PFCs) against overheating is fundamental to guarantee a safe steady-state high-heat-flux operation. The fast reaction times required to prevent damage to the device makes imperative to fully automate the analysis of the thermographic images of the PFCs to detect the thermal events in real-time and timely interrupt operation through the interlock system if a critical event is detected. This imaging system, being the last line of defense, must be based on highly reliable image and risk analysis techniques to guarantee the integrity of the device under all circumstances. During the past operation phases, W7-X was equipped with inertially-cooled test divertor units and although the system still required manual supervision, several concepts to deal with the intrinsic 3D geometry of stellarators were put under test. With the experience gained, a new PFC protection system has been designed based on advanced image segmentation techniques. In order to achieve steady-state operation, however, a feedback control system able to prevent unnecessary interruptions of plasma operation is needed. The feedback control shall be guided by the image analysis, requiring a high-level scene understanding by means of computer vision and machine learning algorithms. Based on this analysis, the feedback control can then take the most effective counter measures to prevent the PFCs overloading by acting on the heating systems, the strike-line shape and positioning or on plasma detachment. In this work, we present the design of the real-time image analysis system for the protection of the PFCs of W7-X through the Interlock, and its validation results based on the analysis of the extensive existing experimental data. Additionally, we present the roadmap towards a feedback control system for the protection of the device in long high-performance plasmas.

}, url = {https://www.clpu.es/en/ECPD2021_Schedule/}, author = {Puig-Sitjes, A. and Jakubowski, M. and Gao, Y. and Drewelow, P. and Niemann, H. and Fellinger, J. and Moncada, V. and Pisano, F. and Belafdil, C. and Mitteau, R. and Aumeunier, M.-H. and Cannas, B. and Casas, J. and Salembier, P. and Clemente, R. and Dumke, S. and Winter, A. and Laqua, H. and Bluhm, T. and Brandt, K.} } @conference {cManas, title = {Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data}, booktitle = {International Conference in Computer Vision (ICCV)}, year = {2021}, publisher = {IEEE/CVF}, organization = {IEEE/CVF}, address = {Virtual}, abstract = {

Remote sensing and automatic earth monitoring are key to solve global-scale challenges such as disaster prevention, land use monitoring, or tackling climate change. Although there exist vast amounts of remote sensing data, most of it remains unlabeled and thus inaccessible for supervised learning algorithms. Transfer learning approaches can reduce the data requirements of deep learning algorithms. However, most of these methods are pre-trained on ImageNet and their generalization to remote sensing imagery is not guaranteed due to the domain gap. In this work, we propose Seasonal Contrast (SeCo), an effective pipeline to leverage unlabeled data for in-domain pre-training of re-mote sensing representations. The SeCo pipeline is com-posed of two parts. First, a principled procedure to gather large-scale, unlabeled and uncurated remote sensing datasets containing images from multiple Earth locations at different timestamps. Second, a self-supervised algorithm that takes advantage of time and position invariance to learn transferable representations for re-mote sensing applications. We empirically show that models trained with SeCo achieve better performance than their ImageNet pre-trained counterparts and state-of-the-art self-supervised learning methods on multiple downstream tasks. The datasets and models in SeCo will be made public to facilitate transfer learning and enable rapid progress in re-mote sensing applications.

}, url = {https://arxiv.org/abs/2103.16607}, author = {Ma{\~n}as,Oscar and Lacoste, Alexandre and Xavier Gir{\'o}-i-Nieto and Vazquez, David and Rodr{\'\i}guez, Pau} } @article {aSalgueiroa, title = {Single-image super-resolution of Sentinel-2 low resolution bands with residual dense convolutional neural networks}, journal = {Remote Sensing}, volume = {13}, year = {2021}, month = {2021}, pages = {5007}, abstract = {

Sentinel-2 satellites have become one of the main resources for\  Earth observation images because they are free of charge, have a great spatial coverage and high temporal revisit. Sentinel-2 senses the same location providing different spatial resolutions as well as generating a multi-spectral image with 13 bands of 10, 20, and 60 m/pixel. In this work, we propose a single-image super-resolution model based on convolutional neural networks that enhances the low-resolution bands (20 m and 60 m) to reach the maximal resolution sensed (10 m) at the same time, whereas other approaches provide two independent models for each group of LR bands. Our proposed model, named Sen2-RDSR, is made up of Residual in Residual blocks that produce two final outputs at maximal resolution, one for 20 m/pixel bands and the other for 60 m/pixel bands. The training is done in two stages, first focusing on 20 m bands and then on the 60 m bands. Experimental results using 6 quality metrics (RMSE, SRE, SAM, PSNR, SSIM, ERGAS) show that our model has superior performance compared to other state-of-the-art approaches, and it is very effective and suitable as a preliminary step for land and coastal applications, as studies involving pixel-based classification for Land-Use-Land-Cover or the generation of vegetation indices.

}, author = {Luis Salgueiro and Javier Marcello and Ver{\'o}nica Vilaplana} } @mastersthesis {xOriola, title = {Species-agnostic Local Ancestry Inference on Genomic Data with Convolutions}, year = {2021}, abstract = {

Local Ancestry Inference (LAI) is the high resolution prediction of ancestry (African, European, ...) across a DNA sequence. LAI is becoming increasingly important in DNA sequence analysis for the study of human ancestry and migrations. It is also necessary for polygenic risk scores research (prediction of traits and disease risk). Most current LAI models are built for specific species, set of ancestries and chromosomes, hence a new model needs to be trained from scratch for every slightly different setting. This creates a big barrier for research and industry to shift across different LAI scenarios. In this thesis we present SALAI-Net, the first statistical method for LAI with reference panel that can be used on any set of species and ancestries (species-agnostic). Loter is the state of the art in species-agnostic models with reference panel, and is based on a dynamic programming algorithm. However, it is slow and does not perform very well in small reference panel settings. Our model is based on a novel hand-engineered template matching block followed by a convolutional smoothing filter optimized to minimize cross-entropy loss on a training dataset. The right choice of DNA sequence encoding, similarity features and architecture is what makes our model able to generalize well to unseen ancestries, species, and different chromosomes. We benchmark our models on whole genome data of humans and we test the ability to generalize to dog species when trained on human data. Our models outperform the state-of-the-art method by a big margin in terms of accuracy, testing in different settings and datasets. Moreover, it is up to two orders of magnitude faster. Our model also shows close to no generalization gap when switching between species.

}, author = {Oriol, Benet}, editor = {Mas-Montserrat, Daniel and Ioannidis, Alexander G. and Xavier Gir{\'o}-i-Nieto} } @phdthesis {dGirbau21, title = {Sports broadcasting and multiple object tracking with deep learning methods}, volume = {PhD}, year = {2021}, month = {03/2021}, type = {Industrial}, abstract = {

Since less than a decade ago, deep learning techniques started to dominate many different\ fields, revolutionizing the possibilities of artificial intelligence. Seeing their potential, industrial\ sectors started to invest in applying such technologies as key components of the company\ strategy. This thesis has been developed in an industrial context, in AutomaticTV. The main\ focus along this period has been the transfer of knowledge and know-how between academia\ and industry, development of tools to exploit this knowledge, the exploration of new techniques\ for future challenges, and, from an academic research perspective, contributions to the\ multiple object tracking problem.

The first part of the thesis is devoted to the introduction of deep learning technologies to\ AutomaticTV, a company dedicated to automatic sports analysis and broadcasting, and the\ development of tools and tasks that surround the application.

The second part of this thesis introduces the contributions to the multiple object tracking\ challenge. We present TrajE, a trajectory estimator based on mixture density networks and\ beam search, used to boost the performance of existing multiple object trackers, and introduce\ an occlusion reconstruction step using the estimated trajectory information. By adding TrajE\ to an existing multiple object tracker, we boost its performance by 6.3, 1.8 points in MOTA and\ IDF1 scores respectively, becoming the new state of the art in the MOTChallenge dataset.

}, author = {Girbau, A.}, editor = {Rius, Ignasi and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @mastersthesis {dGeleta21, title = {Unsupervised learning with applications in genomic}, volume = {BSc Data Science Engineering}, year = {2021}, abstract = {

In this study we show the power of variational autoencoders (VAEs) for a variety of tasks relating to the interpretation and compression of genomic data. The unsupervised setting allows for detecting and learning of granular population structure and inferring of new informative latent factors, opening up an avenue for applications in dimensionality reduction, data simulation, population classification, imputation, and lossless genomic data compression. The latent spaces of VAEs are able to capture and represent clearly differentiated Gaussian-like clusters of similar genetic composition on a fine-scale with a relatively small number of Single Nucleotide Polymorphisms (SNPs) as input. Furthermore, sequences can be decomposed into latent representations and reconstruction errors (residuals) providing a sparse representation that provides a means for efficient lossless compression.

Identifying genetic clusters can be important when performing genome-wide association studies and provides an alternative to self-reported ethnic labels, which are culturally constructed and vary according to the location and individual. A variety of unsupervised dimensionality reduction methods have been explored in the past for such applications, including PCA, MDS, t-SNE, and UMAP. Our proposed VAE can represent the population structure as a Gaussian-distributed continuous multi-dimensional representation and as classification probabilities providing flexible and interpretable population descriptors.

We train our VAE method with several worldwide whole genome datasets from both humans and canids, and evaluate the performance of the different proposed applications with networks with and without ancestry conditioning. Our experiments show that different population groups have significantly differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, noting its effect on compression across populations and connect these patterns to historical migrations and ancestral relationships.

}, author = {Geleta, Margarita}, editor = {Mas-Montserrat, Daniel and Ioannidis, Alexander G. and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xGraneroa, title = {2D to 3D body pose estimation for sign language with Deep Learning}, year = {2020}, abstract = {

This project aims at leveraging the challenge of using 3D poses for Sign Language translation or animation by transforming 2D pose datasets into 3D ones. The goal is, using a 3D dataset of American Sign Language, to train a deep neural network that will predict the depth coordinates of the skeleton keypoints from 2D coordinates. Specifically, it will be explored a Long Short-Term Memory network, an architecture broadly used for sequence to sequence tasks. The conclusions extracted on this report are that despite some of the results being good enough to be used for actual 3D SL annotation, the majority of them lack the precision to do so, and they are too variant with respect to the dataset split. It is also concluded that the solutions approached here could be improved by adding some regularization methods, more powerful hardware to run better experiments, and new input features such as keypoint visibility.

}, author = {P{\'e}rez-Granero, Pol}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @conference {cMora20, title = {Brain Tumor Segmentation using 3D-CNNs with Uncertainty Estimation}, booktitle = {MICCAI 2020 - Brain Lesion Workshop (BrainLes), Multimodal Brain Tumor Segmentation Challenge (BRATS)}, year = {2020}, month = {10/2020}, abstract = {

Automation of brain tumors in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural networks (CNNs) have shown improved results in the task. However, high memory consumption is still a problem in 3D-CNNs. Moreover, most methods do not include uncertainty information, which is specially critical in medical diagnosis. This work proposes a 3D encoder-decoder architecture, based on V-Net which is trained with patching techniques to reduce memory consumption and decrease the effect of unbalanced data. We also introduce voxel-wise uncertainty, both epistemic and aleatoric using test-time dropout and data-augmentation respectively. Uncertainty maps can provide extra information to expert neurologists, useful for detecting when the model is not confident on the provided segmentation.

}, author = {Laura Mora and Ver{\'o}nica Vilaplana} } @article {aGene-Molac, title = {Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry}, journal = {Computers and Electronics in Agriculture}, volume = {169}, year = {2020}, month = {02/2020}, abstract = {

The development of remote fruit detection systems able to identify and 3D locate fruits provides opportunities to improve the efficiency of agriculture management. Most of the current fruit detection systems are based on 2D image analysis. Although the use of 3D sensors is emerging, precise 3D fruit location is still a pending issue. This work presents a new methodology for fruit detection and 3D location consisting of: (1) 2D fruit detection and segmentation using Mask R-CNN instance segmentation neural network; (2) 3D point cloud generation of detected apples using structure-from-motion (SfM) photogrammetry; (3) projection of 2D image detections onto 3D space; (4) false positives removal using a trained support vector machine. This methodology was tested on 11 Fuji apple trees containing a total of 1455 apples. Results showed that, by combining instance segmentation with SfM the system performance increased from an F1-score of 0.816 (2D fruit detection) to 0.881 (3D fruit detection and location) with respect to the total amount of fruits. The main advantages of this methodology are the reduced number of false positives and the higher detection rate, while the main disadvantage is the high processing time required for SfM, which makes it presently unsuitable for real-time work. From these results, it can be concluded that the combination of instance segmentation and SfM provides high performance fruit detection with high 3D data precision. The dataset has been made publicly available and an interactive visualization of fruit detection results is accessible at http://www.grap.udl.cat/documents/photogrammetry_fruit_detection.html

}, keywords = {Fruit detection, Fruit location, Mask R-CNN, Structure-from-motion, Terrestrial remote sensing}, issn = {ISSN: 0168-1699}, doi = {https://doi.org/10.1016/j.compag.2019.105165}, url = {https://doi.org/10.1016/j.compag.2019.105165}, author = {Gen{\'e}-Mola, Jordi and Sanz, Ricardo and Rosell-Polo, Joan R. and Morros, J.R. and Ruiz-Hidalgo, J. and Ver{\'o}nica Vilaplana and Gregorio, Eduard} } @article {aGene-Mola20, title = {Fuji-SfM dataset: A collection of annotated images and point clouds for Fuji apple detection and location using structure-from-motion photogrammetry}, volume = {Data in Brief}, year = {2020}, month = {06/2020}, keywords = {Fruit detection, Mask R-CNN, Photogrammetry, Structure-from-motion, Terrestrial remote sensing, Yield mapping, Yield prediction}, doi = {https://doi.org/10.1016/j.dib.2020.105591}, author = {Gen{\'e}-Mola, Jordi and Sanz, Ricardo and Rosell-Polo, Joan R. and Morros, J.R. and Ruiz-Hidalgo, J. and Ver{\'o}nica Vilaplana and Gregorio, Eduard} } @article {9103248, title = {Grounded Sequence to Sequence Transduction}, journal = {IEEE Journal of Selected Topics in Signal Processing}, volume = {14}, year = {2020}, month = {05/2020}, pages = {577-591}, abstract = {

Speech recognition and machine translation have made major progress over the past decades, providing practical systems to map one language sequence to another. Although multiple modalities such as sound and video are becoming increasingly available, the state-of-the-art systems are inherently unimodal, in the sense that they take a single modality {\textemdash} either speech or text {\textemdash} as input. Evidence from human learning suggests that additional modalities can provide disambiguating signals crucial for many language tasks. In this article, we describe the How2 dataset\ , a large, open-domain collection of videos with transcriptions and their translations. We then show how this single dataset can be used to develop systems for a variety of language tasks and present a number of models meant as starting points. Across tasks, we find that building multimodal architectures that perform better than their unimodal counterpart remains a challenge. This leaves plenty of room for the exploration of more advanced solutions that fully exploit the multimodal nature of the How2 dataset\ , and the general direction of multimodal learning with other datasets as well.

}, author = {L. Specia and L. Barrault and O. Caglayan and Amanda Duarte and D. Elliott and S. Gella and N. Holzenberger and C. Lala and S. J. Lee and J. Libovicky and P. Madhyastha and F. Metze and K. Mulligan and A. Ostapenko and S. Palaskar and R. Sanabria and J. Wang and R. Arora} } @mastersthesis {xMuschik20, title = {Learn2Sign : sign language recognition and translation using human keypoint estimation and transformer model}, year = {2020}, abstract = {

Sign language recognition and translation has been an active research field in the recent years with most approaches using deep neural networks to extract information from sign language data. This work investigates the mostly disregarded approach of using human keypoint estimation from image and video data with OpenPose in combination with transformer network architecture. Firstly, it was shown that it is possible to recognize individual signs (4.5\% word error rate (WER)). Continuous sign language recognition though was more error prone (77.3\% WER) and sign language translation was not possible using the proposed methods, which might be due to low accuracy scores of human keypoint estimation by OpenPose and accompanying loss of information or insufficient capacities of the used transformer model. Results may improve with the use of datasets containing higher repetition rates of individual signs or focusing more precisely on keypoint extraction of hands.

}, doi = {10.18419/opus-11197}, url = {https://elib.uni-stuttgart.de/handle/11682/11214}, author = {Muschik, Peter}, editor = {A. Duarte and Xavier Gir{\'o}-i-Nieto} } @article {aCasamitjanab, title = {NeAT: a nonlinear analysis toolbox for neuroimaging}, journal = {Neuroinformatics}, year = {2020}, month = {03/2020}, abstract = {

NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer{\textquoteright}s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/.

}, keywords = {Alzheimer{\textquoteright}s disease, APOE, GAM, GLM, inference, neuroimaging, nonlinear., SVR}, doi = {10.1007/s12021-020-09456-w}, url = {https://link.springer.com/article/10.1007/s12021-020-09456-w}, author = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Santi Puch and Asier Aduriz and Carlos Lopez and G. Operto and R. Cacciaglia and C. Falcon and J.L. Molinuevo and Juan D. Gispert} } @mastersthesis {xPunti20, title = {PixInPix: Hidding Pixels in Pixels}, year = {2020}, abstract = {

PixInPix is an steganography hidding system of images within other images.\ The system designed is able to create, from an cover image and a message, a new steganography image. This new stego-image looks as similar as possible as the cover but has the message hidden in it.\ Our approach adopts the U-net architecture and combines two reconstruction losses to provide a simple yet effective approach tested in low resolution images from MNIST, CIFAR and ImageNet.

}, author = {Punt{\'\i}, Cristina}, editor = {McGuinness, Kevin and Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @article {aCasamitjanac, title = {Projection to Latent Spaces disentangles pathological effects on brain morphology in the asymptomatic phase of Alzheimer{\textquoteright}s disease}, journal = {Frontiers in Neurology, section Applied Neuroimaging}, volume = {11}, year = {2020}, month = {07/2020}, chapter = {648}, abstract = {

Alzheimer{\textquoteright}s disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers such as cerebrospinal fluid (CSF) levels of Aβ, p-tau and t-tau. In parallel, brain anatomy can be characterized through imaging techniques such as magnetic resonance imaging (MRI). In this work, we relate both sets of measurements seeking associations between biomarkers and brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we use the projection to latent structures (PLS)\ method. Using PLS, we find a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We look for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we use a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We apply this technique to the study of subjects in the whole AD continuum from the preclinical asymptomatic stages all through to the symptomatic groups. Subsequent analyses involve splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitive impaired subjects (MCI) and dementia subjects (AD-dementia) where all symptoms are due to AD.

}, keywords = {Alzheimer{\textquoteright}s disease, brain morphology, CSF biomarkers, Latent model, PLS, preclinical AD}, author = {Adri{\`a} Casamitjana and Paula Petrone and Jose Luis Molinuevo and Juan D. Gispert and Ver{\'o}nica Vilaplana} } @mastersthesis {xManas, title = {Self-Supervised Visual Representation Learning for Remote Sensing}, year = {2020}, abstract = {

With the creation of large-scale annotated datasets such as the ImageNet, fully-supervised machine learning methods have become the standard for solving computer vision tasks. These methods require large amounts of labeled data, which are usually obtained with crowdsourcing tools or social media tags. However, these approaches do not scale for specialized domains, such as medical or satellite imaging, where annotations must be provided by experts at a prohibitive cost. Recently, self-supervised learning has emerged as an alternative for obtaining transferable visual representations from unlabeled data. Models based on these representations match the performance of fully-supervised models while only requiring a small fraction of the annotations. In this work, we aim to explore the application of self-supervised learning methods in the remote sensing domain. We propose a contrastive approach for learning visual representations by exploiting the multi-spectral information of satellite images. These representations serve as a good starting point for a variety of downstream tasks that involve remote sensing imagery, accelerating convergence with fewer labeled examples.

Best thesis award 2020 (draw with 4 more other works)\ 

}, author = {Ma{\~n}as,Oscar}, editor = {Rodr{\'\i}guez, Pau and Xavier Gir{\'o}-i-Nieto} } @conference {cPuig-Sitjes20, title = {Strategy for the real-time detection of thermal events on the plasma facing components of Wendelstein 7-X}, booktitle = {31st Symposium on Fusion Technology (SOFT2020)}, year = {2020}, month = {09/2020}, address = {Dubrovnik, Croatia}, abstract = {

Wendelstein 7-X (W7-X), the most advanced fusion experiment in the stellarator line, aims at demonstrating the feasibility of the stellarator concept as a future fusion power plant. It is planned to restart operation by the end of 2021 with a high heat flux divertor and water-cooled plasma facing components (PFCs) to demonstrate steady-state operation. With plasma energy limits starting at 1 GJ and gradually increasing to 18 GJ over several experimental campaigns, the PFCs have to be protected from overheating. For that, a fully autonomous system is required in order to prevent damage to the plasma facing components due to thermal events.
During the last experimental campaign, when W7-X was equipped with inertially cooled test divertor units, extensive experience was gained with the preliminary design of the thermal event detection system. By then, the system was not yet real-time capable and it was not fully automated, requiring manual supervision between discharges. This experience, however, allowed to prove the validity of some design concepts and to define the new strategy towards the protection of the machine in steady-state operation, when the system will be connected to the Interlock System and the feedback control.
In this work, the design of the real-time thermal event detection system for W7-X for steady-state operation is presented. The system is based on the thermography and video diagnostics to monitor the divertor units, the baffles, and the wall heat-shields and panels. It will be implemented on a real-time system and integrated in CoDaC{\textquoteright}s safety infrastructure. The system relies on computer vision and machine learning techniques to perform a spatio-temporal analysis to detect and classify the thermal events and to perform a risk evaluation. The results and the main conclusions drawn from the analysis of the data from the past campaign are reported.

}, url = {http://hdl.handle.net/21.11116/0000-0007-7FF5-7}, author = {Puig-Sitjes, A. and Jakubowski, M. and Fellinger, J. and Drewelow, P. and Gao, Y. and Niemann, H. and Sunn-Pedersen, T. and K{\"o}nig, R. and Naujoks, D. and Winter, A. and Laqua, H. and Dumke, S. and Moncada, V. and Belafdil, C. and Mitteau, R. and Aumeunier, M.-H. and Pisano, F. and Aymerich, E. and Cannas, B. and Kocsis, G. and Szepesi, T. and Cseh, G. and Szabolics, T. and Casas, J. and Morros, J.R. and Salembier, P. and Clemente, R. and Cobos, M. and I. Caminal and Palacios Corral, A. and Moreno Punzano, A. and Quiceno Lopera, S.} } @article {aSalgueiro, title = {Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks}, journal = {Remote Sensing}, volume = {12}, year = {2020}, month = {01/2020}, abstract = {

Sentinel-2 satellites provide multi-spectral optical remote sensing images with four bands at 10 m of spatial resolution. These images, due to the open data distribution policy, are becoming an important resource for several applications. However, for small scale studies, the spatial detail of these images might not be sufficient. On the other hand, WorldView commercial satellites offer multi-spectral images with a very high spatial resolution, typically less than 2 m, but their use can be impractical for large areas or multi-temporal analysis due to their high cost. To exploit the free availability of Sentinel imagery, it is worth considering deep learning techniques for single-image super-resolution tasks, allowing the spatial enhancement of low-resolution (LR) images by recovering high-frequency details to produce high-resolution (HR) super-resolved images. In this work, we implement and train a model based on the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) with pairs of WorldView-Sentinel images to generate a super-resolved multispectral Sentinel-2 output with a scaling factor of 5. Our model, named RS-ESRGAN, removes the upsampling layers of the network to make it feasible to train with co-registered remote sensing images. Results obtained outperform state-of-the-art models using standard metrics like PSNR, SSIM, ERGAS, SAM and CC. Moreover, qualitative visual analysis shows spatial improvements as well as the preservation of the spectral information, allowing the super-resolved Sentinel-2 imagery to be used in studies requiring very high spatial resolution.

}, author = {Luis Salgueiro and Javier Marcello and Ver{\'o}nica Vilaplana} } @conference {cCombaliad, title = {Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification}, booktitle = {CVPR 2020, ISIC Skin Image Analysis Workshop}, year = {2020}, month = {2020}, abstract = {

The high performance of machine learning algorithms for the task of skin lesion classification has been shown over the past few years. However, real-world implementations are still scarce. One of the reasons could be that most methods do not quantify the uncertainty in the predictions and are not able to detect data that is anomalous or significantly different from that used in training, which may lead to a lack of confidence in the automated diagnosis or errors in the interpretation of results.

In this work, we explore the use of uncertainty estimation techniques and metrics for deep neural networks based on Monte-Carlo sampling and apply them to the problem of skin lesion classification on data from ISIC Challenges 2018 and 2019.

Our results show that uncertainty metrics can be successfully used to detect difficult and out-of-distribution samples.

}, author = {Marc Combalia and Ferran Hueto and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana} } @conference {cMoliner, title = {Weakly Supervised Semantic Segmentation for Remote Sensing Hyperspectral Imaging}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020)}, year = {2020}, month = {05/2020}, abstract = {

This paper studies the problem of training a semantic segmentation neural network with weak annotations, in order to be applied in aerial vegetation images from Teide National Park. It proposes a Deep Seeded Region Growing system which consists on training a semantic segmentation network from a set of seeds generated by a Support Vector Machine. A region growing algorithm module is applied to the seeds to progressively increase the pixel-level supervision. The proposed method performs better than an SVM, which is one of the most popular segmentation tools in remote sensing image applications.

}, author = {Eloi Moliner and Luis Salgueiro and Ver{\'o}nica Vilaplana} } @conference {cGorrizc, title = {Assessing Knee OA Severity with CNN attention-based end-to-end architectures}, booktitle = {International Conference on Medical Imaging with Deep Learning (MIDL) 2019}, year = {2019}, month = {02/2019}, publisher = {JMLR}, organization = {JMLR}, address = {London, United Kingdom}, abstract = {

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to\ automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which\ incorporates trainable attention modules acting as unsupervised fine-grained detectors of\ the region of interest (ROI). The proposed attention modules can be applied at different\ levels and scales across any CNN pipeline helping the network to learn relevant attention\ patterns over the most informative parts of the image at different resolutions. We test\ the proposed attention mechanism on existing state-of-the-art CNN architectures as our\ base models, achieving promising results on the benchmark knee OA datasets from the\ osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST). All the codes\ from our experiments will be publicly available on the github repository: https://github.com/marc-gorriz/KneeOA-CNNAttention

}, url = {http://proceedings.mlr.press/v102/gorriz19a.html}, author = {G{\'o}rriz, Marc and Antony, Joseph and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @conference {cLopez-Palmab, title = {Audience measurement using a top-view camera and oriented trajectories}, booktitle = {IEEE IECON 2019}, year = {2019}, month = {10/2019}, address = {Lisbon, Portugal}, abstract = {

One of the points of maximum interest in the valuation of the areas of commercial advertising is the probability with which that publicity will be seen. This paper presents a method based on top-view camera measurement, where the probability of viewing is estimated based on the trajectories and movements of the heads of the passerby individuals in the area of interest. Using a camera with a depth sensor, the heads of the people in the range of view can be detected and modeled. This allows to\  determine the orientation of the head which is used to estimate the direction of vision. A tracking by detection algorithm\  allows to compute the trajectory of each user. The attention at each advertising point is estimated based on the trajectories and head orientations of the individuals in the area of interest.

}, keywords = {attention time, depth sensor, top-view camera, tracking}, doi = {https://doi.org/10.1109/IECON.2019.8927222}, author = {Manuel L{\'o}pez-Palma and Morros, J.R. and Montserrat Corbal{\'a}n and Javier Gago} } @conference {cCombaliac, title = {BCN20000: Dermoscopic Lesions in the Wild}, booktitle = {International Skin Imaging Collaboration (ISIC) Challenge on Dermoscopic Skin Lesion Analysis 2019}, year = {2019}, month = {10/2019}, abstract = {

This article summarizes the BCN20000 dataset, composed of 19424 dermoscopic images of skin lesions captured from 2010 to 2016 in the facilities of the Hospital Cl{\'\i}nic in Barcelona. With this dataset, we aim to study the problem of unconstrained classification of dermoscopic images of skin cancer, including lesions found in hard-to-diagnose locations (nails and mucosa), large lesions which do not fit in the aperture of the dermoscopy device, and hypo-pigmented lesions. The BCN20000 will be provided to the participants of the ISIC Challenge 2019 \cite{ISIC2019}, where they will be asked to train algorithms to classify dermoscopic images of skin cancer automatically.

}, author = {Marc Combalia and Noel C. F. Codella and Veronica Rotemberg and Brian Helba and Ver{\'o}nica Vilaplana and Ofer Reiter and Cristina Carrera and Alicia Barreiro and Allan C. Halpern and Susana Puig and Josep Malvehy} } @article {aWang19, title = {Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge}, journal = {IEEE Transactions on Medical Imaging}, year = {2019}, month = {2019/2/27}, abstract = {

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.

}, doi = {10.1109/TMI.2019.2901712}, author = {Li Wang and Dong Nie and Guannan Li and Elodie Puybareau and Jose Dolz and Qian Zhang and Fan Wang and Jing Xia and Zhengwang Wu and Jiawei Chen and Kim-HanThung and Toan Duc Bui and Jitae Shin and Guodong Zeng and Guoyan Zheng and Vladimir S. Fonov and Andrew Doyle and Yongchao Xu and Pim Moeskops and Josien Pluim and Christian Desrosiers and Ismail Ben Ayed and Gerard Sanroma and Oualid Benkarim and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Weili Lin and Gang Li and Dinggang Shen} } @conference {cSalgueiro, title = {Comparative study of upsampling methods for super-resolution in remote sensing}, booktitle = {International Conference on Machine Vision}, year = {2019}, month = {11/2019}, abstract = {

Many remote sensing applications require high spatial resolution images, but the elevated cost of these images makes some studies unfeasible. Single-image super-resolution algorithms can improve the spatial resolution of a low-resolution image by recovering feature details learned from pairs of low-high resolution images. In this work, several configurations of ESRGAN, a state-of-the-art algorithm for image super-resolution are tested. We make a comparison between several scenarios, with different modes of upsampling and channels involved.\  The best results are obtained training a model with RGB-IR channels and using progressive upsampling.\ 

}, keywords = {deep learning, Remote sensing, Super-resolution, WorldView-2}, author = {Luis Salgueiro and Javier Marcello and Ver{\'o}nica Vilaplana} } @phdthesis {dSalvador, title = {Computer Vision beyond the visible: Image understanding through language}, volume = {Phd}, year = {2019}, school = {Universitat Politecnica de Catalunya}, address = {Barcelona}, abstract = {

In the past decade, deep neural networks have revolutionized computer vision. High performing deep neural architectures trained for visual recognition tasks have pushed the field towards methods relying on learned image representations instead of hand-crafted ones, in the seek of designing end-to-end learning methods to solve challenging tasks, ranging from long-lasting ones such as image classification to newly emerging tasks like image captioning.\ 

As this thesis is framed in the context of the rapid evolution of computer vision, we present contributions that are aligned with three major changes in paradigm that the field has recently experienced, namely 1) the power of re-utilizing deep features from pre-trained neural networks for different tasks, 2) the advantage of formulating problems with end-to-end solutions given enough training data, and 3) the growing interest of describing visual data with natural language rather than pre-defined categorical label spaces, which can in turn enable visual understanding beyond scene recognition.

The first part of the thesis is dedicated to the problem of visual instance search, where we particularly focus on obtaining meaningful and discriminative image representations which allow efficient and effective retrieval of similar images given a visual query. Contributions in this part of the thesis involve the construction of sparse Bag-of-Words image representations from convolutional features from a pre-trained image classification neural network, and an analysis of the advantages of fine-tuning a pre-trained object detection network using query images as training data.

The second part of the thesis presents contributions to the problem of image-to-set prediction, understood as the task of predicting a variable-sized collection of unordered elements for an input image. We conduct a thorough analysis of current methods for multi-label image classification, which are able to solve the task in an end-to-end manner by simultaneously estimating both the label distribution and the set cardinality. Further, we extend the analysis of set prediction methods to semantic instance segmentation, and present an end-to-end recurrent model that is able to predict sets of objects (binary masks and categorical labels) in a sequential manner.

Finally, the third part of the dissertation takes insights learned in the previous two parts in order to present deep learning solutions to connect images with natural language in the context of cooking recipes and food images. First, we propose a retrieval-based solution in which the written recipe and the image are encoded into compact representations that allow the retrieval of one given the other. Second, as an alternative to the retrieval approach, we propose a generative model to predict recipes directly from food images, which first predicts ingredients as sets and subsequently generates the rest of the recipe one word at a time by conditioning both on the image and the predicted ingredients.

}, url = {https://www.tdx.cat/handle/10803/667162}, author = {Amaia Salvador}, editor = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cCasamitjana19, title = {Detection of Amyloid Positive Cognitively unimpaired individuals using voxel-based machine learning on structural longitudinal brain MRI}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2019}, month = {07/2019}, author = {Adri{\`a} Casamitjana and P. Petrone and C. Falcon and M. Artigues and G. Operto and R. Cacciaglia and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @article {aCasamitjana19, title = {Detection of Amyloid-Positive Cognitively Unimpaired Individuals Using Voxel-Based Machine Learning on Structural Longitudinal Brain MRI}, journal = {Alzheimer{\textquoteright}s \& Dementia}, volume = {15}, year = {2019}, month = {07/2019}, chapter = {752}, abstract = {

Magnetic resonance imaging (MRI) has unveiled specific AD alterations at different stages of the AD pathophysiologic continuum that constitutes what has been established as the {\textquoteleft}AD signature{\textquoteright}. To what extent MRI can detect amyloid-related cerebral changes from structural MRI in unimpaired individuals is still an area open for exploration.

}, issn = {1552-5260}, doi = {10.1016/j.jalz.2019.06.2796}, author = {Adri{\`a} Casamitjana and P. Petrone and C. Falcon and M. Artigues and G. Operto and R. Cacciaglia and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @article {aRoisman18, title = {Differential expression of long non-coding RNAs related to proliferation and histological diversity in follicular lymphomas}, journal = {British Journal of Haematology}, volume = {184}, year = {2019}, month = {Feb 2019}, pages = {373-383}, issn = {ISSN:1365-2141}, doi = {DOI: 10.1111/bjh.15656}, author = {A. Roisman and A. Navarro and G. Clot and G. Castellano and B. Gonzalez-Farre and P. P{\'e}rez-Galan and A. Esteve and M. Dabad and S. Heath and M. Gut and Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras and I. Slavusky and L. Magnano and H. Horn and A. Rosenwald and G. Ott and M. Aymerich and A. L{\'o}pez-Guillermo and P. Jares and J.I. Mart{\'\i}n-Subero and E. Campo and L. Hern{\'a}ndez} } @conference {cCombaliab, title = {Digitally Stained Confocal Microscopy through Deep Learning}, booktitle = {International Conference on Medical Imaging with Deep Learning (MIDL 2019)}, year = {2019}, month = {07/2019}, address = {London}, abstract = {

Specialists have used confocal microscopy in the ex-vivo modality to identify tumors with\ an overall sensitivity of 96.6\% and specicity of 89.2\%. However, this technology hasn{\textquoteright}t\ established yet in the standard clinical practice because most pathologists lack the knowledge to interpret its output. In this paper we propose a combination of deep learning and\ computer vision techniques to digitally stain confocal microscopy images into H\&E-like\ slides, enabling pathologists to interpret these images without specic training. We use a\ fully convolutional neural network with a multiplicative residual connection to denoise the\ confocal microscopy images, and then stain them using a Cycle Consistency Generative\ Adversarial Network.

}, author = {Marc Combalia and Javiera P{\'e}rez-Anker and Adriana Garc{\'\i}a-Herrera and Ll{\'u}cia Alos and Ver{\'o}nica Vilaplana and Ferran Marques and Susana Puig and Josep Malvehy} } @article {aGene-Molab, title = {Fruit Detection in an Apple Orchard Using a Mobile Terrestrial Laser Scanner}, journal = {Biosystems Engineering}, volume = {187}, year = {2019}, month = {09/2019}, chapter = {171}, abstract = {

The development of reliable fruit detection and localization systems provides an opportunity to improve the crop value and management by limiting fruit spoilage and optimized harvesting practices. Most proposed systems for fruit detection are based on RGB cameras and thus are affected by intrinsic constraints, such as variable lighting conditions. This work presents a new technique that uses a mobile terrestrial laser scanner (MTLS) to detect and localise Fuji apples. An experimental test focused on Fuji apple trees (Malus domestica Borkh. cv. Fuji) was carried out. A 3D point cloud of the scene was generated using an MTLS composed of a Velodyne VLP-16 LiDAR sensor synchronized with an RTK-GNSS satellite navigation receiver. A reflectance analysis of tree elements was performed, obtaining mean apparent reflectance values of 28.9\%, 29.1\%, and 44.3\% for leaves, branches and trunks, and apples, respectively. These results suggest that the apparent reflectance parameter (at 905 nm wavelength) can be useful to detect apples. For that purpose, a four-step fruit detection algorithm was developed. By applying this algorithm, a localization success of 87.5\%, an identification success of 82.4\%, and an F1-score of 0.858 were obtained in relation to the total amount of fruits. These detection rates are similar to those obtained by RGB-based systems, but with the additional advantages of providing direct 3D fruit location information, which is not affected by sunlight variations. From the experimental results, it can be concluded that LiDAR-based technology and, particularly, its reflectance information, has potential for remote apple detection and 3D location.

}, issn = {1537-5110}, doi = {10.1016/j.biosystemseng.2019.08.017}, url = {https://authors.elsevier.com/c/1Zmc45Tbkk9EHW}, author = {Gen{\'e}-Mola, Jordi and Gregorio, Eduard and Guevara, Javier and Auat Cheein, Fernando and Sanz, Ricardo and Escol{\`a}, Alexandre and Llorens Calveras, Jordi and Morros, J.R. and Ruiz-Hidalgo, J. and Ver{\'o}nica Vilaplana and Rosell-Polo, Joan R.} } @mastersthesis {xCaselles, title = {Integrating low-level motion cues in deep video saliency}, year = {2019}, abstract = {

This thesis investigates the importance of motion when predicting saliency in videos. Naturally, humans observe both dynamic and static objects. When we are focused on watching a video, we tend to keep our eyes on the objects that are moving in the scene, items that we quickly recognize, as well as to those that attract our attention. In this work, different experiments are presented to corroborate this implication. Various approaches will be shown implementing an adaptation of the SalBCE neural network by using only motion. A simple implementation is proposed for the generation of saliency maps using previously extracted static and dynamic information from the images. The DHF1K dataset has been used for the experiment{\textquoteright}s realization.

}, keywords = {Motion, Saliency, video}, author = {Caselles, Pol}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @article {aGene-Molaa, title = {KFuji RGB-DS database: Fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data}, journal = {Data in Brief}, year = {2019}, month = {07/2019}, abstract = {

This article contains data related to the research article entitle {\textquotedblleft}Multi-modal Deep Learning for Fruit Detection Using RGB-D Cameras and their Radiometric Capabilities{\textquotedblright} [1]. The development of reliable fruit detection and localization systems is essential for future sustainable agronomic management of high-value crops. RGB-D sensors have shown potential for fruit detection and localization since they provide 3D information with color data. However, the lack of substantial datasets is a barrier for exploiting the use of these sensors. This article presents the KFuji RGB-DS database which is composed by 967 multi-modal images of Fuji apples on trees captured using Microsoft Kinect v2 (Microsoft, Redmond, WA, USA). Each image contains information from 3 different modalities: color (RGB), depth (D) and range corrected IR intensity (S). Ground truth fruit locations were manually annotated, labeling a total of 12,839 apples in all the dataset. The current dataset is publicly available at http://www.grap.udl.cat/publicacions/datasets.html.

}, keywords = {Depth cameras; RGB-D, Fruit detection, Fruit reflectance, Fuji apple, Multi-modal dataset}, doi = {10.1016/j.dib.2019.104289}, author = {Gen{\'e}-Mola, Jordi and Ver{\'o}nica Vilaplana and Rosell-Polo, Joan R. and Morros, J.R. and Ruiz-Hidalgo, J. and Gregorio, Eduard} } @article {aGene-Mola, title = {Multi-modal Deep Learning for Fuji Apple Detection Using RGB-D Cameras and their Radiometric Capabilities}, journal = {Computers and Electronics in Agriculture}, volume = {162}, year = {2019}, month = {07/2019}, chapter = {689-698}, abstract = {

Fruit detection and localization will be essential for future agronomic management of fruit crops, with applications in yield prediction, yield mapping and automated harvesting. RGB-D cameras are promising sensors for fruit detection given that they provide geometrical information with color data. Some of these sensors work on the principle of time-of-flight (ToF) and, besides color and depth, providethe backscatter signal intensity. However, this radiometric capability has not been exploited for fruit detection applications. This workpresents the KFuji RGB-DS database, composed of 967 multi-modal images containing a total of 12,839 Fuji apples. Compilation of th\ database allowed a study of the usefulness of fusing RGB-D and radiometric information obtained with Kinect v2 for fruit detection. Todo so, the signal intensity was range corrected to overcome signal attenuation, obtaining an image that was proportional to the reflectanceof the scene. A registration between RGB, depth and intensity images was then carried out. The Faster R-CNN model was adapted foruse with five-channel input images: color (RGB), depth (D) and range-corrected intensity signal (S). Results show an improvement of4.46\% in F1-score when adding depth and range-corrected intensity channels, obtaining an F1-score of 0.898 and an AP of 94.8\% whenall channels are used. From our experimental results, it can be concluded that the radiometric capabilities of ToF sensors give valuableinformation for fruit detection.

}, keywords = {Agricultural robotics, Convolutional Neural Networks, Fruit detection, Fruit reflectance, Multi-modal faster R-CNN, RGB-D}, doi = {10.1016/j.compag.2019.05.016}, author = {Gen{\'e}-Mola, Jordi and Ver{\'o}nica Vilaplana and Rosell-Polo, Joan R. and Morros, J.R. and Ruiz-Hidalgo, J. and Gregorio, Eduard} } @article {aVentura, title = {Multiresolution co-clustering for uncalibrated multiview segmentation}, journal = {Signal Processing: Image Communication}, year = {2019}, abstract = {

We propose a technique for coherently co-clustering uncalibrated views of a scene\ with a contour-based representation. Our work extends the previous framework,\ an iterative algorithm for segmenting sequences with small variations, where the\ partition solution space is too restrictive for scenarios where consecutive images\ present larger variations. To deal with a more flexible scenario, we present three\ main contributions. First, motion information has been considered both for\ region adjacency and region similarity. Second, a two-step iterative architecture\ is proposed to increase the partition solution space. Third, a feasible global\ optimization that allows to jointly process all the views has been implemented.\ In addition to the previous contributions, which are based on low-level features,\ we have also considered introducing higher level features as semantic information\ in the co-clustering algorithm. We evaluate these techniques on multiview and\ temporal datasets, showing that they outperform state-of-the-art approaches.

}, keywords = {Co-clustering techniques, Image segmentation, Multiview segmentation, Object segmentation}, doi = {10.1016/j.image.2019.04.010}, url = {https://www.sciencedirect.com/science/article/abs/pii/S0923596518302054}, author = {Ventura, C. and David Varas and Ver{\'o}nica Vilaplana and Xavier Gir{\'o}-i-Nieto and Ferran Marques} } @conference {cMas, title = {Picking groups instead of samples: A close look at Static Pool-based Meta-Active Learning}, booktitle = {ICCV Workshop - MDALC 2019}, year = {2019}, month = {10/2019}, address = {Seoul, South Korea}, abstract = {

Active Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of samples from a pool of unsupervised input for further annotation. This scenario is called Static Pool-based Meta-Active Learning. We propose to extend existing approaches by performing the selection in a manner that, unlike previous works, can handle the selection of each sample based on the whole selected subset.

}, doi = {http://dx.doi.org/10.1109/ICCVW.2019.00171}, author = {Mas, Ignasi and Morros, J.R. and Ver{\'o}nica Vilaplana} } @conference {cPareto, title = {Prediction of a second clinical event in CIS patients by combining lesion and brain features}, booktitle = {Congress of the European Comitee for Treatment and Research in Multiple Sclerosis (ECTRIMS 2019)}, year = {2019}, month = {09/2019}, abstract = {

Prediction of a second clinical event in clinically isolated syndrome (CIS) patients, leading to clinically definite multiple sclerosis (CDMS), is still a matter of investigation. The aim of the current study was to predict conversion to CDMS after a first attack, by means of a machine-learning approach and MRI-derived brain features. For that purpose, lesion-based features (volume and distribution within brain lobes) as well as cortical thickness (CT) and deep grey matter volumes (DGMV) were used. The final goal was to determine which features were more frequently found in the classification between converters and non-converters.

}, author = {Deborah Pareto and Pau Vidal and M. Alberich and Carlos Lopez and C. Auger and M. Tintor{\'e} and X. Montalban and J. Sastre-Garriga and Ver{\'o}nica Vilaplana and Alex Rovira} } @article {aPetrone, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using voxelwise analysis of longitudinal structural brain MRI}, journal = {Alzheimer{\textquoteright}s Research \& Therapy}, volume = {11}, year = {2019}, month = {12/2019}, abstract = {

Background: Magnetic resonance imaging (MRI) has unveiled specific alterations at different stages of Alzheimer{\textquoteright}s disease (AD) pathophysiologic continuum constituting what has been established as {\textquoteleft}AD signature{\textquoteright}. To what extent MRI can detect amyloid-related cerebral changes from structural MRI in cognitively unimpaired individuals is still an area open for exploration.

Method: Longitudinal 3D-T1 MRI scans were acquired from a subset of the ADNI cohort comprising 403 subjects: 79 controls (Ctrls), 50 preclinical AD (PreAD), 274 MCI and dementia due to AD (MCI/AD). Amyloid CSF was used as gold-standard measure with established cut-offs (\<192pg/mL) to establish diagnostic categories. Cognitively unimpaired individuals were defined as Ctrls if were amyloid negative and PreAD otherwise. The MCI/AD group was amyloid positive. Only subjects with the same diagnostic category at baseline and follow-up visits were considered for the study. Longitudinal morphometric analysis was performed using SPM12 to calculate Jacobian determinant maps. Statistical analysis was carried out on these jacobian maps to identify structural changes that were significantly different between diagnostic categories. A machine learning classifier was applied on Jacobian determinant maps to predict the presence of abnormal amyloid levels in cognitively unimpaired individuals. The performance of this classifier was evaluated using receiver operating characteristic curve analysis and as a function of the follow-up time between MRI scans. We applied a cost function to assess the benefit of using this classifier in the triaging of individuals in a clinical trial-recruitment setting.

Results: The optimal follow-up time for classification of Ctrls vs PreAD was Δt\>2.5 years and hence, only subjects within this temporal span are used for evaluation (15 Ctrls, 10 PreAD). The longitudinal voxel-based classifier achieved an AUC=0.87 (95\%CI:0.72-0.97). The brain regions that showed the highest discriminative power to detect amyloid abnormalities were the medial, inferior and lateral temporal lobes, precuneus, caudate heads, basal forebrain and lateral ventricles.

Conclusions: Our work supports that machine learning applied to longitudinal brain volumetric changes can be used to predict, with high precision, presence of amyloid abnormalities in cognitively unimpaired subjects. Used as a triaging method to identify a fixed number of amyloid positive individuals, this longitudinal voxelwise classifier is expected to avoid 55\% of unnecessary CSF and/or PET scans and reduce economic cost by 40\%.

}, doi = {https://doi.org/10.1186/s13195-019-0526-8}, url = {https://link.springer.com/article/10.1186/s13195-019-0526-8}, author = {Paula Petrone and Adri{\`a} Casamitjana and Carles Falcon and Miguel Artigues C{\`a}naves and G. Operto and R. Cacciaglia and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cRuiz-Hidalgo19, title = {Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification}, booktitle = {IEEE Conference on Computer Vision Workshop (ICCVW)}, year = {2019}, month = {11/2019}, publisher = {IEEE}, organization = {IEEE}, address = {Seoul, Korea}, abstract = {

Geometric 3D scene classification is a very challenging task. Current methodologies extract the geometric information using only a depth channel provided by an RGBD sensor. These kinds of methodologies introduce possible errors due to missing local geometric context in the depth channel. This work proposes a novel Residual Attention Graph Convolutional Network that exploits the intrinsic geometric context inside a 3D space without using any kind of point features, allowing the use of organized or unorganized 3D data. Experiments are done in NYU Depth v1 and SUN-RGBD datasets to study the different configurations and to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed method outperforms current state-of-the-art in geometric 3D scene classification tasks.\ 

}, doi = {10.1109/ICCVW.2019.00507}, url = {https://imatge-upc.github.io/ragc/}, author = {Mosella-Montoro, Albert and Ruiz-Hidalgo, J.} } @conference {cVenturaa, title = {RVOS: End-to-End Recurrent Network for Video Object Segmentation}, booktitle = {CVPR}, year = {2019}, month = {06/2019}, publisher = {OpenCVF / IEEE}, organization = {OpenCVF / IEEE}, address = {Long Beach, CA, USA}, abstract = {

Multiple object video object segmentation is a challenging task, specially for the zero-shot case, when no object mask is given at the initial frame and the model has to find the objects to be segmented along the sequence. In our work, we propose RVOS, a recurrent network that is fully end-to-end trainable for multiple object video object segmentation, with a recurrence module working on two different domains: (i) the spatial, which allows to discover the different object instances within a frame, and (ii) the temporal, which allows to keep the coherence of the segmented objects along time. We train RVOS for zero-shot video object segmentation and are the first ones to report quantitative results for DAVIS-2017 and YouTube-VOS benchmarks. Further, we adapt RVOS for one-shot video object segmentation by using the masks obtained in previous time-steps as inputs to be processed by the recurrent module. Our model reaches comparable results to state-of-the-art techniques in YouTube-VOS benchmark and outperforms all previous video object segmentation methods not using online learning in the DAVIS-2017 benchmark.

RVOS: End-to-End Recurrent Network for Video Object Segmentation (CVPR 2019) from Universitat Polit{\`e}cnica de Catalunya



}, url = {http://openaccess.thecvf.com/content_CVPR_2019/html/Ventura_RVOS_End-To-End_Recurrent_Network_for_Video_Object_Segmentation_CVPR_2019_paper.html}, author = {Ventura, C. and M{\'\i}riam Bellver and Girbau, A. and Amaia Salvador and Marqu{\'e}s, F. and Xavier Gir{\'o}-i-Nieto} } @article {aCasamitjanaa, title = {Shared latent structures between imaging features and biomarkers in early stages of Alzheimer{\textquoteright}s disease: a predictive study}, journal = {IEEE Journal of Biomedical and Health Informatics}, year = {2019}, month = {08/2019}, abstract = {

Magnetic resonance imaging (MRI) provides high resolution brain morphological information and is used as a biomarker in neurodegenerative diseases. Population studies of brain morphology often seek to identify pathological structural changes related to different diagnostic categories (e.g: controls, mild cognitive impairment or dementia) which normally describe highly heterogeneous groups with a single categorical variable. Instead, multiple biomarkers are used as a proxy for pathology and are more powerful in capturing structural variability. Hence, using the joint modeling of brain morphology and biomarkers, we aim at describing structural changes related to any brain condition by means of few underlying processes. In this regard, we use a multivariate approach based on Projection to Latent Structures in its regression variant (PLSR) to study structural changes related to aging and AD pathology. MRI volumetric and cortical thickness measurements are used for brain morphology and cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau and amyloid-beta) are used as a proxy for AD pathology. By relating both sets of measurements, PLSR finds a low-dimensional latent space describing AD pathological effects on brain structure. The proposed framework allows to separately model aging effects on brain morphology as a confounder variable orthogonal to the pathological effect. The predictive power of the associated latent spaces (i.e. the capacity of predicting biomarker values) is assessed in a cross-validation framework.\ 

}, keywords = {CSF biomarkers, Latent model, MRI, PLS, preclinical AD}, doi = {10.1109/JBHI.2019.2932565}, author = {Adri{\`a} Casamitjana and Paula Petrone and J.L. Molinuevo and Juan D. Gispert and Ver{\'o}nica Vilaplana} } @conference {cLinardosa, title = {Simple vs complex temporal recurrences for video saliency prediction}, booktitle = {British Machine Vision Conference (BMVC)}, year = {2019}, month = {09/2019}, publisher = {British Machine Vision Association}, organization = {British Machine Vision Association}, address = {Cardiff, Wales / UK.}, abstract = {

This paper investigates modifying an existing neural network architecture for static saliency prediction using two types of recurrences that integrate information from the temporal domain. The first modification is the addition of a ConvLSTM within the architecture, while the second is a computationally simple exponential moving average of an internal convolutional state. We use weights pre-trained on the SALICON dataset and fine-tune our model on DHF1K. Our results show that both modifications achieve state-of-the-art results and produce similar saliency maps.

}, url = {https://bmvc2019.org/wp-content/uploads/papers/0952-paper.pdf}, author = {Linardos, Panagiotis and Mohedano, Eva and Nieto, Juan Jos{\'e} and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto and McGuinness, Kevin} } @article {aKuijf, title = {Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge}, journal = {IEEE Transactions on Medical Imaging}, year = {2019}, month = {03/2019}, abstract = {

Quantification of white matter hyperintensities (WMH) of presumed vascular origin is of key\ importance in many neurological research studies. Advanced\ measurements are obtained from manual segmentations on brain\ MR images, which is a laborious procedure. Automatic WMH\ segmentation methods exist, but a standardized comparison of\ such methods is lacking. We organized a scientific challenge, in\ which developers could evaluate their method on a standardized\ multi-center/-scanner image dataset, giving an objective comparison:\ the WMH Segmentation Challenge (http://wmh.isi.uu.nl/).\ Sixty T1+FLAIR images from three MR scanners were released\ with manual WMH segmentations. A secret test set of 110\ images from five MR scanners was used for evaluation. Methods\ had to be containerized and submitted to the challenge organizers.Five evaluation metrics were used to rank the methods:\ (1) Dice Similarity Coefficient, (2) modified Hausdorff distance\ (95th percentile), (3) absolute percentage volume difference, (4)\ sensitivity for detecting individual lesions, and (5) F1-score for\ individual lesions. Additionally, methods were ranked on their\ inter-scanner robustness.\ Twenty participants submitted their method for evaluation.\ This paper provides a detailed analysis of the results. In brief,there is a cluster of four methods that rank significantly better\ than the other methods. There is one clear winner, which also\ has the best inter-scanner robustness.\ The challenge remains open for future submissions and provides\ a public platform for method evaluation.

}, keywords = {brain, Evaluation and performance, Magnetic resonance imaging (MRI), segmentation}, issn = {0278-0062}, doi = {10.1109/TMI.2019.2905770}, author = {Hugo Kuijf and Matthijs Biesbroek and Jeroen de Bresser and Rutger Heinen and Simon Andermatt and Mariana Bento and Matt Berseth and Mikhail Belyaev and Jorge Cardoso and Adri{\`a} Casamitjana and Louis Collins and Mahsa Dadar and Achileas Georgiou and Mohsen Ghafoorian and Dakai Jin and April Khademi and Jesse Knight and Hongwei Li and Xavier Llado and Miguel Luna and Qaiser Mahmood and Richard McKinley and Alireza Mehrtash and Sebastien Ourselin and Bo-yong Park and Hyunkin Park and Sang Hyun Park and Simon Pezold and Elodie Puybareau and Leticia Rittner and Carole Sudre and Sergi Valverde and Ver{\'o}nica Vilaplana and Rolan Wiest and Yongchao Xu and Ziyue Xu and Guodong Zeng and Jianguo Zhang and Guoyan Zheng and Christoper Chen and Wiesje van der Flier and Frederik Barkhof and Max Viergever and Geert Jan Biessels} } @inbook {bBellot19, title = {Unsupervised GRN Ensemble}, booktitle = {Sanguinetti G., Huynh-Thu V. (eds) Methods in Molecular Biology }, volume = {1883}, year = {2019}, pages = {283-302}, publisher = { Springer science, Humana Press}, organization = { Springer science, Humana Press}, address = {New York, NY}, issn = {978-1-4939-8881-5}, doi = {https://doi-org.recursos.biblioteca.upc.edu/10.1007/978-1-4939-8882-2}, author = {P. Bellot and Salembier, P. and Pham, N.C. and Meyer, P. E.} } @conference {cGene-Mola19, title = {Uso de redes neuronales convolucionales para la detecci{\'o}n remota de frutos con c{\'a}maras RGB-D}, booktitle = {Congreso Ib{\'e}rico de Agroingenier{\'\i}a}, year = {2019}, month = {09/2019}, publisher = { Universidad de Zaragoza (UZA)}, organization = { Universidad de Zaragoza (UZA)}, address = {Huesca}, abstract = {

La detecci{\'o}n remota de frutos ser{\'a} una herramienta indispensable para la gesti{\'o}n agron{\'o}mica optimizada y sostenible de las plantaciones frut{\'\i}colas del futuro, con aplicaciones en previsi{\'o}n de cosecha, robotizaci{\'o}n de la recolecci{\'o}n y elaboraci{\'o}n de mapas de producci{\'o}n. Este trabajo propone el uso de c{\'a}maras de profundidad RGB-D para la detecci{\'o}n y la posterior localizaci{\'o}n 3D de los frutos. El material utilizado para la adquisici{\'o}n de datos consiste en una plataforma terrestre autopropulsada equipada con dos sensores Kinect v2 de Microsoft y un sistema de posicionamiento RTK-GNSS. Con este equipo se escanearon 3 filas de manzanos Fuji de una explotaci{\'o}n comercial. El conjunto de datos adquiridos est{\'a} compuesto por 110 capturas que contienen un total de 12,838 manzanas Fuji. La detecci{\'o}n de frutos se realiz{\'o} mediante los datos RGB (im{\'a}genes de color proporcionadas por el sensor). Para ello, se implement{\'o} y se entren{\'o} la red neuronal convolucional de detecci{\'o}n de objetos Faster R-CNN, la cual est{\'a} compuesta por dos m{\'o}dulos: red de propuesta de regiones de inter{\'e}s y red de clasificaci{\'o}n. Ambos m{\'o}dulos comparten las primeras capas convolucionales siguiendo el modelo VGG-16 pre-entrenado con la base de datos ImageNet. Los resultados de test muestran un porcentaje de detecci{\'o}n del 91.4\% de los frutos con un 15.9\% de falsos positivos (F1-score = 0.876). La evaluaci{\'o}n cualitativa de las detecciones muestra que los falsos positivos corresponden a zonas de la imagen que presentan un patr{\'o}n muy similar a una manzana, donde, incluso a percepci{\'o}n del ojo humano, es dif{\'\i}cil de determinar si hay o no manzana. Por otro lado, las manzanas no detectadas corresponden a aquellas que estaban ocultas casi en su totalidad por otros {\'o}rganos vegetativos (hojas o ramas) o a manzanas cortadas por los m{\'a}rgenes de la imagen. De los resultados experimentales se concluye que el sensor Kinect v2 tiene un gran potencial para la detecci{\'o}n y localizaci{\'o}n 3D de frutos. La principal limitaci{\'o}n del sistema es que el rendimiento del sensor de profundidad se ve afectado en condiciones de alta iluminaci{\'o}n

}, keywords = {C{\'a}maras de profundidad, Detecci{\'o}n de frutos, Redes neuronales convolucionales, RGB-D, Rob{\'o}tica agr{\'\i}cola}, doi = {https://doi.org/10.26754/c_agroing.2019.com.3325}, author = {Gen{\'e}-Mola, Jordi and Ver{\'o}nica Vilaplana and Rosell-Polo, Joan R. and Morros, J.R. and Ruiz-Hidalgo, J. and Gregorio, Eduard} } @mastersthesis {xNieto, title = {Video Saliency Prediction with Deep Neural Networks}, year = {2019}, abstract = {

Saliency prediction is a topic undergoing intense study in computer vision with a broad range of applications. It consists in predicting where the attention is going to be received in an image or a video by a human. Our work is based on a deep neural network named SalGAN, which was trained on a saliency annotated dataset of static images. In this thesis we investigate different approaches for extending SalGAN to the video domain. To this end, we investigate the recently proposed saliency annotated video dataset DHF1K to train and evaluate our models. The obtained results indicate that techniques such as depth estimation or coordconv can effectively be used as additional modalities to enhance the saliency prediction of static images obtained with SalGAN, achieving encouraging results in the DHF1K benchmark. Our work is based on pytorch and it is publicly available here.

Video Saliency Prediction with Deep Neural Networks - Juan Jose Nieto - DCU 2019 from Universitat Polit{\`e}cnica de Catalunya
}, author = {Nieto, Juan Jos{\'e}}, editor = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @conference {cDuartea, title = {Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks}, booktitle = {ICASSP}, year = {2019}, month = {05/2019}, publisher = {IEEE}, organization = {IEEE}, address = {Brighton, UK}, abstract = {

Speech is a rich biometric signal that contains information\ about the identity, gender and emotional state of the speaker.\ In this work, we explore its potential to generate face images\ of a speaker by conditioning a Generative Adversarial\ Network (GAN) with raw speech input. We propose a deep\ neural network that is trained from scratch in an end-to-end\ fashion, generating a face directly from the raw speech waveform\ without any additional identity information (e.g reference\ image or one-hot encoding). Our model is trained in a\ self-supervised fashion by exploiting the audio and visual signals\ naturally aligned in videos. With the purpose of training\ from video data, we present a novel dataset collected for this\ work, with high-quality videos of ten youtubers with notable\ expressiveness in both the speech and visual signals.

}, doi = {10.1109/ICASSP.2019.8682970}, url = {http://hdl.handle.net/2117/167073}, author = {Amanda Duarte and Rold{\'a}n, Francisco and Tubau, Miquel and Escur, Janna and Pascual-deLaPuente, Santiago and Amaia Salvador and Mohedano, Eva and McGuinness, Kevin and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cXu, title = {Action Tube Extraction based 3D -CNN for RGB-D Action Recognition}, booktitle = {International Conference on Content-Based Multimedia Indexing CBMI 2018}, year = {2018}, month = {09/2018}, abstract = {

In this paper we propose a novel action tube extractor for RGB-D action recognition in trimmed videos. The action tube extractor takes as input a video and outputs an action

tube. The method consists of two parts: spatial tube extraction and temporal sampling. The first part is built upon MobileNet-SSD and its role is to define the spatial region where the action takes place. The second part is based on the structural similarity index (SSIM) and is designed to remove frames without obvious motion from the primary action tube. The final extracted action tube has two benefits: 1) a higher ratio of ROI (subjects of action) to background; 2) most frames contain obvious motion change. We propose to use a two-stream (RGB and Depth) I3D architecture as our 3D-CNN model. Our approach outperforms the state-of-the-art methods on the OA and NTU RGB-D datasets.

}, doi = {https://doi.org/10.1109/CBMI.2018.8516450}, author = {Zineng Xu and Ver{\'o}nica Vilaplana and Morros, J.R.} } @mastersthesis {xArtigot18, title = {Automatic fruit classification using deep learning}, year = {2018}, abstract = {

To achieve greater efficiency at harvesting tasks, the mechanization of such task is unavoidable. Apart from the mechanical aspects, the harvesting systems needs software that can locate the fruit to be harvested.

The use of machine learning and deep learning techniques to achieve such software was studied in this thesis. The results showed that an accuracy similar to other studies is feasible with a limited number of training samples using deep learning techniques.

From this thesis we conclude that the mechanization of the harvesting labour is possible, at least from the software point of view, while the crop estimation application may need some more work before being feasible.

}, author = {Joaquim Mart{\'\i}nez Artigot}, editor = {Josep Ramon Morros and Ver{\'o}nica Vilaplana} } @conference {cPetrone, title = {Characteristic Brain Volumetric Changes in the AD Preclinical Signature}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2018}, month = {07/2018}, address = {Chicago, USA}, abstract = {

In the last decade, magnetic resonance imaging has unveiled specific AD alterations at different stages of the AD pathophysiologic continuum that conform what has been established as the AD signature. To which extent MRI can detect structural changes at the preclinical asymptomatic stage of AD - the preclinical AD signature- is still an area open for exploration. Our work supports the idea that there are brain volumetric changes specific to preclinical AD subjects and defines the preclinical AD signature based on longitudinal data. While some regions show a pattern of atrophy that overlaps with the AD signature, other specific regions exhibit changes that are unique to this early asymptomatic AD stage.

}, author = {P. Petrone and Adri{\`a} Casamitjana and C. Falcon and M. Artigues and G. Operto and S. Skouras and R. Cacciaglia and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @article {aPetrone18, title = {Characteristic Brain Volumetric Changes in the AD Preclinical Signature.}, journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association}, volume = {14}, year = {2018}, month = {07/2018}, pages = {P1235}, doi = {10.1016/j.jalz.2018.06.1737}, author = {P. Petrone and Adri{\`a} Casamitjana and C. Falcon and M. Artigues and G. Operto and S. Skouras and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @conference {cGomez, title = {Demonstration of an Open Source Framework for Qualitative Evaluation of CBIR Systems}, booktitle = {ACM Multimedia}, year = {2018}, month = {10/2018}, publisher = {ACM}, organization = {ACM}, address = {Seoul, South Korea}, abstract = {

Evaluating image retrieval systems in a quantitative way, for example by computing measures like mean average precision, allows for objective comparisons with a ground-truth. However, in cases where ground-truth is not\  available, the only alternative is to collect feedback from a user. Thus, qualitative assessments become important to better understand how the system works. Visualizing the results could be, in some scenarios, the only way to evaluate the results obtained and also the only opportunity to identify that a system is failing. This necessitates developing a User Interface (UI) for a Content Based Image Retrieval (CBIR) system that allows visualization of results and improvement via capturing user relevance feedback. A well-designed UI facilitates understanding of the performance of the system, both in cases where it works well and perhaps more importantly those which highlight the need for improvement. Our open-source system implements three components to facilitate researchers to quickly develop these capabilities for their retrieval engine. We present: a web-based user interface to visualize retrieval results and collect user annotations; a server that simplifies\  connection with any underlying CBIR system; and a server that manages the search engine data.\ 

User Interface for an Image Retrieval Engine System from Universitat Polit{\`e}cnica de Catalunya
}, doi = {10.1145/3240508.3241395}, url = {https://dl.acm.org/citation.cfm?id=3241395}, author = {Gomez, Paula and Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @unpublished {xMoreno18, title = {English to ASL Translator for Speech2Signs}, year = {2018}, month = {01/2018}, type = {Internal report}, abstract = {

This paper illustrates the work around the English - American Signs Language (ASL) data generation for the speech2signs system that is devoted to the generation of a signs language interpreter. The current work will be, first, an approximation to the speech2signs system and, second, a video-to-video corpus generator for an end-to-end approximation of speech2signs. In order to generate the desired corpus data, the Google Transformer (a Neural Machine Translation system based completely on attention) will be trained to translate from English to ASL. The dataset used to train the Transformer is the ASLG-PC12.

}, author = {Moreno, Daniel and Costa-juss{\`a}, Marta R. and Xavier Gir{\'o}-i-Nieto} } @conference {cGene-Mola18, title = {Fruit Detection Using Mobile Terrestrial Laser Scanning}, booktitle = {AgEng 2018,}, year = {2018}, month = {07/2018}, address = {Wageningen (Netherlands)}, abstract = {

The development of reliable fruit detection and localization systems is essential for future sustainable agronomic management of high-value crops. Up to date, most proposed systems on fruit detection and characterization are based on RGB cameras and thus affected by intrinsic constraints, such as variable lighting conditions and camera calibration. This work presents a new technique that uses a mobile terrestrial laser scanner to detect and localize fruits regardless of the prevailing lighting conditions and without the need of a previous calibration. An experimental test focused on two Fuji apple trees (containing 139 and 145 apples each) was carried out. A 3D point cloud of this scene was generated using a Velodyne VLP-16 LiDAR sensor synchronized with a RTK-GNSS receiver. A reflectivity analysis of tree elements was performed, obtaining mean reflectivity values of 28.9\%, 29.1\%, and 44.3\% for leaves, trunks, and fruits, respectively. These results suggest that the reflectivity parameter can be useful to localize fruits in the tree. From this knowledge, a three-step fruit detection algorithm has been developed: 1) reflectivity thresholding to remove most of the leaves and trunks from the original point cloud; 2) statistical outlier removal to reduce noise; 3) connected components clustering using a density-based algorithm. By applying this algorithm to our dataset, a localization success of 85\%, a detachment success of 78.8\%, and a false detection rate of 15.2\% were obtained. These detection rates are similar to those obtained by current RGB-based system, but with the additional advantage of providing direct 3D fruit location information (global coordinates) which is not affected by sunlight variations. It can be concluded that LiDAR technology and, particularly, its reflectivity information, might have potential use in fruit detection. Future work should include the application of this fruit detection technique on a wider range of crop types

}, author = {Gen{\'e}-Mola, Jordi and Gregorio, Eduard and Guevara, Javier and Auat, Fernando and Escol{\`a}, Alexandre and Morros, J.R. and Rosell-Polo, Joan R.} } @mastersthesis {xColl-Pol, title = {The Importance of Time in Visual Attention Models}, year = {2018}, abstract = {

Predicting visual attention is a very active field in the computer vision community. Visual attention is a mechanism of the visual system that can select relevant areas within a scene. Models for saliency prediction are intended to automatically predict which regions are likely to be attended by a human observer. Traditionally, ground truth saliency maps are built using only the spatial position of the fixation points, being these xation points the locations where an observer fixates the gaze when viewing a scene. In this work we explore encoding the temporal information as well, and assess it in the application of prediction saliency maps with deep neural networks. It has been observed that the later fi xations in a scanpath are usually selected randomly during visualization, specially in those images with few regions of interest. Therefore, computer vision models have dificulties learning to predict them. In this work, we explore a temporal weighting over the saliency maps to better cope with this random behaviour. The newly proposed saliency representation assigns di erent weights depending on the position in the sequence of gaze fixations, giving more importance to early timesteps than later ones. We used this maps to train MLNet, a state of the art for predicting saliency maps. MLNet predictions were evaluated and compared to the results obtained when the model has been trained using traditional saliency maps. Finally, we show how the temporally weighted saliency maps brought some improvement when used to weight the visual features in an image retrieval task.

The Importance of Time in Visual Attention Models from Universitat Polit{\`e}cnica de Catalunya
}, author = {Coll-Pol, Marta}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @article {aCasamitjana, title = {MRI-Based Screening of Preclinical Alzheimer{\textquoteright}s Disease for Prevention Clinical Trials}, journal = {Journal of Alzheimer{\textquoteright}s Disease}, volume = {64}, year = {2018}, month = {07/2018}, chapter = {1099}, abstract = {

The identification of healthy individuals harboring amyloid pathology constitutes one important challenge for secondary prevention clinical trials in Alzheimer{\textquoteright}s disease (AD). Consequently, noninvasive and cost-efficient techniques to detect preclinical AD constitute an unmet need of critical importance. In this manuscript, we apply machine learning to structural MRI (T1 and DTI) of 96 cognitively normal subjects to identify amyloid-positive ones. Models were trained on public ADNI data and validated on an independent local cohort. Used for subject classification in a simulated clinical trial setting, the proposed method is able to save 60\% unnecessary CSF/PET tests and to reduce 47\% of the cost of recruitment when used in a simulated clinical trial setting. This recruitment strategy capitalizes on already acquired MRIs to reduce the overall amount of invasive PET/CSF tests in prevention trials, demonstrating a potential value as a tool for AD screening. This protocol could foster the development of secondary prevention strategies for AD.

}, author = {Adri{\`a} Casamitjana and Paula Petrone and Alan Tucholka and Carles Falcon and Stavros Skouras and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cShou, title = {Online Action Detection in Untrimmed, Streaming Videos}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2018}, month = {02/2018}, address = {Munich, German}, abstract = {

}, url = {https://arxiv.org/abs/1802.06822}, author = {Shou, Zheng and Pan, Junting and Chan, Johnatan and Miyazawa, Kazuyuki and Mansour, Hassan and Vetro, Anthony and Xavier Gir{\'o}-i-Nieto and Chang, Shih-Fu} } @conference {cLopez-Palma, title = {Oriented trajectories as a method for audience measurement}, booktitle = {27th International Symposium on Industrial Electronics (ISIE)}, year = {2018}, month = {06/2018}, publisher = {IEEE}, organization = {IEEE}, address = {Cairns, Australia}, abstract = {

The quantification of the attention received by advertisements is of paramount importance to determine its effectiveness. In this work, a simple and effective objective method for the assessment of the attention given to advertisements is provided. The proposed method is based on computing the oriented trajectory of the different test persons along with their head pose. This way, it is possible to determine if a given person is looking towards the advertisement. While other similar methods use more complex setups, requiring a camera at each advertisement location, our method needs only a single (or a few) ceiling camera. Even though the apparent simplicity, the method can compute attention measures at each point of the scene.

}, doi = {10.1109/ISIE.2018.8433615}, author = {Manuel L{\'o}pez-Palma and Morros, J.R. and Javier Gago and Montserrat Corbal{\'a}n} } @conference {cAssensa, title = {PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks}, booktitle = {ECCV 2018 Workshop on Egocentric Perception, Interaction and Compution (EPIC)}, year = {2018}, month = {07/2018}, publisher = {Springer}, organization = {Springer}, address = {Munich, Germany}, abstract = {

We introduce PathGAN, a deep neural network for visual scanpath prediction trained on adversarial examples. A visual scanpath is defined as the sequence of fixation points over an image defined by a human observer with its gaze. PathGAN is composed of two parts, the generator and the discriminator. Both parts extract features from images using off-the-shelf networks, and train recurrent layers to generate or discriminate scanpaths accordingly. In scanpath prediction, the stochastic nature of the data makes it very difficult to generate realistic predictions using supervised learning strategies, but we adopt adversarial training as a suitable alternative. Our experiments prove how PathGAN improves the state of the art of visual scanpath prediction on the Salient360! dataset.

This work obtained the\ 2nd award in Prediction of Head-gaze Scan-paths for Images, and the 2nd award in Prediction of Eye-gaze Scan-paths for Images at the IEEE ICME 2018 Salient360! Challenge.

}, doi = {10.1007/978-3-030-11021-5_25}, url = {https://doi.org/10.1007/978-3-030-11021-5_25}, author = {Assens, Marc and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @article {aCasamitjana18, title = { Projection to Latent Spaces Disentangles Specific Cerebral Morphometric Patterns Associated to Aging and Preclinical AD}, journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association }, volume = {14}, year = {2018}, month = {07/2018}, pages = {P869-P870}, doi = {10.1016/j.jalz.2018.06.1111}, author = {Adri{\`a} Casamitjana and P. Petrone and M. Artigues and J.L. Molinuevo and J.D. Gispert and Ver{\'o}nica Vilaplana} } @conference {cCasamitjanab, title = {Projection to Latent Spaces Disentangles Specific Cerebral Morphometric Patterns Associated to Aging and Preclinical AD}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2018}, month = {07/2018}, address = {Chicago, USA}, abstract = {

Partial Least Squares (PLS) is a mathematical technique that relates two sets of observable variables by means of a few latent explanatory factors. The aim of this study is to use PLS to discover the associations between CSF biomarkers and structural brain imaging in preclinical AD and to disentangle their specific contribution from confounding demographic factors.PLS is able to disentangle the cerebral morphometric patterns associated to preclinical AD stages from other demographic factors. Results with both cortical thickness and volumetric data present significant overlap, thus showing the robustness of this approach. Interestingly, volumetric data showed more significant correlations with CSF Abeta than cortical thickness.

}, author = {Adri{\`a} Casamitjana and P. Petrone and M. Artigues and J.L. Molinuevo and J.D. Gispert and Ver{\'o}nica Vilaplana} } @conference {cSalvadord, title = {Recurrent Neural Networks for Semantic Instance Segmentation}, booktitle = {ECCV 2018 Women in Computer Vision (WiCV) Workshop}, year = {2018}, month = {12/2017}, abstract = {

We present a recurrent model for semantic instance segmentation that sequentially generates pairs of masks and their associated class probabilities for every object in an image. Our proposed system is trainable end-to-end, does not require post-processing steps on its output and is conceptually simpler than current methods relying on object proposals. We observe that our model learns to follow a consistent pattern to generate object sequences, which correlates with the activations learned in the encoder part of our network. We achieve competitive results on three different instance segmentation benchmarks (Pascal VOC 2012, Cityscapes and CVPPP Plant Leaf Segmentation).

Recurrent Neural Networks for Semantic Instance Segmentation from Universitat Polit{\`e}cnica de Catalunya
}, url = {https://imatge-upc.github.io/rsis/}, author = {Amaia Salvador and M{\'\i}riam Bellver and Baradad, Manel and V{\'\i}ctor Campos and Marqu{\'e}s, F. and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cSalvadore, title = {Recurrent Neural Networks for Semantic Instance Segmentation}, booktitle = {CVPR 2018 DeepVision Workshop}, year = {2018}, month = {06/2018}, abstract = {

We present a recurrent model for semantic instance segmentation that sequentially generates binary masks and their associated class probabilities for every object in an image. Our proposed system is trainable end-to-end from an input image to a sequence of labeled masks and, compared to methods relying on object proposals, does not require post-processing steps on its output. We study the suitability of our recurrent model on three different instance segmentation benchmarks, namely Pascal VOC 2012, CVPPP Plant Leaf Segmentation and Cityscapes. Further, we analyze the object sorting patterns generated by our model and observe that it learns to follow a consistent pattern, which correlates with the activations learned in the encoder part of our network.

}, author = {Amaia Salvador and M{\'\i}riam Bellver and Baradad, Manel and V{\'\i}ctor Campos and Marqu{\'e}s, F. and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cMohedanob, title = {Saliency Weighted Convolutional Features for Instance Search}, booktitle = {Content-Based Multimedia Indexing - CBMI}, year = {2018}, month = {09/2018}, publisher = {IEEE}, organization = {IEEE}, address = {La Rochelle, France}, abstract = {

This work explores attention models to weight the contribution of local convolutional representations for the instance search task. We present a retrieval framework based on bags of local convolutional features (BLCF) that benefits from saliency weighting to build an efficient image representation. The use of human visual attention models (saliency) allows significant improvements in retrieval performance without the need to conduct region analysis or spatial verification, and without requiring any feature fine tuning. We investigate the impact of different saliency models, finding that higher performance on saliency benchmarks does not necessarily equate to improved performance when used in instance search tasks. The proposed approach outperforms the state-of-the-art on the challenging INSTRE benchmark by a large margin, and provides similar performance on the Oxford and Paris benchmarks compared to more complex methods that use off-the-shelf representations.

Saliency Weighted Convolutional Features for Instance Search from Universitat Polit{\`e}cnica de Catalunya
}, author = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @article {aAssens, title = {Scanpath and Saliency Prediction on 360 Degree Images}, journal = {Elsevier Signal Processing: Image Communication}, year = {2018}, abstract = {

We introduce deep neural networks for scanpath and saliency prediction trained on 360-degree images. The scanpath prediction model called SaltiNet is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation using a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. We also show how a similar architecture achieves state-of-the-art performance for the related task of saliency map prediction. Our source code and trained models available here.

}, url = {https://www.sciencedirect.com/science/article/pii/S0923596518306209}, author = {Assens, Marc and McGuinness, Kevin and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto} } @inbook {bCasamitjana18a, title = {Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer{\textquoteright}s Disease}, booktitle = {PRedictive Intelligence in MEdicine}, volume = {11121}, year = {2018}, pages = {60-67}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

In this work, we identify meaningful latent patterns in MR images for patients across the Alzheimer{\textquoteright}s disease (AD) continuum. For this purpose, we apply Projection to Latent Structures (PLS) method using cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau, amyloid-beta) and age as response variables and imaging features as explanatory variables. Freesurfer pipeline is used to compute MRI surface and volumetric features resulting in 68 cortical ROIs and 84 cortical and subcortical ROIs, respectively. The main assumption of this work is that there are two main underlying processes governing brain morphology along the AD continuum: brain aging and dementia. We use two different and orthogonal PLS models to describe each process: PLS-aging and PLS-dementia. To define PLS-aging model we use normal aging subjects and age as predictor and response variables, respectively, while for PLS-dementia we only use demented subjects and biomarkers as response variables.

}, issn = {978-3-030-00320-3}, doi = {10.1007/978-3-030-00320-3}, author = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Paula Petrone and Jose Luis Molinuevo and Juan D. Gispert} } @conference {cCasamitjanac, title = {Shared latent structures between imaging features and biomarkers in early stages of Alzheimer{\textquoteright}s disease}, booktitle = {Workshop on Predictive Intelligence in Medicine (PRIME), MICCAI}, year = {2018}, month = {2018}, address = {Granada, Spain}, abstract = {

In this work, we identify meaningful latent patterns in MR images for patients across the Alzheimer{\textquoteright}s disease (AD) continuum. For this purpose, we apply Projection to Latent Structures (PLS) method using cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau, amyloid-beta) and age as response variables and imaging features as explanatory variables. Freesurfer pipeline is used to compute MRI surface and volumetric features\  \ resulting in 68 cortical ROIs and 84 cortical and subcortical ROIs, respectively. The main assumption of this work is that there are two main underlying processes governing brain morphology along the AD continuum: brain aging and dementia. We use two different and orthogonal PLS models to describe each process: PLS-aging and PLS-dementia. To define PLS-aging model we use normal aging subjects and age as predictor and response variables, respectively, while for PLS-dementia we only use demented subjects and biomarkers as response variables.\ 

}, author = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Paula Petrone and Jose Luis Molinuevo and Juan D. Gispert} } @mastersthesis {xRoldana, title = {Speech-conditioned Face Generation with Deep Adversarial Networks}, year = {2018}, abstract = {

Image synthesis have been a trending task for the AI community in recent years. Many works have shown the potential of Generative Adversarial Networks (GANs) to deal with tasks such as text or audio to image synthesis. In particular, recent advances in deep learning using audio have inspired many works involving both visual and auditory information. In this work we propose a face synthesis method using audio and/or language representations as inputs. Furthermore, a dataset which relates speech utterances with a face and an identity has been built, fitting for other tasks apart from face synthesis such as speaker recognition or voice conversion.

Speech Conditioned Face Generation with Deep Adversarial Networks from Universitat Polit{\`e}cnica de Catalunya
}, author = {Rold{\'a}n, Francisco}, editor = {Pascual-deLaPuente, Santiago and Amaia Salvador and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @conference {cLinardos, title = {Temporal Saliency Adaptation in Egocentric Videos}, booktitle = {ECCV 2018 Workshop on Egocentric Perception, Interaction and Computing}, year = {2018}, month = {07/2018}, publisher = {Extended abstract}, organization = {Extended abstract}, address = {Munich, Germany}, abstract = {

}, author = {Linardos, Panagiotis and Mohedano, Eva and Chert{\'o}, M{\`o}nica and Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @conference {cIndia18, title = {UPC Multimodal Speaker Diarization System for the 2018 Albayzin Challenge}, booktitle = {IberSpeech 2018}, year = {2018}, month = {11/2018}, address = {Barcelona}, abstract = {

This paper presents the UPC system proposed for the Multimodal Speaker Diarization task of the 2018 Albayzin Challenge. This approach works by processing individually the speech and the image signal. In the speech domain, speaker diarization is performed using identity embeddings created by a triplet loss DNN that uses i-vectors as input. The triplet DNN is trained with an additional regularization loss that minimizes the variance of both positive and negative distances. A sliding window is then used to compare speech segments with enrollment speaker targets using cosine distance between the embeddings. To detect identities from the face modality, a face detector followed by a face tracker has been used on the videos. For each cropped face a feature vector is obtained using a Deep Neural Network based on the ResNet 34 architecture, trained using a\  metric learning triplet loss (available from dlib library). For each track the face feature vector is obtained by averaging the features obtained for each one of the frames of that track. Then, this feature vector is compared with the features extracted from the images of the enrollment identities. The proposed system is evaluated on the RTVE2018 database.

}, keywords = {Face Diarization, Multimodal Systems, Speaker Diarization}, doi = {http://dx.doi.org/10.21437/IberSPEECH.2018-40}, author = {India, M. and Sagastiberri, I. and Palau, P. and Elisa Sayrol and Morros, J.R. and Hernando, J.} } @conference {cLopez-Palmaa, title = {Who watches the watchers? Quality control of the human inspection in production lines using Visual Intensity of Attention}, booktitle = {SAAEI 2018}, year = {2018}, month = {07/2018}, address = {Barcelona}, abstract = {

On multiple occasions production lines require inspectors, human operators that visualize certain steps of the production and determine the quality of the resulting products. However, inspectors are subject to errors. We propose a method based on computer vision to decide if the inspector has used an adequate attention in the different points of inspection so that pieces that have not been verified can be marked for rejection or re-inspection. The method uses a top-view ceiling camera that computes the trajectories and areas of vision of the inspector, and determines which products have received the correct amount of attention. The resulting attention figure can be compared with the acceptance range in the inspection protocol to determine if the inspection is valid.

}, author = {Manuel L{\'o}pez-Palma and Morros, J.R. and Javier Gago and Montserrat Corbal{\'a}n} } @article {aMaceiraa, title = {3D hierarchical optimization for multi-view depth map coding}, journal = {Multimedia Tools and Applications}, year = {2017}, month = {12/2017}, abstract = {

Full version available at:\ http://rdcu.be/zHtU

Depth data has a widespread use since the popularity of high resolution 3D sensors. In multi-view sequences, depth information is used to supplement the color data of each view. This article proposes a joint encoding of multiple depth maps with a unique representation. Color and depth images of each view are segmented independently and combined in an optimal Rate-Distortion fashion. The resulting partitions are projected to a reference view where a coherent hierarchy forthe multiple views is built. A Rate-Distortion optimization is applied to obtain the final segmentation choosing nodes of the hierarchy. The consistent segmentation is used to robustly encode depth maps of multiple views obtaining competitive results with HEVC coding standards.

}, keywords = {3D representation, Depth coding, Multiview coding, segmentation based coding}, doi = {10.1007/s11042-017-5409-z}, url = {http://rdcu.be/zHtU}, author = {Maceira, M. and David Varas and Morros, J.R. and Ruiz-Hidalgo, J. and Marqu{\'e}s, F.} } @article {aMartinez17, title = {A closed-loop approach for tracking a humanoid robot using particle filtering and depth data}, journal = {Intelligent Service Robotics}, volume = {10}, year = {2017}, month = {10/2017}, pages = {297{\textendash}312}, abstract = {

Humanoid robots introduce instabilities during biped march that complicate the process of estimating their position and orientation along time. Tracking humanoid robots may be useful not only in typical applications such as navigation, but in tasks that require benchmarking the multiple processes that involve registering measures about the performance of the humanoid during walking. Small robots represent an additional challenge due to their size and mechanic limitations which may generate unstable swinging while walking. This paper presents a strategy for the active localization of a humanoid robot in environments that are monitored by external devices. The problem is faced using a particle filter method over depth images captured by an RGB-D sensor in order to effectively track the position and orientation of the robot during its march. The tracking stage is coupled with a locomotion system controlling the stepping of the robot toward a given oriented target. We present an integral communication framework between the tracking and the locomotion control of the robot based on the robot operating system, which is capable of achieving real-time locomotion tasks using a NAO humanoid robot.

}, keywords = {Humanoid robot, ~Particle Filter, ~RGB-D sensor, ~ROS, ~Tracking}, issn = {1861-2776}, doi = {10.1007/s11370-017-0230-0}, url = {http://rdcu.be/s1RE}, author = {Mart{\'\i}nez, P. A. and X. Lin and Castel{\'a}n, M and Casas, J. and Arechavaleta, G.} } @conference {cBellvera, title = {Detection-aided liver lesion segmentation using deep learning}, booktitle = {ML4H: Machine Learning for Health Workshop at NIPS 2017}, year = {2017}, month = {11/2017}, abstract = {

A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the according treatments. In this work we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans using Convolutional Neural Networks (CNNs), that have proven good results in a variety of computer vision tasks, including medical imaging. The network that segments the lesions consists of a cascaded architecture, which first focuses on the region of the liver in order to segment the lesions on it. Moreover, we train a detector to localize the lesions, and mask the results of the segmentation network with the positive detections. The segmentation architecture is based on DRIU, a Fully Convolutional Network (FCN) with side outputs that work on feature maps of different resolutions, to finally\  benefit from the multi-scale information learned by different stages of the network. The main contribution of this work is the use of a detector to localize the lesions, which we show to be beneficial to remove false positives triggered by the segmentation network.

Detection-aided liver lesion segmentation using deep learning from Xavier Giro-i-Nieto
}, author = {M{\'\i}riam Bellver and Kevis-Kokitsi Maninis and Jordi Pont-Tuset and Jordi Torres and Xavier Gir{\'o}-i-Nieto and Luc van Gool} } @mastersthesis {xBellvera, title = {Detection-aided medical image segmentation using deep learning}, year = {2017}, abstract = {

Program:\ Master{\textquoteright}s Degree in Telecommunications Engineering

Grade: A with honours (10.0/10.0)

A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and also to assess the response to the according treatments. In this thesis we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans, as well as other anatomical structures and organs of the human body. We have used Convolutional Neural Networks (CNNs), that have proven good results in a variety of tasks, including medical imaging. The network to segment the lesions consists of a cascaded architecture, which first focuses on the liver region in order to segment the lesion. Moreover, we train a detector to localize the lesions and just keep those pixels from the output of the segmentation network where a lesion is detected. The segmentation architecture is based on DRIU [24], a Fully Convolutional Network (FCN) with side outputs that work at feature maps of different resolutions, to finally benefit from the multi-scale information learned by different stages of the network. Our pipeline is 2.5D, as the input of the network is a stack of consecutive slices of the CT scans. We also study different methods to benefit from the liver segmentation in order to delineate the lesion. The main focus of this work is to use the detector to localize the lesions, as we demonstrate that it helps to remove false positives triggered by the segmentation network. The benefits of using a detector on top of the segmentation is that the detector acquires a more global insight of the healthiness of a liver tissue compared to the segmentation network, whose final output is pixel-wise and is not forced to take a global decision over a whole liver patch. We show experiments with the LiTS dataset for the lesion and liver segmentation. In order to prove the generality of the segmentation network, we also segment several anatomical structures from the Visceral dataset.

Detection-aided liver lesion segmentation using deep learning from Xavier Giro-i-Nieto
}, author = {M{\'\i}riam Bellver}, editor = {Kevis-Kokitsi Maninis and Jordi Pont-Tuset and Luc van Gool and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @inbook {bBellot17, title = {Efficient Combination of Pairwise Feature Networks}, booktitle = {Neural Connectomics Challenge}, year = {2017}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, chapter = {7}, issn = {978-3-319-53069-7}, doi = {10.1007/978-3-319-53070-3}, author = {P Bellot and Patrick Meyer}, editor = {Battaglia, D and Guyon, I. and Lemaire, V. and Orlandi, J. and Ray, B. and Soriano, J.} } @unpublished {xMohedanoa, title = {Fine-tuning of CNN models for Instance Search with Pseudo-Relevance Feedback}, year = {2017}, publisher = {NIPS 2017 Women in Machine Learning Workshop}, address = {Long Beach, CA, USA}, abstract = {

CNN classification models trained on millions of labeled images have been proven to encode {\textquotedblleft}general purpose{\textquotedblright} descriptors in their intermediate layers. These descriptors are useful for a diverse range of computer vision problems~\cite{1}. However, the target task of these models is substantially different to the instance search task. While classification is concerned with distinguishing between different classes, instance search is concerned with identifying concrete instances of a particular class.\ 

In this work we propose an unsupervised approach to finetune a model for similarity learning~\cite{2}. For that, we combine two different search engines: one based on off-the-shelf CNN features, and another one on the popular SIFT features. As shown in the figure below, we observe that the information of pre-trained CNN representations and SIFT is in most of the cases complementary, which allows the generation of high quality rank lists. The fusion of the two rankings is used to generate training data for a particular dataset. A pseudo-relevance feedback strategy~\cite{3} is used for sampling images from rankings, considering the top images as positive examples of a particular instance and middle-low ranked images as negative examples.

}, author = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @inbook {bBellver17, title = {Hierarchical Object Detection with Deep Reinforcement Learning}, booktitle = {Deep Learning for Image Processing Applications}, volume = {31}, year = {2017}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam, The Netherlands}, abstract = {

This work introduces a model for Hierarchical Object Detection with Deep Reinforcement Learning (HOD-DRL). The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention on five different predefined region candidates (smaller windows). This procedure is iterated providing a hierarchical image analysis.We compare two different candidate proposal strategies to guide the object search: with and without overlap. Moreover, our work compares two different strategies to extract features from a convolutional neural network for each region proposal: a first one that computes new feature maps for each region proposal, and a second one that computes the feature maps for the whole image to later generate crops for each region proposal. Experiments indicate better results for the overlapping candidate proposal strategy and a loss of performance for the cropped image features due to the loss of spatial resolution. We argue that, while this loss seems unavoidable when working with a large number of object candidates, the much more reduced number of region proposals generated by our reinforcement learning agent allows considering to extract features for each location without sharing convolutional computation among regions. Source code and models are available at https://imatge-upc.github.io/detection-2016-nipsws/.

}, keywords = {computer Vision, Object detection, reinforcement learning}, issn = {978-1-61499-822-8}, doi = {10.3233/978-1-61499-822-8-164}, url = {http://ebooks.iospress.nl/volumearticle/48029}, author = {M{\'\i}riam Bellver and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Jordi Torres} } @article {aFrias-Velazquez, title = {Hierarchical stack filtering: a bitplane-based algorithm for massively parallel processors}, journal = {Journal of Real-Time Image Processing}, year = {2017}, month = {03/2017}, abstract = {

Full version available at http://rdcu.be/p6w1

With the development of novel parallel architectures for image processing, the implementation of well-known image operators needs to be reformulated to take advantage of the so-called massive parallelism. In this work, we propose a general algorithm that implements a large class of nonlinear filters, called stack filters, with a 2D-array processor. The proposed method consists of decomposing an image into bitplanes with the bitwise decomposition, and then process every bitplane hierarchically. The filtered image is reconstructed by simply stacking the filtered bitplanes according to their order of significance. Owing to its hierarchical structure, our algorithm allows us to trade-off between image quality and processing time, and to significantly reduce the computation time of low-entropy images. Also, experimental tests show that the processing time of our method is substantially lower than that of classical methods when using large structuring elements. All these features are of interest to a variety of real-time applications based on morphological operations such as video segmentation and video enhancement.\ 

}, keywords = {Array processors, Bitwise decomposition, Morphological operators, Smart camera, Stack filters}, url = {http://rdcu.be/p6w1}, author = {Frias-Velazquez, A. and Morros, J.R. and Garc{\'\i}a, M. and Philips, Wilfried} } @conference {cRomero-Lopeza, title = {The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier based on Skin Lesion Images}, booktitle = {Annual Meeting of the Society of Imaging Informatics in Medicine (SIIM)}, year = {2017}, month = {06/2017}, publisher = {Society of Imaging Informatics for Medicine}, organization = {Society of Imaging Informatics for Medicine}, address = {Pittsburgh, PA, USA}, abstract = {

The accuracy and sensitivity of a Deep Learning based approach for a 2-class classifier for early melanoma detection\ based on skin lesion dermoscopic images increases when\ the classifier is trained with segmented inputs (i.e., images\ containing only the lesions as binary masks, without the\ surrounding context) instead of entire images.

[SIIM 2017 Annual Meeting website]

[SIIM 2017 Session where our work is presented]

The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier Based on Skin Lesion Images from Oge Marques
}, url = {http://hdl.handle.net/2117/105582}, author = {Romero-Lopez, Adria and Burdick, Jack and Xavier Gir{\'o}-i-Nieto and Marques, Oge} } @mastersthesis {xArazo, title = {The impact of visual saliency prediction in image classification}, year = {2017}, abstract = {

Advisors: Eva Mohedano, Kevin McGuinness and Xavier Giro-i-Nieto

Program: Master{\textquoteright}s degree in Telecommunications Engineering (MET)

Grade: A (9.7/10.0)

This thesis introduces an architecture to improve the accuracy of a Convolutional Neural Network trained for image classification using visual saliency predictions from the original images. In this thesis the accuracy of a Convolutional Neural Network (CNN) trained for classification has been improved using saliency maps from the original images. The network had an AlexNet architecture and was trained using 1.2 million images from the Imagenet dataset. Two methods had been explored in order to exploit the information from the visual saliency predictions. The first methodologies implemented applied the saliency maps directly to the existing layers of the CNN, which in some cases were already trained for classification and in other they were initialized with random weights. In the second methodology the information from the saliency maps was merged from a new branch, trained at the same time as the initial CNN. In order to speed up the training of the networks the experiments were implemented using images reduced to 128x128. With this sizes the proposed model achieves 12.39\% increase in Top-1 accuracy performance with respect to the original CNN, and additionally reduces the number of parameters needed compared to AlexNet. Regarding the original size images 227x227 a model that increases 1.72\% Top-1 accuracy is proposed. To accelerate the training process of the network the images have been reduced. The methodology that provides the higher improvement in accuracy will be implemented using the original size of the images. The results will be compared to those obtained from the network trained only with the original images. All the methodologies proposed are implemented in a network previously trained for classification. Additionally the most successful methodologies will be implemented in the training of a network. The results will provide information about the best way to add saliency maps to improve the accuracy.

The impact of visual saliency prediction in image classification from Xavier Giro
}, author = {Arazo, Eric}, editor = {McGuinness, Kevin and Mohedano, Eva and Xavier Gir{\'o}-i-Nieto} } @article {xGiro-i-Nieto17, title = {La meitat de les not{\'\i}cies que consumirem el 2022 seran falses}, year = {2017}, publisher = {Corporaci{\'o} Catalana de Mitjans Audiovisuals}, address = {Sant Joan Desp{\'\i}}, abstract = {

Reportatge em{\`e}s dins el Telenot{\'\i}cies Vespre de Televisi{\'o} de Catalunya el diumenge 26 de novembre de 2017.

Els programes d{\textquoteright}intel{\textperiodcentered}lig{\`e}ncia artificial s{\'o}n capa{\c c}os de crear imatges i veus cada cop m{\'e}s realistes i obren la porta a generar mentides de forma m{\'e}s automatitzada

}, keywords = {deep learning, fake news, gan}, url = {http://www.ccma.cat/324/la-meitat-de-les-noticies-que-consumirem-el-2022-seran-falses/noticia/2823178/}, author = {Xavier Gir{\'o}-i-Nieto and Pascual-deLaPuente, Santiago and Mir{\'o}, Vict{\`o}ria and Esteve, Oriol} } @conference {cSalvadorc, title = {Learning Cross-modal Embeddings for Cooking Recipes and Food Images}, booktitle = {CVPR}, year = {2017}, month = {03/2017}, publisher = {CVF / IEEE}, organization = {CVF / IEEE}, address = {Honolulu, Hawaii, USA}, abstract = {

In this paper, we introduce Recipe1M, a new large-scale, structured corpus of over 1m cooking recipes and 800k food images. As the largest publicly available collection of recipe data, Recipe1M affords the ability to train high-capacity models on aligned, multi-modal data. Using these data, we train a neural network to find a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Additionally, we demonstrate that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulate that these embeddings will provide a basis for further exploration of the Recipe1M dataset and food and cooking in general.

[Project page]

In the news:

}, doi = {10.1109/CVPR.2017.327}, url = {http://openaccess.thecvf.com/content_cvpr_2017/html/Salvador_Learning_Cross-Modal_Embeddings_CVPR_2017_paper.html}, author = {Amaia Salvador and Hynes, Nicholas and Aytar, Yusuf and Marin, Javier and Ofli, Ferda and Weber, Ingmar and Torralba, Antonio} } @article {aPetrone17, title = {Magnetic Resonance Imaging as a valuable tool for Alzheimer{\textquoteright}s disease screening}, journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association}, volume = {13}, year = {2017}, month = {07/2017}, pages = {P1245}, doi = {10.1016/j.jalz.2017.07.457}, url = {https://doi.org/10.1016/j.jalz.2017.07.457}, author = {P. Petrone and Ver{\'o}nica Vilaplana and Adri{\`a} Casamitjana and D. Sanchez-Escobedo and A. Tucholka and R. Cacciaglia and G. Operto and S. Skouras and C. Falcon and J.L. Molinuevo and J.D. Gispert} } @conference {cPetrone17, title = {Magnetic Resonance Imaging as a valuable tool for Alzheimer{\textquoteright}s disease screening}, booktitle = {Alzheimer{\textquoteright}s Association International Conference, London, 2017}, year = {2017}, month = {07/2017}, author = {P. Petrone and Ver{\'o}nica Vilaplana and Adri{\`a} Casamitjana and A. Tucholka and C. Falcon and R. Cacciaglia and G. Operto and S. Skouras and J.L. Molinuevo and J.D. Gispert} } @article {aPont-Tuset2015, title = {Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation}, journal = { IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, volume = {38}, year = {2017}, pages = {128-140}, abstract = {

We propose a unified approach for bottom-up hierarchical image segmentation and object proposal generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object proposals by exploring efficiently their combinatorial space. We also present Single-scale Combinatorial Grouping (SCG), a faster version of MCG that produces competitive proposals in under five second per image. We conduct an extensive and comprehensive empirical validation on the BSDS500, SegVOC12, SBD, and COCO datasets, showing that MCG produces state-of-the-art contours, hierarchical regions, and object proposals.

}, url = {http://arxiv.org/abs/1503.00848v1}, author = {Jordi Pont-Tuset and Pablo Arbelaez and Jonathan T. Barron and Marqu{\'e}s, F. and Jitendra Malik} } @phdthesis {dMaceira17, title = {Multi-view depth coding based on a region representation combining color and depth information}, year = {2017}, month = {06/2017}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {PhD}, abstract = {

Depth map data is used to supplement the color data in multi-view sequences. As depth maps present distinct characteristics than natural color images, new coding techniques are required to represent their smooth regions and sharp edges. In this thesis, segmentation-based coding techniques are proposed to encode depth maps by exploiting the redundancy between color and depth information. Methods developed combine partitions obtained from color and depth images to find efficient representations. The color image is assumed to be available before the depth map coding process, therefore a color partition can be obtained at the decoder without introducing coding cost.

Two hierarchical image segmentation algorithms are proposed to generate color and depth partitions for coding applications. The color segmentation obtains a super-pixel representation using color information, spatial distribution and shape complexity. The depth segmentation uses a 3D planar model for each region to extract the structure of the scene. Color and depth partitions are combined in depth map coding methods to find the final coding partition.

Different methods for texture representation have been explored in this thesis. Initial approaches used 2D coding methods, while a 3D representation have been proposed to represent depth maps from multiple views with a unique segmentation. This 3D representation is used to segment depth maps in single-view and multi-view configurations. Final coding partitions are obtained with a ratedistortion optimization over a hierarchy of regions. Segmentation-based coding techniques proposed obtain competitive results with HEVC coding standards.

}, url = {http://hdl.handle.net/2117/110439}, author = {Maceira, M.}, editor = {Ruiz-Hidalgo, J. and Morros, J.R.} } @inbook {bMohedano17, title = {Object Retrieval with Deep Convolutional Features}, booktitle = {Deep Learning for Image Processing Applications}, volume = {31}, number = {Advances in Parallel Computing}, year = {2017}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam, The Netherlands}, abstract = {

Image representations extracted from convolutional neural networks (CNNs) outdo hand-crafted features in several computer vision tasks, such as visual image retrieval. This chapter recommends a simple pipeline for encoding the local activations of a convolutional layer of a pretrained CNN utilizing the well-known Bag of Words (BoW) aggregation scheme and called bag of local convolutional features (BLCF). Matching each local array of activations in a convolutional layer to a visual word results in an assignment map, which is a compact representation relating regions of an image with a visual word. We use the assignment map for fast spatial reranking, finding object localizations that are used for query expansion. We show the suitability of the BoW representation based on local CNN features for image retrieval, attaining state-of-the-art performance on the Oxford and Paris buildings benchmarks. We demonstrate that the BLCF system outperforms the latest procedures using sum pooling for a subgroup of the challenging TRECVid INS benchmark according to the mean Average Precision (mAP) metric.

}, issn = {978-1-61499-822-8 }, doi = {10.3233/978-1-61499-822-8-137}, url = {http://ebooks.iospress.nl/volumearticle/48028}, author = {Mohedano, Eva and Amaia Salvador and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Marqu{\'e}s, F.} } @conference {xSalvadora, title = {Recurrent Semantic Instance Segmentation}, booktitle = {NIPS 2017 Women in Machine Learning Workshop (WiML)}, year = {2017}, month = {12/2017}, publisher = {NIPS 2017 Women in Machine Learning Workshop}, organization = {NIPS 2017 Women in Machine Learning Workshop}, address = {Long Beach, CA, USA}, abstract = {

We present a recurrent model for end-to-end instance-aware semantic segmentation that is able to sequentially generate pairs of masks and class predictions. Our proposed system is trainable end-to-end for instance segmentation, does not require further post-processing steps on its output and is conceptually simpler than current methods relying on object proposals. While recent works have proposed recurrent architectures for instance segmentation, these are trained and evaluated for a single category.

Our model is composed of a series of Convolutional LSTMs that are applied in chain with upsampling layers in between to predict a sequence of binary masks and associated class probabilities. Skip connections are incorporated in our model by concatenating the output of the corresponding convolutional layer in the base model with the upsampled output of the ConvLSTM. Binary masks are finally obtained with a 1x1 convolution with sigmoid activation. We concatenate the side outputs of all ConvLSTM layers and apply a per-channel max-pooling operation followed by a single fully-connected layer with softmax activation to obtain the category for each predicted mask.

We train and evaluate our models with the Pascal VOC 2012 dataset. Future work will aim at analyzing and understanding the behavior of the network on other datasets, comparing the system with state of the art solutions and study the relationship of the learned object discovery patterns of our model with those of humans.

}, author = {Amaia Salvador and Baradad, Manel and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cPana, title = {SalGAN: Visual Saliency Prediction with Generative Adversarial Networks}, booktitle = {CVPR 2017 Scene Understanding Workshop (SUNw)}, year = {2017}, address = {Honolulu, Hawaii, USA}, abstract = {

We introduce SalGAN, a deep convolutional neural network for visual saliency prediction trained with adversarial examples. The first stage of the network consists of a generator model whose weights are learned by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency maps. The resulting prediction is processed by a discriminator network trained to solve a binary classification task between the saliency maps generated by the generative stage and the ground truth ones. Our experiments show how adversarial training allows reaching state-of-the-art performance across different metrics when combined with a widely-used loss function like BCE.

}, url = {https://arxiv.org/abs/1701.01081}, author = {Pan, Junting and Cristian Canton-Ferrer and McGuinness, Kevin and O{\textquoteright}Connor, N. and Jordi Torres and Elisa Sayrol and Xavier Gir{\'o}-i-Nieto} } @conference {cAssens, title = {SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes}, booktitle = {ICCV Workshop on Egocentric Perception, Interaction and Computing}, year = {2017}, month = {07/2017}, publisher = {IEEE}, organization = {IEEE}, address = {Venice, Italy}, abstract = {

We introduce SaltiNet, a deep neural network for scanpath prediction trained on 360-degree images. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are learned by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scanpaths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks.

Winner of three awards at the Salient 360 Challenge at IEEE ICME 2017 (Hong Kong): Best Scan Path, Best Student Scan-path and Audience Award.

SaltiNet: The Temporal Dimension of Visual Attention Models from Xavier Giro-i-Nieto
}, isbn = {978-1-5386-1034-3}, doi = {10.1109/ICCVW.2017.275}, url = {http://ieeexplore.ieee.org/document/8265485/}, author = {Assens, Marc and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @conference {cRomero-Lopez, title = {Skin Lesion Classification from Dermoscopic Images using Deep Learning}, booktitle = {The 13th IASTED International Conference on Biomedical Engineering (BioMed 2017)}, year = {2017}, month = {02/2017}, address = {Innsbruck Austria}, abstract = {

The recent emergence of deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist the human expert in making better decisions about a patient{\textquoteright}s health. In this paper we focus on the problem of skin lesion classification, particularly early melanoma detection, and present a deep-learning based approach to solve the problem of classifying a dermoscopic image containing a skin lesion as malignant or benign. \ The proposed solution is built around the VGGNet convolutional neural network architecture and uses the transfer learning paradigm. Experimental results are encouraging: on the ISIC Archive dataset, the proposed method achieves a sensitivity value of 78.66\%, which is significantly higher than the current state of the art on that dataset.

[BioMed conference 2017]

}, keywords = {Convolutional Neural Networks, deep learning, machine learning, Medical Decision Support Systems, Medical Image Analysis, Skin Lesions}, url = {http://upcommons.upc.edu/handle/2117/103386}, author = {Romero-Lopez, Adria and Burdick, Jack and Xavier Gir{\'o}-i-Nieto and Marques, Oge} } @mastersthesis {xRomero-Lopez, title = {Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Networks}, year = {2017}, abstract = {

Advisors: Oge Marques (Florida Atlantic University) and Xavier Giro-i-Nieto (UPC)

The recent emergence of machine learning and deep learning methods for medical image analysis has enabled the development of intelligent medical imaging-based diagnosis systems that can assist physicians in making better decisions about a patient{\textquoteright}s health. In particular, skin imaging is a field where these new methods can be applied with a high rate of success.\ 

This thesis focuses on the problem of automatic skin lesion detection, \ particularly on melanoma detection, by applying semantic segmentation and classification from dermoscopic images using a deep learning based approach.\ For the first problem, a U-Net convolutional neural network architecture is applied for an accurate extraction of the lesion region.\ For the second problem, the current model performs a binary classification (benign versus malignant) that can be used for early melanoma detection. The model is general enough to be extended to multi-class skin lesion classification. The proposed solution is built around the VGG-Net ConvNet architecture and uses the transfer learning paradigm.\ Finally, this work performs a comparative evaluation of classification \ alone (using the entire image) against a combination of the two approaches (segmentation followed by classification) in order to assess which of them achieves better classification results.

[Source code]

Skin Lesion Detection from Dermoscopic Images using Convolutional Neural Networks from Xavier Giro
}, author = {Romero-Lopez, Adria}, editor = {Xavier Gir{\'o}-i-Nieto and Marques, Oge} } @conference {cLuque17, title = {Spatio-Temporal Road Detection from Aerial Imagery using CNNs}, booktitle = {International Conference on Computer Vision Theory and Applications}, year = {2017}, month = {2/2017}, address = {Porto, Portugal}, abstract = {

The main goal of this paper is to detect roads from aerial imagery recorded by drones. To achieve this, we propose a modification of SegNet, a deep fully convolutional neural network for image segmentation. In order to train this neural network, we have put together a database containing videos of roads from the point of view of a small commercial drone. Additionally, we have developed an image annotation tool based on the watershed technique, in order to perform a semi-automatic labeling of the videos in this database. The experimental results using our modified version of SegNet show a big improvement on the performance of the neural network when using aerial imagery, obtaining over 90\% accuracy.

}, doi = {10.5220/0006128904930500}, author = {Luque, B. and Morros, J.R. and Ruiz-Hidalgo, J.} } @mastersthesis {xAssens, title = {The Temporal Dimension of Visual Attention Models}, year = {2017}, abstract = {

Program: Bachelor Degree on Telecommunications Science and Technologies (CITTEL)

Grade: A with honours (10.0/10.0)

This thesis explores methodologies for scanpath prediction on images using deep learning\ frameworks.\ As a preliminary step, we analyze the characteristics of the data provided by di erent datasets.\ We then explore the use of Convolutional Neural Networks (CNN) and Long-Short-Term-Memory\ (LSTM) newtworks for scanpath prediction. We observe that these models fail due to the high\ stochastic nature of the data.\ With the gained insight, we propose a novel time-aware visual saliency representation named\ Saliency Volume, that averages scanpaths over multiple observers.\ Next, we explore the SalNet network and adapt it for saliency volume prediction, and we find\ several ways of generating scanpaths from saliency volumes.\ Finally, we ne-tuned our model for scanpaht prediction on 360-degree images and successfully\ submitted it to the Salient360! Challenge from ICME. The source code and models are publicly\ available at https://github.com/massens/saliency-360salient-2017.

The Temporal Dimension of Visual Attention Models from Xavier Giro-i-Nieto
}, author = {Assens, Marc}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and Noel E. O{\textquoteright}Connor} } @conference {cLe, title = {Towards large scale multimedia indexing: A case study on person discovery in broadcast news}, booktitle = {International Workshop on Content-Based Multimedia Indexing - CBMI 2017}, year = {2017}, month = {06/2017}, address = {Firenze, Italy}, abstract = {

The rapid growth of multimedia databases and the human interest in their peers make indices representing the location and identity of people in audio-visual documents essential for searching archives. Person discovery in the absence of prior identity knowledge requires accurate association of audio-visual cues and detected names. To this end, we present 3 different strategies to approach this problem: clustering-based naming, verification-based naming, and graph-based naming. Each of these strategies utilizes different recent advances in unsupervised face / speech representation, verification, and optimization. To have a better understanding of the approaches, this paper also provides a quantitative and qualitative comparative study of these approaches using the associated corpus of the Person Discovery challenge at MediaEval 2016. From the results of our experiments, we can observe the pros and cons of each approach, thus paving the way for future promising research directions.

}, doi = {https://doi.org/10.1145/3095713.3095732}, author = {Nam Le and Herv{\'e} Bredin and Gabriel Sargent and Miquel India and Paula Lopez-Otero and Claude Barras and Camille Guinaudeau and Guillaume Gravier and Gabriel Barbosa da Fonseca and Izabela Lyon Freire and Zenilton Patroc{\'\i}nio Jr. and Silvio Jamil F. Guimaraes and Gerard Mart{\'\i} and Morros, J.R. and Javier Hernando and Laura Docio-Fernandez and Carmen Garcia-Mateo and Sylvain Meignier and Jean-Marc Odobez} } @mastersthesis {xRoldan, title = {Visual Question Answering 2.0}, year = {2017}, abstract = {

This bachelor{\textquoteright}s thesis explores di erent deep learning techniques to solve the Visual Question-Answering (VQA) task, whose aim is to answer questions about images. We study di erent Convolutional\ Neural Networks (CNN) to extract the visual representation from images: Kernelized-CNN (KCNN), VGG-16 and Residual Networks (ResNet). We also analyze the impact of using\ pre-computed word embeddings trained in large datasets (GloVe embeddings). Moreover, we\ examine di erent techniques of joining representations from di erent modalities. This work has\ been submitted to the second edition Visual Question Answering Challenge, and obtained a\ 43.48\% of accuracy.

[Project page]

Visual Question Answering 2.0 from Xavier Giro-i-Nieto
}, author = {Rold{\'a}n, Francisco}, editor = {Xavier Gir{\'o}-i-Nieto and Masuda-Mora, Issey and Pascual-deLaPuente, Santiago} } @article {a, title = {Assessment of Crowdsourcing and Gamification Loss in User-Assisted Object Segmentation}, journal = {Multimedia Tools and Applications}, volume = {23}, year = {2016}, month = {11/2016}, chapter = {15901-15928}, abstract = {

There has been a growing interest in applying human computation -- \ particularly crowdsourcing techniques -- to assist in the solution of multimedia, image processing, and computer vision problems which are still too difficult to solve using fully automatic algorithms, and yet relatively easy for humans.

In this paper we focus on a specific problem -- object segmentation within color images -- and compare different solutions which combine color image segmentation algorithms with human efforts, either in the form of an explicit interactive segmentation task or through an implicit collection of valuable human traces with a game.\ We use Click{\textquoteright}n{\textquoteright}Cut, a friendly, web-based, interactive segmentation tool that allows segmentation tasks to be assigned to many users, and Ask{\textquoteright}nSeek, a game with a purpose designed for object detection and segmentation.

The two main contributions of this paper are: (i) We use the results of Click{\textquoteright}n{\textquoteright}Cut campaigns with different groups of users to examine and quantify the crowdsourcing loss incurred when an interactive segmentation task is assigned to paid crowd-workers, comparing their results to the ones obtained when computer vision experts are asked to perform the same tasks. (ii) Since interactive segmentation tasks are inherently tedious and prone to fatigue, we\ compare the quality \ of the results obtained with Click{\textquoteright}n{\textquoteright}Cut with the ones obtained using a (fun, interactive, and potentially less tedious) game designed for the same purpose. We call this contribution the assessment of the gamification loss, since it refers to how much quality of segmentation results may be lost when we switch to a game-based approach to the same task.\ 

We demonstrate that the crowdsourcing loss is significant when using all the data points from workers, but decreases substantially (and becomes comparable to the quality of expert users performing similar tasks) after performing a modest amount of data analysis and filtering out of users whose data are clearly not useful. We also show that -- on the other hand -- the gamification loss is significantly more severe: the quality of the results drops roughly by half when switching from a focused (yet tedious) task to a more fun and relaxed game environment.\ 

}, keywords = {Crowdsourcing, GWAP, Object detection, Object segmentation, Serious games}, issn = {1573-7721}, doi = {10.1007/s11042-015-2897-6}, url = {http://dx.doi.org/10.1007/s11042-015-2897-6}, author = {Carlier, Axel and Amaia Salvador and Cabezas, Ferran and Xavier Gir{\'o}-i-Nieto and Charvillat, Vincent and Marques, Oge} } @conference {cMohedanoa, title = {Bags of Local Convolutional Features for Scalable Instance Search}, booktitle = {ACM International Conference on Multimedia Retrieval (ICMR)}, year = {2016}, month = {06/2016}, publisher = {ACM}, organization = {ACM}, address = {New York City, NY; USA}, abstract = {

Image representations extracted from convolutional neural networks (CNNs) have been shown to outperform hand-crafted features in multiple computer vision tasks, such as visual image retrieval. This work proposes a simple pipeline for encoding the local activations of a convolutional layer of a pre-trained CNN using the well-known bag of words aggregation scheme (BoW). Assigning each local array of activations in a convolutional layer to a visual word produces an \textit{assignment map}, a compact representation that relates regions of an image with a visual word. We use the assignment map for fast spatial reranking, obtaining object localizations that are used for query expansion. We demonstrate the suitability of the Bag of Words representation based on local CNN features for image retrieval, achieving state-of-the-art performance on the Oxford and Paris buildings benchmarks. We show that our proposed system for CNN feature aggregation with BoW outperforms state-of-the-art techniques using sum pooling at a subset of the challenging TRECVid INS benchmark.

Best poster award at ACM ICMR 2016

Preprint on arXiv

Project page with source code

Post on GitXiv

Overall acceptance rate in ICMR 2016: 30\%\ 

2016-05-Seminar-AmaiaSalvador-DeepVision from Image Processing Group on Vimeo.

Convolutional Features for Instance Search from Xavier Giro
}, keywords = {Bag of Words, Convolutional Neural Networks, Instance Retrieval}, isbn = {978-1-4503-4359-6}, doi = {http://dx.doi.org/10.1145/2911996.2912061}, url = {http://dx.doi.org/10.1145/2911996.2912061}, author = {Mohedano, Eva and Amaia Salvador and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Marqu{\'e}s, F.} } @conference {cPoignant16, title = {The CAMOMILE Collaborative Annotation Platform for Multi-modal, Multi-lingual and Multi-media Documents}, booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)}, year = {2016}, month = {05/2016}, address = {Portoro{\v z} (Slovenia)}, abstract = {

In this paper, we describe the organization and the implementation of the CAMOMILE collaborative annotation framework for multimodal, multimedia, multilingual (3M) data. Given the versatile nature of the analysis which can be performed on 3M data, the structure of the server was kept intentionally simple in order to preserve its genericity, relying on standard Web technologies. Layers of annotations, defined as data associated to a media fragment from the corpus, are stored in a database and can be managed through standard interfaces with authentication. Interfaces tailored specifically to the needed task can then be developed in an agile way, relying on simple but reliable services for the management of the centralized annotations. We then present our implementation of an active learning scenario for person annotation in video, relying on the CAMOMILE server; during a dry run experiment, the manual annotation of 716 speech segments was thus propagated to 3504 labeled tracks. The code of the CAMOMILE framework is distributed in open source.

}, keywords = {active learning, Annotation tool, collaborative annotation, multimedia, person annotation}, isbn = {978-2-9517408-9-1}, url = {http://www.lrec-conf.org/proceedings/lrec2016/pdf/456_Paper.pdf}, author = {Johann Poignant and Mateusz Budnik and Herv{\'e} Bredin and Claude Barras and Mickael Stefas and Pierrick Bruneau and Gilles Adda and Laurent Besacier and Hazim Ekenel and Gil Francopoulo and Javier Hernando and Joseph Mariani and Morros, J.R. and Georges Qu{\'e}not and Sophie Rosset and Thomas Tamisier} } @article {aMaceira, title = {Depth map compression via 3D region-based representation}, journal = {Multimedia Tools and Applications}, year = {2016}, month = {07/2016}, abstract = {

The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-016-3727-1

In 3D video, view synthesis is used to create new virtual views between encoded camera views. Errors in the coding of the depth maps introduce geometry inconsistencies in synthesized views. In this paper, a new 3D plane representation of the scene is presented which improves the performance of current standard video codecs in the view synthesis domain. Two image segmentation algorithms are proposed for generating a color and depth segmentation. Using both partitions, depth maps are segmented into regions without sharp discontinuities without having to explicitly signal all depth edges. The resulting regions are represented using a planar model in the 3D world scene. This 3D representation allows an ecient encoding while preserving the 3D characteristics of the scene. The 3D planes opens up the possibility to code multiview images with an unique representation.

}, keywords = {3D representation, Data Compression, Depth map coding, Image segmentation}, doi = {10.1007/s11042-016-3727-1}, url = {http://rdcu.be/nqyE}, author = {Maceira, M. and Morros, J.R. and Ruiz-Hidalgo, J.} } @conference {cMarsden, title = {Dublin City University and Partners{\textquoteright} Participation in the INS and VTT Tracks at TRECVid 2016}, booktitle = {TRECVID Workshop 2016}, year = {2016}, month = {11/2016}, address = {Gaithersburg, MD, USA}, abstract = {

DCU participated with a consortium of colleagues from NUIG and UPC in two tasks,\ INS and VTT. For the INS task we developed a framework consisting of face detection and\ representation and place detection and representation, with a user annotation of top-ranked\ videos. For the VTT task we ran 1,000 concept detectors from the VGG-16 deep CNN on\ 10 keyframes per video and submitted 4 runs for caption re-ranking, based on BM25, Fusion,\ Word2Vec and a fusion of baseline BM25 and Word2Vec. With the same pre-processing for\ caption generation we used an open source image-to-caption CNN-RNN toolkit NeuralTalk2\ to generate a caption for each keyframe and combine them.

}, url = {http://doras.dcu.ie/21484/}, author = {Marsden, Mark and Mohedano, Eva and McGuinness, Kevin and Calafell, Andrea and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Zhou, Jiang and Azevedo, Lucas and Daubert, Tobias and Davis, Brian and H{\"u}rlimann, Manuela and Afli, Haithem and Du, Jinhua and Ganguly, Debasis and Li, Wei and Way, Andy and Smeaton, Alan F.} } @unpublished {xBellver, title = {Efficient search of objects in images using deep reinforcement learning}, journal = {NIPS Women in Machine Learning Workshop}, year = {2016}, type = {Abstract}, address = {Barcelona.}, abstract = {

When we humans look at an image, we always perform a sequential extraction of information in order to understand its content. First, we fix our gaze to the most salient part of the image, and from the information extracted we guide our look towards another point of the image, until we have analyzed all the relevant information of it. This is our natural and instinctive behaviour to gather information from our surroundings. Traditionally in computer vision, images have been analysed at the local scale following a sliding window scanning, often at different scales. This approach analyses the different parts of the images independently, without constructing a correlation among them. Just by introducing a hierarchical partition of the image, we can more easily exploit the correlation between regions through a top-down scanning which firstly takes a global view of the image to sequentially focus on the local parts that contain the relevant information (eg. objects or faces). Moreover, if we train a deep architecture that is not based on rewarding regions observed independently, such as traditional object proposals, but rewards successful long-term searches by connecting the different regions observed, we can achieve a sequential detection of objects. , which is proven to be richer in information compared to use simple independent fixations.

The goal of this ongoing research is to perform an efficient detection of objects in images. In order to be efficient, the key idea is to focus on those parts of the image which contain richer information and zoom on them, guiding a hierarchical search for objects. An intelligent entity capable of deciding where to focus the attention in the image is trained using deep reinforcement learning techniques. This RL agent first looks the whole image and decides which of the partitions of a quadtree partition is richer in order to find a certain category of objects. The reinforcement learning agent is trained using deep Q-learning using a similar architecture to the one used by DeepMind [1].

This work is based on the key idea that with reinforcement learning we can perform a sequential search that rewards short sequences of searches that obtain the highest long-term reward in terms of intersection over union of predicted bounding boxes and ground truth bounding boxes.

The input of the network is a convolutional descriptor of the region observed at the current step and a history vector that describes the previous steps of the search. This idea was also used in [2]. Our main difference with the approach of such paper, is that we use a fixed hierarchical partition to guide our sequential search. Furthermore, in order to be efficient, sharing of convolutional features is a key aspect of the pipeline of our system. Convolutional features from VGG-16 [3] are extracted from the initial whole resolution picture, and then the descriptors for each subpartition are cropped from this feature map.

References:

[1] Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., \& Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.

[2] Caicedo, J. C., \& Lazebnik, S. (2015). Active object localization with deep reinforcement learning. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2488-2496).


[3] Simonyan, K., \& Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representation 2015.

}, author = {M{\'\i}riam Bellver and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cSalvadorb, title = {Faster R-CNN Features for Instance Search}, booktitle = {CVPR Workshop Deep Vision}, year = {2016}, month = {06/2016}, abstract = {

Image representations derived from pre-trained Convolutional Neural Networks (CNNs) have become the new state of the art in computer vision tasks such as instance retrieval. This work explores the suitability for instance retrieval of image- and region-wise representations pooled from an object detection CNN such as Faster R-CNN. We take advantage of the object proposals learned by a Region Proposal Network (RPN) and their associated CNN features to build an instance search pipeline composed of a first filtering stage followed by a spatial reranking. We further investigate the suitability of Faster R-CNN features when the network is fine-tuned for the same objects one wants to retrieve. We assess the performance of our proposed system with the Oxford Buildings 5k, Paris Buildings 6k and a subset of TRECVid Instance Search 2013, achieving competitive results.

2016-05-Seminar-AmaiaSalvador-DeepVision from Image Processing Group on Vimeo.

Convolutional Features for Instance Search from Xavier Giro
}, url = {http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w12/papers/Salvador_Faster_R-CNN_Features_CVPR_2016_paper.pdf}, author = {Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Satoh, Shin{\textquoteright}ichi} } @conference {cBellver, title = {Hierarchical Object Detection with Deep Reinforcement Learning}, booktitle = {Deep Reinforcement Learning Workshop, NIPS 2016}, year = {2016}, month = {12/2016}, abstract = {

We present a method for performing hierarchical object detection in images guided by a deep reinforcement learning agent. The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention among five different predefined region candidates (smaller windows). This procedure is iterated providing a hierarchical image analysis.

\ We compare two different candidate proposal strategies to guide the object search: with and without overlap. Moreover, our work compares two different strategies to extract features from a convolutional neural network for each region proposal: a first one that computes new feature maps for each region proposal, and a second one that computes the feature maps for the whole image to later generate crops for each region proposal.\ 

Experiments indicate better results for the overlapping candidate proposal strategy and a loss of performance for the cropped image features due to the loss of spatial resolution. We argue that, while this loss seems unavoidable when working with large amounts of object candidates, the much more reduced amount of region proposals generated by our reinforcement learning agent allows considering to extract features for each location without sharing convolutional computation among regions.

[Project page][arXiv][gitXiv][UPCommons][YouTube]

}, author = {M{\'\i}riam Bellver and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Jordi Torres} } @mastersthesis {xMasuda-Mora, title = {Open-Ended Visual Question-Answering}, year = {2016}, abstract = {

Advisors: Santiago de la Puente and Xavier Gir{\'o}-i-Nieto

Studies: Bachelor degree in Science and Telecommunication Technologies Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Grade: A with honors (10/10.0)

This thesis studies methods to solve Visual Question-Answering (VQA) tasks with a Deep Learning framework.As a preliminary step, we explore Long Short-Term Memory (LSTM) networks used in Natural Language Processing (NLP) to tackle Question-Answering (text based). We then modify the previous model to accept an image as an input in addition to the question. For this purpose, we explore the VGG-16 and K-CNN convolutional neural networks to extract visual features from the image. These are merged with the word embedding or with a sentence embedding of the question to predict the answer. This work was successfully submitted to the Visual Question Answering Challenge 2016, where it achieved a 53,62\% of accuracy in the test dataset. The developed software has followed the best programming practices and Python code style, providing a consistent baseline in Keras for different configurations. The source code and models are publicly available at https://github.com/imatge-upc/vqa-2016-cvprw.

Open-ended Visual Question-Answering from Xavier Giro
}, url = {https://upcommons.upc.edu/handle/2117/89671}, author = {Masuda-Mora, Issey}, editor = {Pascual-deLaPuente, Santiago and Xavier Gir{\'o}-i-Nieto} } @phdthesis {dVaras16, title = {Region-based Particle Filter Leveraged with a Hierarchical Co-clustering}, year = {2016}, month = {11/2016}, school = {UPC}, type = {PhD}, address = {Barcelona}, abstract = {

In this thesis, we exploit the hierarchical information associated with images to tackle two fundamental problems of computer vision: video object segmentation and video segmentation.

In the first part of the thesis, we present a video object segmentation approach that extends the well known particle filter algorithm to a region based image representation. Image partition is considered part of the particle filter measurement, which enriches the available information and leads to a reformulation of the particle filter theory. We define particles as unions of regions in the current image partition and their propagation is computed through a single optimization process. During this propagation, the prediction step is performed using a co-clustering between the previous image object partition and a partition of the current one, which allows us to tackle the evolution of non-rigid structures.

The second part of the thesis is devoted to the exploration of a co-clustering technique for video segmentation. This technique, given a collection of images and their associated hierarchies, clusters nodes from these hierarchies to obtain a coherent multiresolution representation of the image collection. We formalize the co-clustering as a Quadratic Semi-Assignment Problem and solve it with a linear programming relaxation approach that makes e ffective use of information from hierarchies. Initially, we address the problem of generating an optimal, coherent partition per image and, afterwards, we extend this method to a multiresolution framework. Finally, we particularize this framework to an iterative multiresolution video segmentation algorithm in sequences with small variations.

Finally, in the last part of the thesis we validate the presented techniques for object and video segmentation using the proposed algorithms as tools to tackle problems in a context for which they were not initially thought.

}, author = {David Varas}, editor = {Marqu{\'e}s, F.} } @conference {cPan, title = {Shallow and Deep Convolutional Networks for Saliency Prediction}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition, CVPR}, year = {2016}, month = {06/2016}, publisher = {Computer Vision Foundation / IEEE}, organization = {Computer Vision Foundation / IEEE}, address = {Las Vegas, NV, USA}, abstract = {

The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train end-to-end architectures that are both fast and accurate. Two designs are proposed: a shallow convnet trained from scratch, and a another deeper solution whose first three layers are adapted from another network trained for classification. To the authors knowledge, these are the first end-to-end CNNs trained and tested for the purpose of saliency prediction.

}, url = {http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pan_Shallow_and_Deep_CVPR_2016_paper.pdf}, author = {Pan, Junting and McGuinness, Kevin and Elisa Sayrol and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto} } @article {Pont-Tuset2015c, title = {Supervised Evaluation of Image Segmentation and Object Proposal Techniques}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, volume = {38}, year = {2016}, pages = {1465 - 1478}, author = {Jordi Pont-Tuset and Marqu{\'e}s, F.} } @mastersthesis {xMontes, title = {Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks}, year = {2016}, abstract = {

Advisors: Amaia Salvador and Xavier Gir{\'o}-i-Nieto.

Studies: Bachelor degree in Science and Telecommunication Technologies Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Grade: A (9.8/10.0)

This thesis explore different approaches using Convolutional and Recurrent Neural Networks to classify and temporally localize activities on videos, furthermore an implementation to achieve it has been proposed.

As the first step, features have been extracted from video frames using an state of the art 3D Convolutional Neural Network. This features are fed in a recurrent neural network that solves the activity classification and temporally location tasks in a simple and flexible way.

Different architectures and configurations have been tested in order to achieve the best performance and learning of the video dataset provided. In addition it has been studied different kind of post processing over the trained network{\textquoteright}s output to achieve a better results on the temporally localization of activities on the videos.

The results provided by the neural network developed in this thesis have been submitted to the ActivityNet Challenge 2016 of the CVPR, achieving competitive results using a simple and flexible architecture.

Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks from Xavier Giro
}, keywords = {deep learning, neural networks, videos}, author = {Montes, Alberto}, editor = {Amaia Salvador and Xavier Gir{\'o}-i-Nieto} } @conference {cMontes, title = {Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks}, booktitle = {1st NIPS Workshop on Large Scale Computer Vision Systems 2016}, year = {2016}, month = {12/2016}, abstract = {

This work proposes a simple pipeline to classify and temporally localize activities in untrimmed videos. Our system uses features from a 3D Convolutional Neural Network (C3D) as input to train a a recurrent neural network (RNN) that learns to classify video clips of 16 frames. After clip prediction, we post-process the output of the RNN to assign a single activity label to each video, and determine the temporal boundaries of the activity within the video. We show how our system can achieve competitive results in both tasks with a simple architecture. We evaluate our method in the ActivityNet Challenge 2016, achieving a 0.5874 mAP and a 0.2237 mAP in the classification and detection tasks, respectively. Our code and models are publicly available at:\ https://imatge-upc.github.io/activitynet-2016-cvprw/

}, author = {Montes, Alberto and Amaia Salvador and Pascual-deLaPuente, Santiago and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xReyesa, title = {Time-sensitive Egocentric Image Retrieval for Fidings Objects in Lifelogs}, year = {2016}, abstract = {

Advisors: Eva Mohedano (Insight DCU), Kevin McGuinness (Insight DCU) and\ Xavier Gir{\'o}-i-Nieto\ (UPC)

Studies: Bachelor degree in Science and Telecommunication Technologies Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Grade: A (9.4/10.0)

This work explores diverse practices for conducting an object search from large amounts of egocentric images taking into account their temporal information. The application of this technology is to identify where personal belongings were lost or forgotten. We develop a pipeline-structured system. Firstly, the images of the day being scanned are sorted based on their probability to depict the forgotten object. This stage is solved by applying an existing visual search engine based on deep learning features. Secondly, a learned threshold selects the top ranked images as candidates to contain the object. Finally the images are reranked based on temporal and diversity criteria. Furthermore, we build a validation environment for assessing the system{\textquoteright}s performance aiming to find the optimal configuration of its parameters. Due to the lack of related works to be compared with, this thesis proposes an novel evaluation framework and metric to assess the problem.

[Tfg cristian reyes] time sensitive egocentric image retrieval for finding objects in lifelogs from Xavier Giro
}, author = {Reyes, Cristian}, editor = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @article {xMora, title = {Towards Automatic Generation of Question Answer Pairs from Images}, year = {2016}, institution = {Visual Question Answering Challenge Workshop, CVPR 2016}, address = {Las Vegas, NV, USA}, abstract = {

This extended abstract presents our research in the generic field of Visual Question-Answering (VQA) focusing on a new branch that aims to generate question-answer pairs based on an image.\ To do so, we use the VQA dataset provided for the VQA challenge to train a Deep Neural Network which has the image as an input and two different outputs, the question and its associated answer.

Extended abstract presented as poster in the Visual Question Answering Challenge Workshop, CVPR 2016.

}, url = {http://www.visualqa.org/abstracts.html}, author = {Masuda-Mora, Issey and Pascual-deLaPuente, Santiago and Xavier Gir{\'o}-i-Nieto} } @conference {cIndia, title = {UPC System for the 2016 MediaEval Multimodal Person Discovery in Broadcast TV task}, booktitle = {MediaEval 2016 Workshop}, year = {2016}, month = {10/2016}, address = {Hilversum, The Netherlands}, abstract = {

The UPC system works by extracting monomodal signal segments (face tracks, speech segments) that overlap with the person names overlaid in the video signal. These segments are assigned directly with the name of the person and used as a reference to \ compare against the non-overlapping (unassigned) signal segments. This process is performed independently both on the speech and video signals. A simple fusion scheme is used to combine both monomodal annotations into a single one.

}, author = {India, M. and Mart{\'\i}, G. and Cotillas, C. and Bouritsas, G. and Elisa Sayrol and Morros, J.R. and Hernando, J.} } @mastersthesis {xCalafell, title = {Video Retrieval of Specific Persons in Specific Locations}, year = {2016}, abstract = {

Student: Andrea Calafell

Advisors: Eva Mohedano (Insight), Kevin McGuinness (Insight), Noel E. O{\textquoteright}Connor (Insight) and Xavier Gir{\'o}-i-Nieto (UPC)

Program: Master in Computer Vision (Class of 2016)

Grade: A (9.0/10.0)

This thesis explores good practices for improving the detection of specific people in specific places. An approach combining recurrent and convolutional neural network have been considered to perform face detection. However, other more conventional methods have been tested, obtaining the best results by exploiting a deformable part model approach. A CNN is also used to obtain the face feature vectors and, \ with the purpose of helping in the face recognition, an approach to perform query expansion has been also developed. Furthermore, in order to be able to evaluate the different configurations in our non-labelled dataset, a user interface has been used to annotate the images and be able to obtain a precision of the system. Finally, different fusion and normalization strategies has been explored with the aim of combining the scores obtained from the face recognition with the ones obtained in the place recognition.

Video Retrieval of Specific Persons in Specific Locations from Xavier Giro
}, author = {Calafell, Andrea}, editor = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @phdthesis {dVentura16, title = {Visual Object Analysis using Regions and Local Features}, year = {2016}, abstract = {

Thesis submission: 10-06-2016

Defense date:\ 08-07-2016

Grade: Excel{\textperiodcentered}lent Cum Laude \& International Mention

The fi rst part of this dissertation focuses on an analysis of the spatial context in semantic image segmentation. First, we review how spatial context has been tackled in the literature by local features and spatial aggregation techniques. From a discussion about whether the context is bene ficial or not for object recognition, we extend a Figure-Border-Ground segmentation for local feature aggregation with ground truth annotations to a more realistic scenario where object proposals techniques are used instead. Whereas the Figure and Ground regions represent the object and the surround respectively, the Border is a region around the object contour, which is found to be the region with the richest contextual information for object recognition. Furthermore, we propose a new contour-based spatial aggregation technique of the local features within the object region by a division of the region into four subregions. Both contributions have been tested on a semantic segmentation benchmark with a combination of free and non-free context local\ features that allows the models automatically learn whether the context is benefi cial or not for each semantic category.

The second part of this dissertation addresses the semantic segmentation for a set of closely-related images from an uncalibrated multiview scenario. State-of-the-art semantic segmentation algorithms fail on correctly segmenting the objects from some viewpoints when the techniques are independently applied to each viewpoint image. The lack of large annotations available for multiview segmentation do not allow to obtain a proper model that is robust to viewpoint changes. In this second part, we exploit the spatial correlation that exists between the di erent viewpoints images to obtain a more robust semantic segmentation. First, we review the state-of-the-art co-clustering, co-segmentation and video segmentation techniques that aim to segment the set of images in a generic way, i.e. without considering semantics. Then, a new architecture that considers motion information and provides a multiresolution segmentation is proposed for the co-clustering framework and outperforms state-of-the-art techniques for generic multiview segmentation. Finally,\ the proposed multiview segmentation is combined with the semantic segmentation results giving a method for automatic resolution selection and a coherent semantic multiview segmentation.

Visual Object Analysis using Regions and Local Features from Xavier Giro

}, url = {http://hdl.handle.net/10803/398407}, author = {Ventura, C.}, editor = {Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @article {xReyes, title = {Where did I leave my phone ?}, year = {2016}, month = {06/2016}, institution = {4th Workshop on Egocentric (First-Person) Vision, CVPR 2016}, type = {Extended abstract}, address = {Las Vegas, NV, USA}, abstract = {

The interest of users in having their lives digitally recorded has grown in the last years thanks to the advances on wearable sensors.\ Wearable cameras are one of the most informative ones, but they generate large amounts of images that require automatic analysis to build useful applications upon them.\ In this work we explore the potential of these devices to find the last appearance of personal objects among the more than 2,000 images that are generated everyday.\ This application could help into developing personal assistants capable of helping users when they do not remember where they left their personal objects.\ We adapt a previous work on instance search to the specific domain of egocentric vision.

Extended abstract presented as poster in the 4th Workshop on Egocentric (First-Person) Vision,\ CVPR 2016.

}, author = {Reyes, Cristian and Mohedano, Eva and McGuinness, Kevin and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto} } @conference {cReyes, title = {Where is my Phone? Personal Object Retrieval from Egocentric Images}, booktitle = {Lifelogging Tools and Applications Workshop in ACM Multimedia}, year = {2016}, month = {10/2016}, publisher = {ACM}, organization = {ACM}, address = {Amsterdam, The Netherlands}, abstract = {

This work presents a retrieval pipeline and evaluation scheme for the problem of finding the last appearance of personal objects in a large dataset of images captured from a wearable camera. Each personal object is modelled by a small set of images that define a query for a visual search engine.The retrieved results are reranked considering the temporal timestamps of the images to increase the relevance of the later detections. Finally, a temporal interleaving of the results is introduced for robustness against false detections. The Mean Reciprocal Rank is proposed as a metric to evaluate this problem. This application could help into developing personal assistants capable of helping users when they do not remember where they left their personal belongings.

}, doi = {http://dx.doi.org/10.1145/2983576.2983582}, url = {http://arxiv.org/abs/1608.08139}, author = {Reyes, Cristian and Mohedano, Eva and McGuinness, Kevin and Noel E. O{\textquoteright}Connor and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xVela15, title = {Combinar im{\'a}genes y h{\'a}bitos musicales para mejorar los sistemas de recomendaci{\'o}n de m{\'u}sica}, year = {2015}, url = {http://hdl.handle.net/2099.1/26236}, author = {Enrique Sa{\~n}oso Vela}, editor = {Ver{\'o}nica Vilaplana and Josep Ramon Morros} } @conference {cSalvadora, title = {Cultural Event Recognition with Visual ConvNets and Temporal Models}, booktitle = {CVPR ChaLearn Looking at People Workshop 2015}, year = {2015}, month = {06/2015}, abstract = {

This paper presents our contribution to the ChaLearn Challenge 2015 on Cultural Event Classification. The challenge in this task is to automatically classify images from 50 different cultural events. Our solution is based on the combination of visual features extracted from convolutional neural networks with temporal information using a hierarchical classifier scheme. We extract visual features from the last three fully connected layers of both CaffeNet (pretrained with ImageNet) and our fine tuned version for the ChaLearn challenge. We propose a late fusion strategy that trains a separate low-level SVM on each of the extracted neural codes. The class predictions of the low-level SVMs form the input to a higher level SVM, which gives the final event scores. We achieve our best result by adding a temporal refinement step into our classification scheme, which is applied directly to the output of each low-level SVM. Our approach penalizes high classification scores based on visual features when their time stamp does not match well an event-specific temporal distribution learned from the training and validation data. Our system achieved the second best result in the \ ChaLearn Challenge 2015 on Cultural Event Classification with a mean average precision of 0.767 on the test set.

[Preprint in arXiv][Workshop site][Slides on GDrive]

Cultural Event Recognition with Visual ConvNets and Temporal Models from Xavier Giro
}, url = {http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W09/papers/Salvador_Cultural_Event_Recognition_2015_CVPR_paper.pdf}, author = {Amaia Salvador and Zeppelzauer, Matthias and Manchon-Vizuete, Daniel and Calafell, Andrea and Xavier Gir{\'o}-i-Nieto} } @conference {cTacchini, title = {Do you have a Pop face? Here is a Pop song. Using profile pictures to mitigate the cold-start problem in Music Recommender Systems}, booktitle = {9th ACM Conference on Recommender Systems}, year = {2015}, month = {09/2015}, author = {Tacchini, E. and Morros, J.R. and Ver{\'o}nica Vilaplana and Sa{\~n}oso, E.} } @conference {cRoldan-Carlosa, title = {Event Video Retrieval using Global and Local Descriptors in Visual Domain}, booktitle = {IEEE/ACM International Workshop on Content-Based Multimedia Indexing - CBMI 2015 }, year = {2015}, month = {06/2015}, abstract = {

With the advent of affordable multimedia smart phones, it has become common that people take videos when they are at events. The larger the event, the larger is the amount of videos taken there and also, the more videos get shared online. To search in this mass of videos is a challenging topic. In this paper we present and discuss a prototype software for searching in such videos. We focus only on visual information, and we report on experiments based on a research data set. With a small study we show that our prototype demonstrates promising results by identifying the same scene in different videos taken from different angles solely based on content based image retrieval.

}, url = {https://upcommons.upc.edu/handle/2117/76553}, author = {Roldan-Carlos, Jennifer and Lux, Mathias and Xavier Gir{\'o}-i-Nieto and Pia Mu{\~n}oz-Trallero and Anagnostopoulos, Nektarios} } @conference {cMohedano, title = {Exploring EEG for Object Detection and Retrieval}, booktitle = {ACM International Conference on Multimedia Retrieval (ICMR) }, year = {2015}, address = {Shanghai, China}, abstract = {

This paper explores the potential for using Brain Computer Interfaces (BCI) as a relevance feedback mechanism in content-based image retrieval. We investigate if it is possible to capture useful EEG signals to detect if relevant objects are present in a dataset of realistic and complex images. \ We perform several experiments using a rapid serial visual presentation (RSVP) of images at different rates (5Hz and 10Hz) on 8 users with different degrees of familiarization with BCI and the dataset. We then use the feedback from the BCI and mouse-based interfaces to retrieve objects in a subset of TRECVid images. We show that it is indeed possible detect such objects in complex images and, also, that users with previous knowledge on the dataset or experience with the RSVP outperform others. When the users have limited time to annotate the images (100 seconds in our experiments) both interfaces are comparable in performance. Comparing our best users in a retrieval task, we found that EEG-based relevance feedback outperforms mouse-based feedback. The realistic and complex image dataset differentiates our work from previous studies on EEG for image retrieval.

[Extended version in arXiv:1504.02356]

[ACM ICMR 2015 website]

Overall acceptance rate: 33\% (source)

}, doi = {10.1145/2671188.2749368}, url = {http://arxiv.org/abs/1504.02356}, author = {Mohedano, Eva and Amaia Salvador and Porta, Sergi and Xavier Gir{\'o}-i-Nieto and Kevin McGuinness and Healy, Graham and O{\textquoteright}Connor, N.} } @article {aMohedano, title = {Improving Object Segmentation by using EEG signals and Rapid Serial Visual Presentation}, journal = {Multimedia Tools and Applications}, year = {2015}, month = {07/2015}, abstract = {

This paper explores the potential of brain-computer interfaces in segmenting objects from images. Our approach is centered around designing an effective method for displaying the image parts to the users such that they generate measurable brain reactions. When a block of pixels is displayed, we estimate the probability of that block containing the object of interest using a score based on EEG activity. After several such blocks are displayed in rapid visual serial presentation, the resulting probability map is binarized and combined with the GrabCut algorithm to segment the image into object and background regions. This study extends our previous work that showed how BCI and simple EEG analysis are useful in locating object boundaries in images

}, issn = {1573-7721}, doi = {10.1007/s11042-015-2805-0}, url = {http://dx.doi.org/10.1007/s11042-015-2805-0}, author = {Mohedano, Eva and Healy, Graham and Kevin McGuinness and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Smeaton, Alan F.} } @conference {cVentura, title = {Improving Spatial Codification in Semantic Segmentation}, booktitle = {IEEE International Conference on Image Processing (ICIP), 2015}, year = {2015}, month = {09/2015}, publisher = {IEEE}, organization = {IEEE}, address = {Quebec City}, abstract = {

This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem. We propose to partition the image into three regions for each object to be described: Figure, Border and Ground. This partition aims at minimizing the influence of the image context on the object description and vice versa by introducing an intermediate zone around the object contour. Furthermore, we also propose a richer visual descriptor of the object by applying a Spatial Pyramid over the Figure region. Two novel Spatial Pyramid configurations are explored: Cartesian-based and crown-based Spatial Pyramids. We test these approaches with state-of-the-art techniques and show that they improve the Figure-Ground based pooling in the Pascal VOC 2011 and 2012 semantic segmentation challenges.

Improving Spatial Codification in Semantic Segmentation from Xavier Giro
}, url = {http://arxiv.org/abs/1505.07409}, author = {Ventura, C. and Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Kevin McGuinness and Marqu{\'e}s, F. and O{\textquoteright}Connor, N.} } @article {xVentura, title = {Improving Spatial Codification in Semantic Segmentation (Supplementary Material)}, year = {2015}, month = {09/2015}, abstract = {

This document contains supplementary material for the paper "Improving Spatial Codification in Semantic Segmentation" submitted to ICIP 2015. First, there is a section dedicated to the results obtained by categories when ideal object candidates (ground truth masks) are used. Then, an analysis of the results using CPMC and MCG object candidates also detailed by categories. Finally, visual results for CPMC and MCG are showed.

}, author = {Ventura, C. and Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Kevin McGuinness and Marqu{\'e}s, F. and Noel E. O{\textquoteright}Connor} } @mastersthesis {xAlfaro15, title = {Inclusion of depth information on a temporal hierarchical co-clustering technique}, year = {2015}, abstract = {

Video segmentation aims to consistently group regions that are similar in appearance and movement along a sequence. This task is an essential step of video analysis and it has important applications in video coding, indexing and retrieval, 3D reconstruction, action recognition, etc. In particular, in this thesis, a multiresolution hierarchical co-clustering technique (MRHC) is analyzed in order to include depth information for improving image segmentation in sequences with small variations. This information is invariant to changes on brightness and/or texture and camera position. Thus, it may correct some errors that are present when segmentation techniques based on color and movement are used, because this information is independent of color information of the image and the movement that occurs in the scene.

Two considerations can be made. Firstly, all the regions of the same object should share similar depth values and, secondly, depth values from regions that do not belong to the same object show discontinuities. Three different ways of coding depth information in MRHC have been studied in this project. In the first approach, the similarity between regions is weighted according to the depth difference between them. The second approach determines the 3D-neighborhood between regions. Finally, a combination of the previous approaches is considered.

The Video Occlusion/Object Boundary Dataset has been used to evaluate the inclusion of the depth on MRHC and to compare this method with the state-of-the-art techniques in the field of video segmentation. The results obtained show that the use of depth information improves the outcome video techniques obtained with the MRHC, outperforming the state-of-the-art methods in this scenario.

}, author = {Alfaro, M.}, editor = {David Varas and Marqu{\'e}s, F.} } @conference {cMcGuinnessa, title = {Insight DCU at TRECVID 2015}, booktitle = {TRECVID 2015 Workshop}, year = {2015}, month = {11/2015}, publisher = {NIST}, organization = {NIST}, address = {Gaithersburg, MD, USA}, abstract = {

Insight-DCU participated in the instance search (INS), semantic indexing (SIN), and localization tasks (LOC) this year.

In the INS task we used deep convolutional network features trained on external data and the query data for this year to train our system. We submitted four runs, three based on convolutional network features, and one based on SIFT/BoW. F A insightdcu 1 was an automatic run using features from the last convolutional layer of a deep network with bag-of-words encoding and achieved 0.123 mAP. F A insightdcu 2 modi ed the previous run to use re-ranking based on an R-CNN model and achieved 0.111 mAP. I A insightdcu 3, our interactive run, achieved 0.269 mAP. Our SIFT-based run F A insightdcu 2 used weak geometric consistency to improve performance over the previous year to 0.187 mAP. Overall we found that using features from the convolutional layers improved performance over features from the fully connected layers used in previous years, and that weak geometric consistency improves performance for local feature ranking.

In the SIN task we again used convolutional network features, this time netuning a network pretrained on external data for the task. We submitted four runs, 2C D A insightdcu.15 1..4 varying the top-level learning algorithm and use of concept co-occurance. 2C D A insightdcu.15 1 used a linear SVM top-level learner, and achieved 0.63 mAP. Exploiting concept co-occurance improved the accuracy of our logistic regression run 2C D A insightdcu.15 3 from 0.058 mAP to 0.6 2C D A insightdcu.15 3.

Our LOC system used training data from IACC.1.B and features similar to our INS run, but using a VLAD encoding instead of a bag-of-words. Unfortunately there was problem with the run that we are still investigating.

Note: UPC and NII participated only in the INS task of this submission.

}, url = {http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.15.org.html}, author = {Kevin McGuinness and Mohedano, Eva and Amaia Salvador and Zhang, ZhenXing and Marsden, Mark and Wang, Peng and Jargalsaikhan, Iveel and Antony, Joseph and Xavier Gir{\'o}-i-Nieto and Satoh, Shin{\textquoteright}ichi and O{\textquoteright}Connor, N. and Smeaton, Alan F.} } @conference {cVaras15, title = {Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations}, booktitle = {ICCV - International Conference on Computer Vision}, year = {2015}, month = {12/2015}, abstract = {

This paper presents a co-clustering technique that, given a collection of images and their hierarchies, clusters nodes from these hierarchies to obtain a coherent multiresolution representation of the image collection. We formalize the co-clustering as a Quadratic Semi-Assignment Problem and solve it with a linear programming relaxation approach that makes effective use of information from hierarchies. Initially, we address the problem of generating an optimal, coherent partition per image and, afterwards, we extend this method to a multiresolution framework. Finally, we particularize this framework to an iterative multiresolution video segmentation algorithm in sequences with small variations. We evaluate the algorithm on the Video Occlusion/Object Boundary Detection Dataset, showing that it produces state-of-the-art results in these scenarios.\ 

}, url = {http://arxiv.org/abs/1510.04842}, author = {David Varas and Alfaro, M. and Marqu{\'e}s, F.} } @article {aBellot Pujalte15, title = {NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference}, journal = {BMC Bioinformatics}, volume = {16}, year = {2015}, month = {09/2015}, abstract = {

Background:
In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.

Results:
Our open-source and freely available Bioconductor package aggregates a large set of tools to assess the robustness of network inference algorithms against different simulators, topologies, sample sizes and noise intensities.

Conclusions:
The benchmarking framework that uses various datasets highlights the specialization of some methods toward network types and data. As a result, it is possible to identify the techniques that have broad overall performances.

}, keywords = {Benchmark, Bioconductor package, Gene expression, Gene regulation network reconstruction, Gene regulatory networks, Synthetic genetic networks}, doi = {10.1186/s12859-015-0728-4}, url = {http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0728-4}, author = {P. Bellot and Olsen, Catharina and Salembier, P. and Albert Oliveras and Meyer, P. E.} } @article {aBonet-Carne14a, title = {Quantitative Ultrasound Texture Analysis of Fetal Lungs To Predict Neonatal Respiratory Morbidity}, journal = {Ultrasound in Obstetrics and Gynecology, Wiley}, volume = {45}, year = {2015}, pages = {427{\textendash}433}, author = {E. Bonet-Carne and M. Palacio and T. Cobo and A. Perez-Moreno and M. Lopez and J. P. Piraquive and J. C. Ramirez and F. Marques and E. Gratacos} } @mastersthesis {xPorta, title = {Rapid Serial Visual Presentation for Relevance Feedback in Image Retrieval with EEG Signals}, year = {2015}, abstract = {

Studies: Bachelor degree in Science and Telecommunication Technologies Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Grade: A (9/10)

This thesis explores the potential of relevance feedback for image retrieval using EEG signals for human-computer interaction. This project aims at studying the optimal parameters of a rapid serial visual presentation (RSVP) of frames from a video database when the user is searching for an object instance. The simulations reported in this thesis assess the trade-off between using a small or a large amount of images in each RSVP round that captures the user feedback. While short RSVP rounds allow a quick learning of the user intention from the system, RSVP rounds must also be long enough to let users generate the P300 EEG signals which are triggered by relevant images. This work also addresses the problem of how to distribute potential relevant and non-relevant images in a RSVP round to maximize the probabilities of displaying each relevant frame separated at least 1 second from another relevant frame, as this configuration generates a cleaner P300 EEG signal. The presented simulations are based on a realistic set up for video retrieval with a subset of 1,000 frames from the TRECVID 2014 Instance Search task.

}, keywords = {eeg, feedback, image, relevance, retrieval}, author = {Porta, Sergi}, editor = {Amaia Salvador and Mohedano, Eva and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @mastersthesis {xLopez15, title = {Reconstrucci{\'o} de la forma del rostre a partir de contorns}, year = {2015}, abstract = {

La reconstrucci{\'o} i el modelatge de cares 3D s{\textquoteright}han convertit en els {\'u}ltims anys en una l{\'\i}nia d{\textquoteright}investigaci{\'o} molt activa a causa de la seva utilitzaci{\'o} en gran nombre d{\textquoteright}aplicacions com s{\'o}n el reconeixement facial en 3D, modelatge de cares en videojocs, cirurgia est{\`e}tica, etc. Durant les {\'u}ltimes d{\`e}cades s{\textquoteright}han desenvolupat m{\'u}ltiples t{\`e}cniques computacionals de reconstrucci{\'o} facial 3D. Una de les tecnologies m{\'e}s utilitzades est{\`a} basada en imatges 2D i m{\`e}todes estad{\'\i}stics (3D Morphable Models). L{\textquoteright}estimaci{\'o} de la forma de la cara abordada amb models estad{\'\i}stics, t{\'e} com a objectiu principal trobar un conjunt de par{\`a}metres de la cara que millor s{\textquoteright}ajusten a una imatge o un conjunt d{\textquoteright}imatges. Per a aquest projecte es disposa d{\textquoteright}un model estad{\'\i}stic capa{\c c} d{\textquoteright}estimar la forma 3D del rostre quan una cara o un conjunt de contorns 2D est{\`a} disponible des de m{\'u}ltiples punts de vista. Aquest model estima directament l{\textquoteright}estructura de la cara 3D mitjan{\c c}ant l{\textquoteright}{\'u}s d{\textquoteright}una matriu de regressi{\'o} constru{\"\i}da a trav{\'e}s de PLS (Partial Least Squares). Despr{\'e}s de la validaci{\'o} del model i els bons resultats obtinguts en la predicci{\'o} de subjectes sint{\`e}tics, aquest treball proposa un nou enfoc, entrenar el model amb subjectes reals a partir d{\textquoteright}una seq{\"u}{\`e}ncia de v{\'\i}deo. D{\textquoteright}aquesta forma s{\textquoteright}obtenen reconstruccions de la forma de la cara 3D amb dades reals. Per tant, l{\textquoteright}objectiu d{\textquoteright}aquest projecte {\'e}s la definici{\'o}, la implementaci{\'o} software i l{\textquoteright}an{\`a}lisi d{\textquoteright}un procediment que ens permeti ajustar un model estad{\'\i}stic facial tridimensional gen{\`e}ric a les caracter{\'\i}stiques facials espec{\'\i}fiques d{\textquoteright}un individu a partir dels contorns del seu rostre.

}, author = {Laia del Pino L{\'o}pez}, editor = {Ver{\'o}nica Vilaplana and Josep Ramon Morros} } @conference {cMaceira15, title = {Region-based depth map coding using a 3D scene representation}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing}, year = {2015}, month = {04/2015}, address = {Brisbane, Australia}, abstract = {

In 3D video, view synthesis is used to process new virtual views between encoded camera views. Errors in the coding of the depth maps introduce geometry inconsistencies in synthesized views. In this paper, a 3D plane representation of the scene is presented which improve the performance of current standard video codecs in the view synthesis domain. Depth maps are segmented into regions without sharp edges and represented with a plane in the 3D world scene coordinates. This 3D representation allows an efficient representation while preserving the 3D characteristics of the scene. Experimental results are provided obtaining gains from 10 to 40 \% in bitrate compared to HEVC.

}, author = {Maceira, M. and Morros, J.R. and Ruiz-Hidalgo, J.} } @mastersthesis {xGirbau15, title = {Region-based Particle Filter}, year = {2015}, abstract = {

In this project the implementation of a video object tracking technique based on a particle filter that uses the partitions of the various frames in the video has been tackled. This is an extension of the standard particle filter tracker in which unions of regions of the image are used to generate particles. By doing so, the tracking of the object of interest through the video sequence is expected to be done in a more accurate and robust way. One of the main parts of this video object tracker is a co-clustering technique that allows having an initial estimation of the object in the current frame, relying on the instance of the same object in a previous frame. While developing the object tracker, we realized the importance of this co-clustering technique, not only in the context of the current video tracker but as a basic tool for several of the research projects in the image group. Therefore, we decided to concentrate on the implementation of a generic, versatile co-clustering technique instead of the simple version that was necessary for the tracking problem. This way, the main goal of this project consists on implementing the co-clustering method presented in an accurate way while presenting a low computation time. Moreover, the complete Region-based particle filter for tracking purposes is presented. Therefore, the aim of this Final Degree Project is, mainly, to give a guideline to future researchers who will use this algorithm; to help understand and apply the mentioned co-clustering for any project in need of this method.

}, url = {http://upcommons.upc.edu/bitstream/handle/2099.1/25370/Andreu_Girbau_Xalabarder_TFG.pdf?sequence=4\&isAllowed=y}, author = {Girbau Xalabarder, A.}, editor = {David Varas and Marqu{\'e}s, F.} } @conference {Bell1512:Study, title = {Study of Normalization and Aggregation Approaches for Consensus Network Estimation}, booktitle = {2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Artificial Life (2015 IEEE ALIFE)}, year = {2015}, address = {Cape Town, South Africa}, abstract = {

Inferring gene regulatory networks from expression data is a very difficult problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that the different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises. In this paper, a common framework to standardize already proposed consensus methods is presented, and based on this framework different proposals are introduced and analyzed in two different scenarios: Homogeneous and Heterogeneous. The first scenario reflects situations where the networks to be aggregated are rather similar because the are obtained with inference algorithms working on the same data, whereas the second scenario deals with very diverse networks because various sources of data are used to generate the individual networks. A procedure for combining multiple network inference algorithms is analyzed in a systematic way. The results show that there is a very significant difference between these two scenarios, and that the best way to combine networks in the Heterogeneous scenario is not the most commonly used. We show in particular that aggregation in the Heterogeneous scenario can be very beneficial if the individual networks are combined with our new proposed method ScaleLSum.

}, author = {P. Bellot and Salembier, P. and Albert Oliveras and Meyer, P. E.} } @conference {cIndia15, title = {UPC System for the 2015 MediaEval Multimodal Person Discovery in Broadcast TV task}, booktitle = {MediaEval 2015 Workshop}, year = {2015}, month = {09/2015}, address = {Wurzen, Germany}, abstract = {

This paper describes a system to identify people in broadcast TV shows in a purely unsupervised manner. The system outputs the identity of people that appear, talk and can be identified by using information appearing in the show (in our case, text with person names). Three types of monomodal technologies are used: speech diarization, video diarization and text detection / named entity recognition. These technologies are combined using a linear programming approach where some restrictions are imposed.

}, author = {India, M. and David Varas and Ver{\'o}nica Vilaplana and Morros, J.R. and Hernando, J.} } @article {aTochon15, title = {On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images}, journal = {Remote Sensing of Environment}, volume = {159}, year = {2015}, month = {03/2015}, pages = {318-331}, abstract = {

The segmentation of remotely sensed images acquired over tropical forests is of great interest for numerous ecological applications, such as forest inventories or conservation and management of ecosystems, for which species classification techniques and estimation of the number of individuals are highly valuable inputs. In this paper, we propose a method for hyperspectral image segmentation, based on the binary partition tree (BPT) algorithm, and we apply it to two sites located in Hawaiian and Panamean tropical rainforests. Different strategies combining spatial and spectral dimensionality reduction are compared prior to the construction of the BPT. Various superpixel generation methods including watershed transformation and mean shift clustering are applied to decrease spatial dimensionality and provide an initial segmentation map. Principal component analysis is performed to reduce the spectral dimensionality and different combinations of principal components are compared. A non-parametric region model based on histograms, combined with the diffusion distance to merge regions, is used to build the BPT. An adapted pruning strategy based on the size discontinuity of the merging regions is proposed and compared with an already existing pruning strategy. Finally, a set of criteria to assess the quality of the tree segmentation is introduced. The proposed method correctly segmented up to 68\% of the tree crowns and produced reasonable patterns of the segmented landscapes.

}, issn = {0034-4257}, author = {G. Tochon and J.B. F{\'e}eret and Valero, S. and R.E. Martin and D.E. Knapp and Salembier, P. and Chanussot, J. and G. Asner} } @conference {cRoldan-Carlos, title = {Visual Information Retrieval in Endoscopic Video Archives}, booktitle = {IEEE/ACM International Workshop on Content-Based Multimedia Indexing - CBMI 2015 }, year = {2015}, month = {06/2015}, address = {Prague, Czech Republic}, abstract = {

In endoscopic procedures, surgeons work with live video strea\-ms from the inside of their subjects. A main source for documentation of procedures are still frames from the video, identified and taken during the surgery. However, with growing demands and technical means, the streams are saved to storage servers and the surgeons need to retrieve parts of the videos on demand. In this submission we present a demo application allowing for video retrieval based on visual features and late fusion, which allows surgeons to re-find shots taken during the procedure.

[Paper on arXiv]

[CBMI 2015 Conference website]

Presented in the Special Session on Medical Multimedia Processing (acceptance rate for special sessions= 55\%)

}, url = {http://arxiv.org/abs/1504.07874}, author = {Roldan-Carlos, Jennifer and Lux, Mathias and Xavier Gir{\'o}-i-Nieto and Pia Mu{\~n}oz-Trallero and Anagnostopoulos, Nektarios} } @conference {cMestre, title = {Visual Summary of Egocentric Photostreams by Representative Keyframes}, booktitle = {IEEE International Workshop on Wearable and Ego-vision Systems for Augmented Experience (WEsAX) 2015}, year = {2015}, month = {07/2015}, address = {Turin, Italy}, abstract = {

Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement).\ In this paper, we propose a new summarization method based on keyframes selection that\ uses visual features extracted by means of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the summaries.

}, keywords = {egocentric, keyframes, lifelogging, summarization}, url = {http://arxiv.org/abs/1505.01130}, author = {Bola{\~n}os, Marc and Mestre, Ricard and Talavera, Estefan{\'\i}a and Xavier Gir{\'o}-i-Nieto and Radeva, Petia} } @mastersthesis {xMestre, title = {Visual Summary of Egocentric Photostreams by Representative Keyframes}, year = {2015}, abstract = {

Studies: Bachelor degree in Engineering of Audiovisual Systems at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Grade: A with honors (9.6/10)

This Final Degree Work approach the problem of the visual summarization of sets of images captured by an eggocentric camera for lifelogging purposes. In first place we try to group the images (which represent the day of person{\textquoteright}s life) into distinguishable and significant esdeveniments. For this purpose, we use visual features extracted with the software Caffe. In second place, we explain the design of extraction techniques of the representative images through similarity graphs. Finally we analyze the assessment scores given by different users whom we presented the different visual summaries obtained in this project. We achieve a 60\% of favorable opinions of the quality of the visual summaries obtained with techniques developed in this project.

(This thesis report is writen in Catalan)

Aquest Treball de Final de Grau aborda el problema de resumir visualment conjunts d{\textquoteright}imatges capturats mitjançant una càmera egocèntrica per a propòsits de registre vital (lifelogging en anglès).\ En primer lloc s{\textquoteright}intenta agrupar les imatges (que representen el dia d{\textquoteright}una persona) per esdeveniments distingibles i significatius. Per fer-ho s{\textquoteright}utilitzen característiques visuals extretes amb el programari Caffe.\ En segon lloc s{\textquoteright}explica el disseny de tècniques de extracció d{\textquoteright}imatges representatives mitjançant grafs de similitud.\ Per últim s{\textquoteright}analitzen les puntuacions d{\textquoteright}avaluació donades per diversos usuaris als quals se{\textquoteright}ls han presentat els diferents resums visuals obtinguts en aquest projecte. S{\textquoteright}ha pogut assolir un 60\% d{\textquoteright}opinions favorables a la qualitat dels resums obtinguts amb tècniques desenvolupades en aquest treball.

}, keywords = {computer Vision, egocentric image, image processing, lifeblogging, summarization, wearable camera}, author = {Mestre, Ricard}, editor = {Xavier Gir{\'o}-i-Nieto} } @conference {cMartinez14, title = {3D Shape Reconstruction from a Humanoid Generated Video Sequence}, booktitle = {IEEE International Conference on Humanoid Robots}, year = {2014}, address = {Madrid}, abstract = {

This paper presents a strategy for estimating the\ geometry of an interest object from a monocular video sequence\ acquired by a walking humanoid robot. The problem is solved\ using a space carving algorithm, which relies on both the\ accurate extraction of the occluding boundaries of the object as well as the precise estimation of the camera pose for each video frame. For data acquisition, a monocular visual-based control has been developed that drives the trajectory of the robot around an object placed on a small table. Due to the stepping of the humanoid, the recorded sequence is contaminated with\ artefacts that affect the correct extraction of contours along the\ video frames. To overcome this issue, a method that assigns a fitness score for each frame is proposed, delivering a subset\ of camera poses and video frames that produce consistent 3D shape estimations of the objects used for experimental evaluation.

}, author = {Mart{\'\i}nez, P. A. and David Varas and Castel{\'a}n, M and Camacho, M and Marqu{\'e}s, F. and Arechavaleta, G.} } @mastersthesis {xQueralt14, title = {Automatic Human Detection and Tracking for Robust Video Sequence Annotation}, year = {2014}, author = {Ramon Llorca Queralt}, editor = {Morros, J.R. and David Varas} } @conference {cCarlier, title = {Click{\textquoteright}n{\textquoteright}Cut: Crowdsourced Interactive Segmentation with Object Candidates}, booktitle = {3rd International ACM Workshop on Crowdsourcing for Multimedia (CrowdMM)}, year = {2014}, month = {11/2014}, address = {Orlando, Florida (USA)}, abstract = {

This paper introduces Click{\textquoteright}n{\textquoteright}Cut, a novel web tool for interactive object segmentation addressed to crowdsourcing tasks. Click{\textquoteright}n{\textquoteright}Cut combines bounding boxes and clicks generated by workers to obtain accurate object segmentations. These segmentations are created by combining precomputed object candidates in a light computational fashion that allows an immediate response from the interface. Click{\textquoteright}n{\textquoteright}Cut has been tested with a crowdsourcing campaign to annotate a subset of the Berkeley Segmentation Dataset (BSDS). Results show competitive results with state of the art, especially in time to converge to a high quality segmentation. The data collection campaign included golden standard tests to detect cheaters.

[Related master thesis by Amaia Salvador]

[Related Phd thesis by Axel Carlier]

[CrowdMM website]

}, keywords = {Crowdsourcing, figure-ground segmentation, human computing, object candidates}, doi = {10.1145/2660114.2660125}, url = {http://dx.doi.org/10.1145/2660114.2660125}, author = {Carlier, Axel and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Marques, Oge and Charvillat, Vincent} } @mastersthesis {xGutierrez14, title = {Comparaci{\'o} d{\textquoteright}algoritmes de classificaci{\'o} de tipus de pla en imatges de futbol}, year = {2014}, abstract = {

The purpose of this project is to analyze and evaluate football image viewpoint classification algorithms and to implement, if it is possible, a set of upgrades to improve the results of this classification. In particular, the analysis of a publication of the State of the Art and its subsequent comparison to an algorithm created by the Image Processing Group (GPI) of the Polytechnic University of Catalonia is formulated. Sometimes, during the analysis process of sporting events is interesting to automate the extraction of semantic content. In this context, the algorithms compared in this project are football image viewpoint classification algorithms. To carry through the classification, these algorithms use different descriptors calculated on the images. This project originates from the need to compare the image processing group algorithm to present techniques, in order to assess the obtained results and have a better understanding of the current State of the Art. Thus, the State of the Art algorithm will be implemented, analyzed and improved if possible. Then, it will be compared to the one created by the image processing group using an extensive database previously selected. Finally, the results will be presented and analyzed.

}, url = {http://upcommons.upc.edu/bitstream/handle/2099.1/20890/Final_Report.pdf?sequence=4\&isAllowed=y}, author = {Coronado Guti{\'e}rrez, D}, editor = {David Varas and Marqu{\'e}s, F.} } @article {cBellot Pujalte14, title = {Efficient combination of pairwise feature networks}, journal = {JMLR: Workshop and Conference Proceedings }, volume = {46}, year = {2014}, month = {10/2015}, pages = {77 - 84}, publisher = {JMLR: Workshop and Conference Proceedings}, address = {Nancy, France}, abstract = {

This paper presents a novel method for the reconstruction of a neural network connectivity using calcium fluorescence data. We introduce a fast unsupervised method to integrate different networks that reconstructs structural connectivity from neuron activity. Our method improves the state-of-the-art reconstruction method General Transfer Entropy (GTE). We are able to better eliminate indirect links, improving therefore the quality of the network via a normalization and ensemble process of GTE and three new informative features. The approach is based on a simple combination of networks, which is remarkably fast. The performance of our approach is benchmarked on simulated time series provided at the connectomics challenge and also submitted at the public competition.

}, keywords = {connectomes, elimination of indirect links, network reconstruction algorithms}, author = {P. Bellot and Meyer, P. E.} } @mastersthesis {xSalvador, title = {Exploiting User Interaction and Object Candidates for Instance Retrieval and Object Segmentation}, year = {2014}, abstract = {

Author: Amaia Salvador-Aguilera

Advisors:\ Xavier Gir{\'o}-i-Nieto\ (UPC) and Kevin McGuinness (Dublin CIty University)

Degree:\ Master in Computer Vision\ (1 year)

Video: Thesis defense

This thesis addresses two of the main challenges nowadays for computer vision: object segmentation and visual instance retrieval. The methodologies proposed to solve both problems are based on the use of object candidates and human computation in the computer vision loop. In the object segmentation side, this work explores how human computation can be useful to achieve better segmentation results, by combining users{\textquoteright} traces with a segmentation algorithm based on object candidates. On the other hand, the instance retrieval problem is also addressed using object candidates to compute local features, and involving the user in the retrieval loop by applying relevance feedback strategies.

}, keywords = {computer Vision, human computing, instance search, object candidates, segmentation}, author = {Amaia Salvador}, editor = {Xavier Gir{\'o}-i-Nieto and Kevin McGuinness} } @article {aGiro-i-Nieto13, title = {From Global Image Annotation to Interactive Object Segmentation}, journal = {Multimedia Tools and Applications}, volume = {70}, year = {2014}, month = {05/2014}, chapter = {475}, abstract = {

This paper presents a graphical environment for the annotation of still images that works both at the global and local scales. At the global scale, each image can be tagged with positive, negative and neutral labels referred to a semantic class from an ontology. These annotations can be used to train and evaluate an image classifier. A finer annotation at a local scale is also available for interactive segmentation of objects. This process is formulated as a selection of regions from a precomputed hierarchical partition called Binary Partition Tree. Three different semi-supervised methods have been presented and evaluated: bounding boxes, scribbles and hierarchical navigation. The implemented Java source code is published under a free software license.

}, keywords = {annotation, Hierarchical, Interaction, Multiscale, segmentation}, doi = {10.1007/s11042-013-1374-3}, author = {Xavier Gir{\'o}-i-Nieto and Martos, Manel and Mohedano, Eva and Jordi Pont-Tuset} } @phdthesis {dPont-Tuset14, title = {Image Segmentation Evaluation and Its Application to Object Detection}, year = {2014}, month = {0/2014}, school = {Universitat Polit{\`e}cnica de Catalunya, BarcelonaTech}, address = {Barcelona}, abstract = {

The first two parts of this Thesis are focused on the study of the supervised evaluation of image segmentation algorithms. Supervised in the sense that the segmentation results are compared to a human-made annotation, known as ground truth, by means of different measures of similarity. The evaluation depends, therefore, on three main points.

First, the image segmentation techniques we evaluate. We review the state of the art in image segmentation, making an explicit difference between those techniques that provide a flat output, that is, a single clustering of the set of pixels into regions; and those that produce a hierarchical segmentation, that is, a tree-like structure that represents regions at different scales from the details to the whole image.

Second, ground-truth databases are of paramount importance in the evaluation. They can be divided into those annotated only at object level, that is, with marked sets of pixels that refer to objects that do not cover the whole image; or those with annotated full partitions, which provide a full clustering of all pixels in an image. Depending on the type of database, we say that the analysis is done from an object perspective or from a partition perspective.

Finally, the similarity measures used to compare the generated results to the ground truth are what will provide us with a quantitative tool to evaluate whether our results are good, and in which way they can be improved. The main contributions of the first parts of the thesis are in the field of the similarity measures.

First of all, from an object perspective, we review the existing measures to compare two object representations and show that some of them are equivalent. In order to evaluate full partitions and hierarchies against an object, one needs to select which of their regions form the object to be assessed. We review and improve these techniques by means of a mathematical model of the problem. This analysis allows us to show that hierarchies can represent objects much better with much less number of regions than flat partitions.

From a partition perspective, the literature about evaluation measures is large and entangled. Our first contribution is to review, structure, and deduplicate the measures available. We pro- vide a new measure that improves previous ones in terms of a set of qualitative and quantitative meta-measures. We also extend the measures on flat partitions to cover hierarchical segmentations.

The third part of this Thesis moves from the evaluation of image segmentation to its application to object detection. In particular, we build on some of the conclusions extracted in the first part to generate segmented object candidates. Given a set of hierarchies, we build the pairs and triplets of regions, we learn to combine the set from each hierarchy, and we rank them using low-level and mid-level cues. We conduct an extensive experimental validation that show that our method outperforms the state of the art in terms of object segmentation quality and object detection accuracy.

}, url = {http://hdl.handle.net/10803/134354}, author = {Jordi Pont-Tuset}, editor = {Marqu{\'e}s, F.} } @conference {cMcGuinness, title = {Insight Centre for Data Analytics (DCU) at TRECVid 2014: Instance Search and Semantic Indexing Tasks}, booktitle = {2014 TRECVID Workshop}, year = {2014}, month = {11/2014}, publisher = {National Institute of Standards and Technology (NIST)}, organization = {National Institute of Standards and Technology (NIST)}, address = {Orlando, Florida (USA)}, abstract = {

Insight-DCU participated in the instance search (INS) and semantic indexing (SIN) tasks in 2014. Two very different approaches were submitted for instance search, one based on features extracted using pre-trained deep convolutional neural networks (CNNs), and another based on local SIFT features, large vocabulary visual bag-of-words aggregation, inverted index-based lookup, and geometric verification on the top-N retrieved results. Two interactive runs and two automatic runs were submitted, the best interactive runs achieved a mAP of 0.135 and the best automatic 0.12. Our semantic indexing runs were based also on using convolutional neural network features, and on Support Vector Machine classifiers with linear and RBF kernels. One run was submitted to the main task, two to the no annotation task, and one to the progress task. Data for the no-annotation task was gathered from Google Images and ImageNet. The main task run has achieved a mAP of 0.086, the best no-annotation runs had a close performance to the main run by achieving a mAP of 0.080, while the progress run had 0.043.

[2014 TREC Video Retrieval Evaluation Notebook Papers and Slides]

Poster

}, url = {http://hdl.handle.net/2117/24915}, author = {Kevin McGuinness and Mohedano, Eva and Zhang, ZhenXing and Hu, Feiyan and Albatal, Rami and Gurrin, Cathal and O{\textquoteright}Connor, N. and Smeaton, Alan F. and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Ventura, C.} } @mastersthesis {xPereira, title = {An investigation of eye gaze tracking utilities in image object recognition}, year = {2014}, abstract = {

Computer vision has been one of the most revolutionary technologies of the last few decades. This project investigates how to improve an image recognition system (image classifier) using a not very exploded technology; eye gaze tracking. The aim of this project is to explore the benefits that this technology can bring to an image classifier. The experiment that is set in this project is to build a dataset with an eye tracking device and (using different sized cropped parts of the image based on the eye tracking data) see how the performance of an image classifier is affected with these images. The results are interesting. Since smaller images have to be processed by using this method, the system is more efficient. Regarding the performance, it is very similar to the one obtained without using any eye tracking data, so it is arguable to state that it presents an improvement, and opens new directions of investigation for future works.

Poster


}, author = {Imedio-Pereira, Sergi}, editor = {Mohedano, Eva and Xavier Gir{\'o}-i-Nieto and Albatal, Rami and O{\textquoteright}Connor, N.} } @mastersthesis {xManchon-Vizuete, title = {Low computational cost algorithms for photo clustering and mail signature detection in the cloud}, year = {2014}, abstract = {

Advisors: Xavier Gir{\'o}-i-Nieto (UPC) and Omar Pera (Pixable)

Degree: Electronic Engineering (5 years) at\ Telecom BCN-ETSETB\ (UPC)

Low computational cost algorithms for photo clustering and mail signature detection in the cloud from Xavi Gir{\'o}

This Final degree thesis summarizes the tasks that have been developed during an internship in Pixable Inc. in New York City together with the tasks related to the Me- diaeval 2013 evaluation campaign, where I participated with the team of Universitat Politecnica de Catalunya (UPC). The main focus of my work was on the Photofeed service, that is a photo archive service in the cloud.

The popularisation of the storage of photos on the cloud has opened new oppor- tunities and challenges for the organization and extension of photo collections. In my thesis I have developed a light computational solution for the clustering of web photos based on social events. The proposal combines a first oversegmentation of the photo collections of each user based on temporal cues, as previously proposed in the PhotoTOC algorithm [Platt et al, PACRIM 2003]. On a second stage, the resulting mini-clusters are merged based on contextual metadata such as geolocation, keywords and user IDs.

Closely relate to photo clustering we can study mail classification too. Additional tasks were developed for the Contactive company in this field. In order to solve the problems that Contactive was facing in mail analysis tasks, I developed methods for automatically identifying signature blocks and reply lines in plain-text email messages. This analysis has many potential applications, such as preprocessing email for text-to- speech systems; anonymization of email corpora; improving automatic content-based mail classifiers; and email threading. This method is based on applying machine learning methods to a sequential representation of an email message, in which each email is represented as a sequence of lines, and each line is represented as a set of features.

Final grade: A with honors (10/10)

Daniel Manchon
}, author = {Manchon-Vizuete, Daniel}, editor = {Xavier Gir{\'o}-i-Nieto and Pera, Omar} } @conference {cMorros14, title = {Materials transversals per a l{\textquoteright}aprenentatge actiu de les mat{\`e}ries de processat d{\textquoteright}imatge i v{\'\i}deo}, booktitle = {Congr{\'e}s Internacional de Doc{\`e}ncia Universit{\`a}ria i Innovaci{\'o} (CIDUI)}, year = {2014}, month = {07/2014}, address = {Tarragona, Spain}, abstract = {

This work aims to promote the cooperation and coordination between different image/video processing courses taught at the UPC in order to enhance the learning results. The main contributions are a) the creation of a common set of materials: graphical demonstrators, collections of problems, question banks, etc. and b) the design of strategies strategies to use this material in the development of generic and specific skills, with special emphasis on promoting independent learning

}, keywords = {image/video processing, Matlab demonstrators, question Banks, specific skills, teaching material}, author = {Morros, J.R. and Ver{\'o}nica Vilaplana and Ruiz-Hidalgo, J. and Casas, J. and Gasull, A. and Marqu{\'e}s, F. and M. Pard{\`a}s and Salembier, P.} } @book {bSchreer14, title = {Media Production, Delivery and Interaction for Platform Independent Systems}, volume = {ISBN 978-1-118-60533-2}, year = {2014}, publisher = {Wiley, ISBN 978-1-118-60533-2}, organization = {Wiley, ISBN 978-1-118-60533-2}, issn = {978-1-118-60533-2}, url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118605330,subjectCd-EEJ0.html}, author = {Schreer, O. and Macq, J. and Niamut, O. and Ruiz-Hidalgo, J. and Shirley, B. and Thallinger, G. and Thomas, G.} } @conference {cArbelaez14, title = {Multiscale Combinatorial Grouping}, booktitle = {Computer Vision and Pattern Recognition (CVPR)}, year = {2014}, abstract = {

We propose a unified approach for bottom-up hierarchical image segmentation and object candidate generation for recognition, called Multiscale Combinatorial Grouping (MCG). For this purpose, we first develop a fast normalized cuts algorithm. We then propose a high-performance hierarchical segmenter that makes effective use of multiscale information. Finally, we propose a grouping strategy that combines our multiscale regions into highly-accurate object candidates by efficiently exploring their combinatorial space. We conduct extensive experiments on both the BSDS500 and on the PASCAL 2012 segmentation datasets, showing that MCG produces state-of-the-art contours, hierarchical regions, and object candidates.

Code and pre-computed results available here.

}, author = {Pablo Arbelaez and Jordi Pont-Tuset and Barron, Jon and Marqu{\'e}s, F. and Jitendra Malik} } @conference {cMohedano, title = {Object segmentation in images using EEG signals}, booktitle = {ACM Multimedia}, year = {2014}, month = {11/2014}, address = {Orlando, Florida (USA)}, abstract = {

This paper explores the potential of brain-computer interfaces in segmenting objects from images. Our approach is centered around designing an effective method for displaying the image parts to the users such that they generate measurable brain reactions. When an image region, specifically a block of pixels, is displayed we estimate the probability of the block containing the object of interest using a score based on EEG activity. After several such blocks are displayed, the resulting probability map is binarized and combined with the GrabCut algorithm to segment the image into object and background regions. This study shows that BCI and simple EEG analysis are useful in locating object boundaries in images.

}, keywords = {Brain-computer interfaces, Electroencephalography, GrabCut algorithm, Interactive segmentation, Object segmentation, rapid serial visual presentation}, doi = {10.1145/2647868.2654896}, url = {http://arxiv.org/abs/1408.4363}, author = {Mohedano, Eva and Healy, Graham and Kevin McGuinness and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Smeaton, Alan F.} } @conference {cManchon-Vizuete, title = {Photo Clustering of Social Events by Extending PhotoTOC to a Rich Context}, booktitle = {ICMR 2014 Workshop on Social Events in Web Multimedia (SEWM)}, year = {2014}, month = {04/2014}, publisher = {ACM}, organization = {ACM}, address = {Glasgow, Scotland}, abstract = {

The popularisation of the storage of photos on the cloud has opened new opportunities and challenges for the organisation and extension of photo collections. This paper presents a light computational solution for the clustering of web photos based on social events. The proposal combines a first over-segmentation of the photo collections of each user based on temporal cues, as previously proposed in PhotoTOC. On a second stage, the resulting mini-clusters are merged based on contextual metadata such as geolocation, keywords and user IDs. Results indicate that, although temporal cues are very relevant for event clustering, robust solutions should also consider all these additional features.

[Conference website]

Photo Clustering of Social Events by Extending PhotoTOC to a Rich Context from Xavi Gir{\'o}

}, keywords = {Clustering, Event Detection, Photo Collections}, url = {http://hdl.handle.net/2117/23009}, author = {Manchon-Vizuete, Daniel and Gris-Sarabia, Irene and Xavier Gir{\'o}-i-Nieto} } @article {aBonet-Carne14, title = {Quantitative Ultrasound Texture Analysis of Fetal Lungs To Predict Neonatal Respiratory Morbidity}, journal = {Ultrasound in Obstetrics and Gynecology, Wiley}, volume = {44}, year = {2014}, author = {E. Bonet-Carne and M. Palacio and T. Cobo and A. Perez-Moreno and M. Lopez and J. P. Piraquive and J. C. Ramirez and F. Marques and E. Gratacos} } @conference {cVaras14, title = {Region-based Particle Filter for Video Object Segmentation}, booktitle = {CVPR - Computer Vision and Pattern Recognition}, year = {2014}, publisher = {IEEE}, organization = {IEEE}, address = {Ohio}, abstract = {

We present a video object segmentation approach that\ extends the particle filter to a region-based image representation. Image partition is considered part of the particle filter measurement, which enriches the available information and leads to a re-formulation of the particle filter.\ The prediction step uses a co-clustering between the previous image object partition and a partition of the current\ one, which allows us to tackle the evolution of non-rigid\ structures. Particles are defined as unions of regions in the\ current image partition and their propagation is computed\ through a single co-clustering. The proposed technique is\ assessed on the SegTrack dataset, leading to satisfactory\ perceptual results and obtaining very competitive pixel error rates compared with the state-of-the-art methods.

}, keywords = {co-clustering, particle filter, segmentation, tracking}, author = {David Varas and Marqu{\'e}s, F.} } @conference {cManchon-Vizuetea, title = {UPC at MediaEval 2014 Social Event Detection Task}, booktitle = {MediaEval 2014 Workshop}, year = {2014}, month = {10/2014}, publisher = {CEUR Workshop Proceedings}, organization = {CEUR Workshop Proceedings}, address = {Barcelona}, abstract = {

This document presents the contribution of the UPC team to the Social Event Detection (SED) Subtask 1 in MediaEval 2014. This contribution extends the solution tested in the previous year with a better optimization of the parameters that determine the clustering algorithm, and by introducing an additional pass that considers the merges of all pairs of mini-clusters generated during the two first passes. Our proposal also addresses the problem of incomplete metadata by generating additional textual tags based on geolocation and natural language processing techniques.

[MediaEval 2014 website]

}, url = {http://ceur-ws.org/Vol-1263/}, author = {Manchon-Vizuete, Daniel and Gris-Sarabia, Irene and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xMartos13, title = {Content-based Video Summarisation to Object Maps}, year = {2013}, abstract = {

Advisors: Xavier Gir{\'o}-i-Nieto\ and\ Horst Eidenberger

School: Vienna University of Technology (Austria)

The amount of digital video content available in the web is constantly increasing. Its handling requires efficient technologies: text search on large databases provides users a great amount of videos; the content results are accessible by a description. Users need a fast and visual way to access relevant video content effectively. Quick visualisation of content using static image summarisation is a sophisticated problem. However, it is worth it because it may solve video navigation problems. Users can very rapidly get an idea of the video with no need to browse through it with a sliding bar as normally done.

In this work a system for automatic video summarisation is developed. It creates an object map the segments of which are extracted from an input video. It allows enhancing video browsing and large video databases management generating a visual index so that the user can rapidly grasp the most relevant content. Finally, accessing them with a simple action requires several technologies that define a complex information processing.

Firstly, shot boundary detection algorithms are required to reduce time redundancy of the video. Secondly, different relevant objects are extracted from each keyframe (faces, cars, etc.). We also describe a workflow to train detection models using multiple open source solutions. Furthermore, faces are a particular and very relevant semantic class. For this reason, we use clustering methods in order to recognise them in an unsupervised recognition process. The image composition of all selected objects and faces is the final stage of the architecture. Composition is defined as the combination of distinct parts to form a whole, therefore, objects have to be rendered in the map in a visually attractive manner.

To validate our approach and assess end-user satisfaction, we conducted a user study in which we compare requirements collected by analysing related literature. We analyse redundancy and informativeness as well as pleasantness.

The results show that our approach effectively creates an image representation for videos and is able to summarise customisable content in an attractive way.


Manel Martos, "Content-based Video Summarization to Object Maps" from Image Processing Group on Vimeo.

}, url = {http://hdl.handle.net/2099.1/19359}, author = {Martos, Manel}, editor = {Xavier Gir{\'o}-i-Nieto and Eidenberger, Horst} } @mastersthesis {xSalvador13, title = {Crowdsourced Object Segmentation with a Game}, year = {2013}, abstract = {

Co-advised with Axel Carlier (INP Toulouse), Vincent Charvillat\ (INP Toulouse) and Oge Marques (Florida Atlantic University).

Amaia Salvador, "Crowdsourced Object Segmentation with a Game" from Image Processing Group on Vimeo.

}, author = {Amaia Salvador}, editor = {Xavier Gir{\'o}-i-Nieto and Carlier, Axel and Charvillat, Vincent and Marques, Oge} } @conference {cSalvador13 , title = {Crowdsourced Object Segmentation with a Game}, booktitle = {ACM Workshop on Crowdsourcing for Multimedia (CrowdMM)}, year = {2013}, month = {10/2013}, address = {Barcelona}, abstract = {

We introduce a new algorithm for image segmentation based on crowdsourcing through a game : Ask{\textquoteright}nSeek. The game provides information on the objects of an image, under the form of clicks that are either on the object, or on the background. These logs are then used in order to determine the best segmentation for an object among a set of candidates generated by the state-of-the-art CPMC algorithm. We also introduce a simulator that allows the generation of game logs and therefore gives insight about the number of games needed on an image to perform acceptable segmentation.

Amaia Salvador, "Crowdsourced Object Segmentation with a Game" from Xavi Gir{\'o}
}, isbn = {978-1-4503-2396-3}, doi = {http://dx.doi.org/10.1145/2506364.2506367}, url = {http://dx.doi.org/10.1145/2506364.2506367}, author = {Amaia Salvador and Carlier, Axel and Xavier Gir{\'o}-i-Nieto and Marques, Oge and Charvillat, Vincent} } @conference {cMaceira13, title = {Fusion of colour and depth partitions for depth map coding}, booktitle = {Digital Signal Processing}, year = {2013}, month = {07/2013}, address = {Santorini, Greece}, abstract = {

3D video coding includes the use of multiple color views and depth maps associated to each view. An adequate coding of depth maps should be adapted to the characteristics of depth maps: smooth regions and sharp edges. In this paper a segmentation-based technique is proposed for improving the depth map compression while preserving the main discontinuities that exploits the color-depth similarity of 3D video. An initial coarse depth map segmentation is used to locate the main discontinuities in depth. The resulting partition is improved by fusing a color partition. We assume that the color image is first encoded and available when the associated depth map is encoded, therefore the color partition can be segmented in the decoder without introducing any extra cost. A new segmentation criterion inspired by super-pixels techniques is proposed to obtain the color partition. Initial experimental results show similar compression efficiency to hevc with a big potential for further improvements.

}, keywords = {3DTV, Depth map coding}, doi = {10.1109/ICDSP.2013.6622781}, author = {Maceira, M. and Morros, J.R. and Ruiz-Hidalgo, J.} } @article {aAlcoverro13, title = {Gesture Control Interface for immersive panoramic displays}, journal = {Multimedia Tools and Applications}, year = {2013}, month = {07/2013}, pages = {1-27}, abstract = {

In this paper, we propose a gesture-based interface designed to interact with panoramic scenes. The system combines novel static gestures with a fast hand tracking method. Our proposal is to use static gestures as shortcuts to activate functionalities of the system (i.e. volume up/down, mute, pause, etc.), and hand tracking to freely explore the panoramic video. The overall system is multi-user, and incorporates a user identi cation module based on face recognition, which is able both to recognize returning users and to add new users online. The system exploits depth data, making it robust to challenging illumination conditions. We show through experimental results the performance of every component of the system compared to the state of the art. We also show the results of a usability study\ performed with several untrained users.

}, issn = {1380-7501}, doi = {10.1007/s11042-013-1605-7}, author = {Alcoverro, M. and Suau, X. and Morros, J.R. and L{\'o}pez-M{\'e}ndez, A. and A. Gil-Moreno and Ruiz-Hidalgo, J. and Casas, J.} } @article {aVentura13, title = {Improving retrieval accuracy of Hierarchical Cellular Trees for generic metric spaces}, journal = {Multimedia Tools and Applications}, year = {2013}, abstract = {

Metric Access Methods (MAMs) are indexing techniques which allow working in generic metric spaces. Therefore, MAMs are specially useful for Content-Based Image Retrieval systems based on features which use non Lp norms as similarity measures. MAMs naturally allow the design of image browsers due to their inherent hierarchical structure. The Hierarchical Cellular Tree (HCT), a MAM based indexing technique, provides the starting point of our work. In this paper, we describe some limitations detected in the original formulation of the HCT and propose some modifications to both the index building and the search algorithm. First, the covering radius, which is defined as the distance from the representative to the furthest element in a node,may not cover all the elements belonging to the node{\textquoteright}s subtree. Therefore, we propose to redefine the covering radius as the distance from the representative to the furthest element in the node{\textquoteright}s subtree. This new definition is essential to guarantee a correct construction of the HCT. Second, the proposed Progressive Query retrieval scheme can be redesigned to perform the nearest neighbor operation in a more efficient way. We propose a new retrieval scheme which takes advantage of the benefits of the search algorithm used in the index building. Furthermore, while the evaluation of the HCT in the original work was only subjective, we propose an objective evaluation based on two aspects which are crucial in any approximate search algorithm: the retrieval time and the retrieval accuracy. Finally, we illustrate the usefulness of the proposal by presenting some actual applications.

}, doi = {10.1007/s11042-013-1686-3}, author = {Ventura, C. and Ver{\'o}nica Vilaplana and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @mastersthesis {xMohedano13, title = {Investigating EEG for Saliency and Segmentation Applications in Image Processing}, year = {2013}, abstract = {

Advisors: Kevin McGuinness, Xavier Gir{\'o}-i-Nieto, Noel O{\textquoteright}Connor

School: Dublin City University (Ireland)

The main objective of this project is to implement a new way to compute saliency maps and to locate an object in an image by using a brain-computer interface. To achieve this, the project is centered in designing the proper way to display the different parts of the images to the users in such a way that they generate measurable reactions. Once an image window is shown, the objective is to compute a score based on the EEG activity and compare its result with the current automatic methods to estimate saliency maps. Also, the aim of this work is to use the EEG map as a seed for another segmentation algorithm that will extract the object from the background in an image. This study provides evidence that BCI are useful to find the location of the objects in a simple images via straightforward EEG analysis and this represents the starting point to locate objects in more complex images.

Related post on BitSearch.

Eva Mohedano, "Investigating EEG for Saliency and Segmentation Applications in Image Processing" from Xavi Gir{\'o}
}, keywords = {Brain-computer interfaces (BCI), Eelectroencephalography (EEG), rapid serial visual presentation (RSVP), saliency map, segmentation}, author = {Mohedano, Eva}, editor = {Kevin McGuinness and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @conference {cPont-Tuset13, title = {Measures and Meta-Measures for the Supervised Evaluation of Image Segmentation}, booktitle = {Computer Vision and Pattern Recognition (CVPR)}, year = {2013}, month = {06/2013}, abstract = {

This paper tackles the supervised evaluation of image segmentation algorithms. First, it surveys and structures the measures used to compare the segmentation results with a ground truth database; and proposes a new measure: the precision-recall for objects and parts. To compare the goodness of these measures, it defines three quantitative meta-measures involving six state of the art segmentation methods. The meta-measures consist in assuming some plausible hypotheses about the results and assessing how well each measure reflects these hypotheses. As a conclusion, this paper proposes the precision-recall curves for boundaries and for objects-and-parts as the tool of choice for the supervised evaluation of image segmentation. We make the datasets and code of all the measures publicly available.

Examples of the meta-measure principles: How good are\ the evaluation measures at distinguishing these pairs of partitions?

}, author = {Jordi Pont-Tuset and Marqu{\'e}s, F.} } @article {aMolina11 , title = {Real-time user independent hand gesture recognition from time-of-flight camera video using static and dynamic models}, journal = {Machine vision and applications}, volume = {24}, year = {2013}, month = {08/2011}, pages = {187{\textendash}204}, chapter = {187}, abstract = {

The use of hand gestures offers an alternative to the commonly used human computer interfaces, providing a more intuitive way of navigating among menus and multimedia applications. This paper presents a system for hand gesture recognition devoted to control windows applications. Starting from the images captured by a time-of-flight camera (a camera that produces images with an intensity level inversely proportional to the depth of the objects observed) the system performs hand segmentation as well as a low-level extraction of potentially relevant features which are related to the morphological representation of the hand silhouette. Classification based on these features discriminates between a set of possible static hand postures which results, combined with the estimated motion pattern of the hand, in the recognition of dynamic hand gestures. The whole system works in real-time, allowing practical interaction between user and application.

}, issn = {0932-8092}, doi = {10.1007/s00138-011-0364-6}, url = {http://www.springerlink.com/content/062m51v58073572h/fulltext.pdf}, author = {Molina, J. and Escudero-Vi{\~n}olo, M. and Signorelo, A. and M. Pard{\`a}s and Ferran, C. and Bescos, J. and Marqu{\'e}s, F. and Mart{\'\i}nez, J.} } @inbook {bLeon13, title = {Region-based caption text extraction}, booktitle = {Lecture Notes in Electrical Engineering (Analysis, Retrieval and Delivery of Multimedia Content)}, volume = {158}, year = {2013}, month = {07/2012}, pages = {21-36}, publisher = {Springer}, organization = {Springer}, address = {New York}, abstract = {

This chapter presents a method for caption text detection. The proposed method will be included in a generic indexing system dealing with other semantic concepts which are to be automatically detected as well. To have a coherent detection system, the various object detection algorithms use a common image description, a hierarchical region-based image model. The proposed method takes advantage of texture and geometric features to detect the caption text. Texture features are estimated using wavelet analysis and mainly applied for\ text candidate spotting. In turn,\ text characteristics verification\ relies on geometric features, which are estimated exploiting the region-based image model. Analysis of the region hierarchy provides the final caption text objects. The final step of\ consistency analysis for output\ is performed by a binarization algorithm that robustly estimates the thresholds on the caption text area of support.

}, keywords = {Text detection}, isbn = {978-1-4614-3830-4}, doi = {10.1007/978-1-4614-3831-1_2}, author = {Le{\'o}n, M. and Ver{\'o}nica Vilaplana and Gasull, A. and Marqu{\'e}s, F.} } @conference {cVilaplana13, title = {Salient Object Detection on a Hierarchy of Image Partitions}, booktitle = {IEEE Int. Conf. in Image Processing, ICIP 2013}, year = {2013}, month = {09/2013}, address = {Melbourne, Australia}, author = {Ver{\'o}nica Vilaplana and G. Muntaner} } @article {aTochon13, title = {Segmentation hyperspectrales de forets tropicales par arbres de partition binaires}, journal = {Revue fran{\c c}aise de photogramm{\'e}trie et de t{\'e}l{\'e}d{\'e}tection}, volume = {202}, year = {2013}, month = {May 2013}, pages = {55-65}, author = {G. Tochon and J.B. Feret and Valero, S. and R.E. Martin and R. Tupayachi and Chanussot, J. and Salembier, P. and G. Asner} } @conference {cNiamut13, title = {Towards A Format-agnostic Approach for Production, Delivery and Rendering of Immersive Media}, booktitle = {ACM Multimedia Systems}, year = {2013}, month = {3/2013}, address = {Oslo, Norway}, abstract = {

The media industry is currently being pulled in the often-opposing directions of increased realism (high resolution, stereoscopic, large screen) and personalization (selection and control of content, availability on many devices). We investigate the feasibility of an end-to-end format-agnostic approach to support both these trends. In this paper, different aspects of a format- agnostic capture, production, delivery and rendering system are discussed. At the capture stage, the concept of layered scene representation is introduced, including panoramic video and 3D audio capture. At the analysis stage, a virtual director component is discussed that allows for automatic execution of cinematographic principles, using feature tracking and saliency detection. At the delivery stage, resolution-independent audiovisual transport mechanisms for both managed and unmanaged networks are treated. In the rendering stage, a rendering process that includes the manipulation of audiovisual content to match the connected display and loudspeaker properties is introduced. Different parts of the complete system are revisited demonstrating the requirements and the potential of this advanced concept.

}, author = {Niamut, O. and Kaiser, R. and Kienast, G. and Kochale, A. and Spille, J. and Schreer, O. and Ruiz-Hidalgo, J. and Macq, J. and Shirley, B.} } @conference {cManchon-Vizuete13, title = {UPC at MediaEval 2013 Social Event Detection Task}, booktitle = {MediaEval 2013 Workshop}, year = {2013}, month = {10/2013}, publisher = {CEUR Workshop Proceedings}, organization = {CEUR Workshop Proceedings}, address = {Barcelona, Catalonia}, abstract = {

These working notes present the contribution of the UPC team to the Social Event Detection (SED) task in MediaEval 2013. The proposal extends the previous PhotoTOC work in the domain of shared collections of photographs stored in cloud services. An initial over-segmentation of the photo collection is later refined by merging pairs of similar clusters.

Xavier Gir{\'o}-i-Nieto, "UPC at MediaEval Social Event Detection 2013" from Xavi Gir{\'o}
}, keywords = {Benchmark, Event Detection, Photo Collections}, url = {http://ceur-ws.org/Vol-1043/mediaeval2013_submission_34.pdf}, author = {Manchon-Vizuete, Daniel and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xVentura13, title = {Visual Object Analysis Using Regions and Interest Points}, year = {2013}, abstract = {

This dissertion research explores two of the most-used image models for object detection, 3D reconstruction, visual search: region-based and interest-points image representations; and will try to provide a new image model to take advantage of the strengths and overcome the weaknesses of both approaches. More specifically, we will focus on the gPb-owt-ucm segmentation algorithm and the SIFT local features since they are the most contrasted techniques in their respective fields. Furthermore, using an object retrieval benchmark, this dissertation research will analyze three basic questions: (i) the usefulness of an interest points hierarchy based on a contour strength signal, (ii) the influence of the context on both interest points location and description, and (iii) the analysis of regions as spatial support for bundling interest points.\ 

}, author = {Ventura, C.}, editor = {Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @conference {cMaceira12, title = {Depth map coding based on a optimal hierarchical region representation}, booktitle = {3DTV Conference}, year = {2012}, month = {10/2012}, publisher = {IEEE}, organization = {IEEE}, address = {Zurich, Switzerland}, abstract = {

Multiview color information used jointly with depth maps is a widespread technique for 3D video. Using\ this depth information, 3D functionalities such as free view point video can be provided by means of\ depth-image-based rendering techniques. In this paper, a new technique to encode depth maps is\ proposed. Based on the usually smooth structure and the sharp edges of depth map, our proposal segments\ the depth map into homogeneous regions of arbitrary shape and encodes the contents of these regions\ using different texture coding strategies. An optimal lagrangian approach is applied to the hierarchical\ region representation provided by our segmentation technique. This approach automatically selects the\ best encoding strategy for each region and the optimal partition to encode the depth map. To avoid the\ high coding costs of coding the resulting partition, a prediction is made using the associated decoded\ color image.

}, keywords = {3DTV, Depth map coding, depth/texture compresion, rate-distortion optimization, Shape-adaptive DCT}, isbn = {978-1-4673-4903-1}, doi = {10.1109/3DTV.2012.6365481}, author = {Maceira, M. and Ruiz-Hidalgo, J. and Morros, J.R.} } @inbook {bVentura12, title = {Hierarchical Navigation and Visual Search for Video Keyframe Retrieval}, booktitle = {Advances in Multimedia Modeling}, series = {Lecture Notes in Computer Science}, volume = {7131}, year = {2012}, pages = {652-654}, publisher = {Springer Berlin / Heidelberg}, organization = {Springer Berlin / Heidelberg}, abstract = {

This work presents a browser that supports two strategies for video browsing: the navigation through visual hierarchies and the retrieval of similar images. The input videos are firstly processed by a keyframe extractor to reduce the temporal redundancy and decrease the number of elements to consider. These generated keyframes are hierarchically clustered with the Hierachical Cellular Tree (HCT) algorithm, an indexing technique that also allows the creation of data structures suitable for browsing. Different clustering criteria are available, in the current implementation, based on four MPEG-7 visual descriptors computed at the global scale. The navigation can directly drive the user to find the video timestamps that best match the query or to a keyframe which is globally similar in visual terms to the query. In the latter case, a visual search engine is also available to find other similar keyframes, based as well on MPEG-7 visual descriptors.

Winners of the Novice Run at the VideoBrowser Showdown 2012 at the 18th International Conference on MultiMedia Modeling, Kalgenfurt, Austria, January 4-6, 2012.

}, keywords = {hierarchical navigation, image retrieval, video browser}, isbn = {978-3-642-27354-4}, doi = {10.1007/978-3-642-27355-1_67}, url = {http://dx.doi.org/10.1007/978-3-642-27355-1_67}, author = {Ventura, C. and Martos, Manel and Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @inbook {bCalderero12 , title = {Image Analysis and Understanding Based on Information Theoretical Region Merging Approaches for Segmentation and Cooperative Fusion}, booktitle = {Handbook of Research on Computational Intelligence for Engineering, Science, and Business}, year = {2012}, pages = {75-121}, publisher = {IGI Global}, organization = {IGI Global}, chapter = {4}, abstract = {

This chapter addresses the automatic creation of simplified versions of the image, known as image segmentation or partition, preserving the most semantically relevant information of the image at different levels of analysis. From a semantic and practical perspective, image segmentation is a first and key step for image analysis and pattern recognition since region-based image representations provide a first level of abstraction and a reduction of the number of primitives, leading to a more robust estimation of parameters and descriptors. The proposed solution is based on an important class of hierarchical bottom-up segmentation approaches, known as region merging techniques. These approaches naturally provide a bottom-up hierarchy, more suitable when no a priori information about the image is available, and an excellent compromise between efficiency of computation and representation. The chapter is organized in two parts dealing with the following objectives: (i) provide an unsupervised solution to the segmentation of generic images; (ii) design a generic and scalable scheme to automatically fuse hierarchical segmentation results that increases the robustness and accuracy of the final solution

}, isbn = {978-1-466-62518-1}, doi = {10.4018/978-1-4666-2518-1.ch004}, author = {Calderero, F. and Marqu{\'e}s, F.} } @conference {cGiro-i-Nieto12a, title = {Interactive segmentation and tracking of video objects}, booktitle = {Image Analysis for Multimedia Interactive Services (WIAMIS), 2012 13th International Workshop on}, year = {2012}, month = {05/2012}, publisher = {IEEE}, organization = {IEEE}, address = {Dublin, Ireland}, abstract = {

This paper describes a mechanism to interactively segment objects from a sequence of video frames. The extracted object can be later embedded in a different background, associated to local scale metadata or used to train an automatic object detector. The workflow requires the interaction of the user at two stages: the temporal segmentation of the frames contain- ing the object and the generation of an object mask to initial- ize a video tracker. The mask is defined as a combination of regions generated by an image segmentation algorithm. This framework has been integrated in an annotation tool available to the public.\ 

}, keywords = {Image segmentation, Object segmentation, Proposals, semantics, Signal processing algorithms, Video sequences, Visualization}, doi = {10.1109/WIAMIS.2012.6226749}, url = {http://dx.doi.org/10.1109/WIAMIS.2012.6226749}, author = {Xavier Gir{\'o}-i-Nieto and Martos, Manel} } @conference {cGiro-i-Nieto12b, title = {Multiscale annotation of still images with GAT}, booktitle = {Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications}, year = {2012}, publisher = {ACM}, organization = {ACM}, address = {Capri, Italy}, abstract = {

This paper presents GAT, a Graphical Annotation Tool for still images that works both at the global and local scales. This interface has been designed to assist users in the an- notation of images with relation to the semantic classes de- scribed in an ontology. Positive, negative and neutral labels can be assigned to both the whole images or parts of them. The user interface is capable of exploiting segmentation data to assist in the selection of objects. Moreover, the annota- tion capabilities are complemented with additional function- alities that allow the creation and evaluation of an image classifier. The implemented Java source code is published under a free software license.\ 

}, keywords = {annotation, image, interactive, segmentation, semantics}, isbn = {978-1-4503-1405-3}, doi = {10.1145/2304496.2304497}, url = {http://doi.acm.org/10.1145/2304496.2304497}, author = {Xavier Gir{\'o}-i-Nieto and Martos, Manel} } @article {aCalderero12, title = {Multispectral Cooperative Partition Sequence Fusion for Joint Classification and Hierarchical Segmentation}, journal = {Geoscience and Remote Sensing Letters, IEEE}, volume = {9}, year = {2012}, pages = {1012-1016}, abstract = {

In this letter, a region-based fusion methodology is presented for joint classification and hierarchical segmentation of specific ground cover classes from high-spatial-resolution remote sensing images. Multispectral information is fused at the partition level using nonlinear techniques, which allows the different relevance of the various bands to be fully exploited. A hierarchical segmentation is performed for each individual band, and the ensuing segmentation results are fused in an iterative and cooperative way. At each iteration, a consensus partition is obtained based on information theory and is combined with a specific ground cover classification. Here, the proposed approach is applied to the extraction and segmentation of vegetation areas. The result is a hierarchy of partitions with the most relevant information of the vegetation areas at different levels of resolution. This system has been tested for vegetation analysis in high-spatial-resolution images from the QuickBird and GeoEye satellites.

}, keywords = {GeoEye satellite, geophysical image processing, geophysical techniques, ground cover classification, hierarchical segmentation, high-spatial-resolution remote sensing images, image classification, image fusion, image region analysis, Image segmentation, information fusion, information theory, joint classification, Joints, Merging, multispectral cooperative partition sequence fusion, multispectral images, multispectral information, nonlinear techniques, partition level, QuickBird satellite, region merging, region-based fusion methodology, Remote sensing, Spatial resolution, specific ground cover classes, Vegetation mapping}, issn = {1545-598X}, doi = {10.1109/LGRS.2012.2188776}, author = {Calderero, F. and F. Eugenio and Marcello, J. and Marqu{\'e}s, F.} } @inbook {bNavarro12, title = {Multi-view Body Tracking with a Detector-Driven Hierarchical Particle Filter}, booktitle = {Lecture Notes in Computer Science: Articulated Motion and Deformable Objects}, series = {Lecture Notes in Computer Science}, volume = {7378}, year = {2012}, pages = {82-91}, publisher = {Springer }, organization = {Springer }, address = {Berlin / Heidelberg}, abstract = {

In this paper we present a novel approach to markerless human motion capture that robustly integrates body part detections in multiple views. The proposed method fuses cues from multiple views to enhance the propagation and observation model of particle filtering methods aiming at human motion capture. We particularize our method to improve arm tracking in the publicly available IXMAS dataset. Our experiments show that the proposed method outperforms other state-of-the-art approaches.

}, isbn = {978-3-642-31566-4}, doi = {10.1007/978-3-642-31567-1_8}, author = {Navarro, Sergio and L{\'o}pez-M{\'e}ndez, A. and Alcoverro, M. and Casas, J.}, editor = {Perales, Francisco and Fisher, Robert and Moeslund, Thomas} } @article {aRuiz-Hidalgo12, title = {Multiview depth coding based on combined color/depth segmentation}, journal = {Journal of visual communication and image representation}, volume = {23}, number = {1}, year = {2012}, pages = {42{\textendash}52}, abstract = {

In this paper a new coding method for multiview depth video is presented. Considering the smooth structure and sharp edges of depth maps, a segmentation based approach is proposed. This allows further preserving the depth contours thus introducing fewer artifacts in the depth perception of the video. To reduce the cost associated with partition coding, an estimation of the depth partition is built using the decoded color view segmentation. This estimation is refined by sending some complementary information about the relevant differences between color and depth partitions. For coding the depth content of each region, a decomposition into orthogonal basis is used in this paper although similar decompositions may be also employed. Experimental results show that the proposed segmentation based depth coding method outperforms H.264/AVC and H.264/MVC by more than 2dB at similar bitrates.

}, keywords = {3DTV, Depth map, multiview video coding, virtual view}, issn = {1047-3203}, doi = {10.1016/j.jvcir.2011.08.001}, url = {http://www.sciencedirect.com/science/article/pii/S1047320311001040}, author = {Ruiz-Hidalgo, J. and Morros, J.R. and Aflaki, P. and Calderero, F. and Marqu{\'e}s, F.} } @phdthesis {dGiro-i-Nieto12, title = {Part-Based Object Retrieval With Binary Partition Trees}, volume = {Phd}, year = {2012}, month = {05/2012}, pages = {215}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, address = {Barcelona, Catalonia}, abstract = {

This thesis addresses the problem of visual object retrieval, where a user formulates a query to an image database by providing one or multiple examples of an object of interest. The presented techniques aim both at finding those images in the database that contain the object as well as locating the object in the image and segmenting it from the background.

Every considered image, both the ones used as queries and the ones contained in the target database, is represented as a Binary Partition Tree (BPT), the hierarchy of regions previously proposed by Salembier and Garrido (2000). This data structure offers multiple opportunities and challenges when applied to the object retrieval problem.

One application of BPTs appears during the formulation of the query, when the user must interactively segment the query object from the background. Firstly, the BPT can assist in adjusting an initial marker, such as a scribble or bounding box, to the object contours. Secondly, BPT can also define a navigation path for the user to adjust an initial selection to the appropriate scale.

The hierarchical structure of the BPT is also exploited to extract a new type of visual words named Hierarchical Bag of Regions (HBoR). Each region defined in the BPT is char- acterized with a feature vector that combines a soft quantization on a visual codebook with an ecient bottom-up computation through the BPT. These features allow the definition of a novel feature space, the Parts Space, where each object is located according to the parts that compose it.

HBoR features have been applied to two scenarios for object retrieval, both of them solved by considering the decomposition of the objects in parts. In the first scenario, the query is formulated with a single object exemplar which is to be matched with each BPT in the target database. The matching problem is solved in two stages: an initial top-down one that assumes that the hierarchy from the query is respected in the target BPT, and a second bottom-up one that relaxes this condition and considers region merges which are not in the target BPT.

The second scenario where HBoR features are applied considers a query composed of several visual objects, such as a person, a bottle or a logo. In this case, the provided exemplars are considered as a training set to build a model of the query concept. This model is composed of two levels, a first one where each part is modelled and detected separately, and a second one\ that characterises the combinations of parts that describe the complete object. The analysis process exploits the hierarchical nature of the BPT by using a novel classifier that drives an efficient top-down analysis of the target BPTs.\ \ 

Xavier Gir{\'o}-i-Nieto, "Part-based Object Retrieval with Binary Partition Trees" from Xavi Gir{\'o}
}, url = {http://hdl.handle.net/10803/108909}, author = {Xavier Gir{\'o}-i-Nieto}, editor = {Marqu{\'e}s, F. and Chang, Shih-Fu} } @conference {cIrurueta12, title = {PROMEDS: An adaptive robust fundamental matrix estimation approach}, booktitle = {3DTV Conference}, year = {2012}, month = {09/2012}, publisher = {IEEE}, organization = {IEEE}, address = {Zurich, Switzerland}, abstract = {

Accurate fundamental matrix estimation from computed correspondences is hard to achieve depending on the constraints on computational time and available data (i.e. correspondences and quality scores). Several algorithms exist for this task, like the 8-points, the 7-points algorithm or robust methods such as RANSAC, MSAC or LMedS. Robust methods are capable of discriminating correspondence outliers, thus, obtaining better results. Additionally, some variations of the previous methods have been proposed. For instance PROSAC is an improvement of RANSAC which takes into account additional information of the quality of the matches to largely reduce the computational cost of the fundamental matrix estimation process. This work proposes a new robust method for fundamental matrix estimation that combines the benefits of PROSAC and LMedS algorithms, namely improved quality, reduced computational time and less parameters to adjust.

}, keywords = {Fundamental Matrix, LMedS, PROSAC}, doi = {https://doi.org/10.1109/3DTV.2012.6365452}, author = {Irurueta, A. and Morros, J.R.} } @conference {cVaras12a, title = {A Region-Based Particle Filter for Generic Object Tracking and Segmentation}, booktitle = {ICIP - International Conference on Image Processing}, year = {2012}, month = {09/2012}, address = {Orlando}, abstract = {

In this work we present a region-based particle filter for generic object tracking and segmentation. The representation of the object in terms of regions homogeneous in color allows the proposed algorithm to robustly track the object and accurately segment its shape along the sequence. Moreover, this segmentation provides a mechanism to update the target model and allows the tracker to deal with color and shape variations of the object. The performance of the algorithm has been tested using the LabelMe Video public database.

The experiments show satisfactory results in both tracking and segmentation of the object without an important increase of the computational time due to an efficient computation of the image partition.\ 

}, doi = {http://dx.doi.org/10.1109/ICIP.2012.6467114}, author = {David Varas and Marqu{\'e}s, F.} } @conference {cJimenez12, title = {Registration of Multi-Modal Neuroimaging Datasets by Considering the Non-Overlapping Field of View into the NMI Calculation}, booktitle = {IEEE International Symposium on Biomedical Imaging, ISBI 2012}, year = {2012}, address = {Barcelona, Spain}, author = {Jim{\'e}nez, X and Figueiras, F and Marqu{\'e}s, F. and Salembier, P. and Herance, R and Rojas, S and Mill{\'a}n, O and Pareto, D and Domingo Gispert, J} } @inbook {bCarcel12, title = {Rich Internet Application for Semi-automatic Annotation of Semantic Shots on Keyframes}, booktitle = {Computational Intelligence for Multimedia Understanding}, volume = {7242}, number = {Lecture Notes in Computer Science}, year = {2012}, pages = {172-182}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, address = {Pisa, Italy}, abstract = {

This paper describes a system developed for the semi- automatic annotation of keyframes in a broadcasting company. The tool aims at assisting archivists who traditionally label every keyframe manually by suggesting them an automatic annotation that they can intuitively edit and validate. The system is valid for any domain as it uses generic MPEG-7 visual descriptors and binary SVM classifiers. The classification engine has been tested on the multiclass problem of semantic shot detection, a type of metadata used in the company to index new con- tent ingested in the system. The detection performance has been tested in two different domains: soccer and parliament. The core engine is accessed by a Rich Internet Application via a web service. The graphical user interface allows the edition of the suggested labels with an intuitive drag and drop mechanism between rows of thumbnails, each row representing a different semantic shot class. The system has been described as complete and easy to use by the professional archivists at the company.

[Blog]

Rich Internet Application for Semi-Automatic Annotation of Semantic Shots on Keyframes from Xavi Gir{\'o}
}, keywords = {annotation, classification, MPEG-7 visual descriptors, RIA, semantic shot}, isbn = {978-3-642-32435-2}, doi = {10.1007/978-3-642-32436-9_15}, url = {http://www.springerlink.com/content/x34632125j381045/}, author = {Carcel, Elisabet and Martos, Manel and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cPont-Tuset12, title = {Supervised Assessment of Segmentation Hierarchies}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2012}, month = {01/2012}, abstract = {

This paper addresses the problem of the supervised assessment of hierarchical region-based image representations. Given the large amount of partitions represented in such structures, the supervised assessment approaches in\ the literature are based on selecting a reduced set of representative partitions and\ evaluating their quality. Assessment results, therefore, depend on the partition selection strategy used. Instead, we propose to find the partition in the tree that best\ matches the ground-truth partition, that is, the upper-bound partition selection.

We show that different partition selection algorithms can lead to different conclusions regarding the quality of the assessed trees and that the upper-bound partition\ selection provides the following advantages: 1) it does not limit the assessment\ to a reduced set of partitions, and 2) it better discriminates the random trees from\ actual ones, which reflects a better qualitative behavior. We model the problem as\ a Linear Fractional Combinatorial Optimization (LFCO) problem, which makes\ the upper-bound selection feasible and efficient.

[Poster]

}, doi = {10.1007/978-3-642-33765-9_58}, author = {Jordi Pont-Tuset and Marqu{\'e}s, F.} } @conference {cMohedano12, title = {Teaching Students to Teach Computers}, booktitle = { International Congress on University Teaching and Innovation (CIDUI)}, year = {2012}, month = {07/2012}, abstract = {

This paper presents a Problem-Based Learning activity that introduce machine learning contents in a study plan that is based on electronics and signal processing subjects. The activity proposes students to follow the same evaluation protocols adopted in scientific challenges, where differents research groups test their own machine learning techniques on a common dataset and evaluation metrics. Teams of students adopt the role of a research group to develop their novel solution for classifying images depending on\ whether they represent a certain semantic concepts. Student must implement software solutions that are tested and presented in class to prove their performance and originalty. This paper includes a detailed evaluation of the workload and satisfaction activity, based on questionaries answered by the students.

}, keywords = {groups, machine learning, problem-based learning}, url = {http://hdl.handle.net/2117/16354}, author = {Mohedano, Eva and Xavier Gir{\'o}-i-Nieto} } @conference {cPont-Tuset12a, title = {Upper-bound assessment of the spatial accuracy of hierarchical region-based image representations}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, year = {2012}, month = {03/2012}, pages = {865-868}, abstract = {

Hierarchical region-based image representations are versatile tools for segmentation, filtering, object detection, etc.\ The evaluation of their spatial accuracy has been usually performed assessing the final result of an algorithm based on\ this representation.\ Given its wide applicability, however, a direct supervised assessment, independent of any application, would be desirable and fair.

A brute-force assessment of all the partitions represented in the hierarchical structure would be a correct approach,\ but as we prove formally, it is computationally unfeasible.\ This paper presents an efficient algorithm to find the upper-bound performance of the representation and we show\ that the previous approximations in the literature can fail at finding this bound.

}, isbn = {978-1-4673-0044-5}, doi = {10.1109/ICASSP.2012.6288021}, author = {Jordi Pont-Tuset and Marqu{\'e}s, F.} } @book {eRoca11, title = {14{\`e} Premi a la Qualitat en la Doc{\`e}ncia Universit{\`a}ria 2011}, year = {2011}, url = {http://cataleg.upc.edu/record=b1395249~S1*cat}, author = {Roca, E. and Marqu{\'e}s, F.} } @conference {cNiamut11, title = {Advanced visual rendering, gesture-based interaction and distributed delivery for immersive and interactive media services}, booktitle = {International Broadcasting Convention 2011}, year = {2011}, pages = {1{\textendash}8}, isbn = {0780388747}, url = {http://www.ibc.org/page.cfm/Action=Visitor/VisitorID=2851/PageOption=Seminar_1/libEntryID=15}, author = {Niamut, O. and Kochale, A. and Ruiz-Hidalgo, J. and Macq, J. and Kienast, G.} } @article {aTenorio11, title = {Correlation between a semiautomated method based on ultrasound texture analysis and standard ultrasound diagnosis using white matter damage in preterm neonates as a model}, journal = {Journal of ultrasound in medicine}, volume = {30}, number = {10}, year = {2011}, pages = {1365{\textendash}1377}, issn = {0278-4297}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21968487}, author = {Tenorio, V. and Bonet, E. and Botet, F. and Marqu{\'e}s, F. and Amat, I. and Gratacos, E.} } @conference {cGiro-i-Nieto11, title = {Diversity ranking for video retrieval from a broadcaster archive}, booktitle = {1st ACM International Conference on Multimedia Retrieval (ICMR {\textquoteright}11)}, year = {2011}, pages = {1{\textendash}8}, abstract = {

Video retrieval through text queries is a very common practice in broadcaster archives. The query keywords are compared to the metadata labels that documentalists have previously associated to the video assets. This paper focuses on a ranking strategy to obtain more relevant keyframes among the top hits of the results ranked lists but, at the same time, keeping a diversity of video assets. Previous solutions based on a random walk over a visual similarity graph have been modified to increase the asset diversity by filtering the edges between keyframes depending on their asset. The random walk algorithm is applied separately for ever visual feature to avoid any normalization issue between visual similarity metrics. Finally, this work evaluates performance with two separate metrics: the relevance is measured by the Average Precision and the diversity is assessed by the Average Diversity, a new metric presented in this work.

}, isbn = {978-1-4503-0336-1}, doi = {10.1145/1991996.1992052}, url = {http://dx.doi.org/10.1145/1991996.1992052}, author = {Xavier Gir{\'o}-i-Nieto and Alfaro, M. and Marqu{\'e}s, F.} } @article {aTello11, title = {Edge enhancement algorithm based on the wavelet transform for automatic edge detection in SAR images}, journal = {IEEE transactions on geoscience and remote sensing}, volume = {49}, number = {1}, year = {2011}, pages = {222{\textendash}235}, issn = {0196-2892}, doi = {10.1109/TGRS.2010.2052814}, url = {http://hdl.handle.net/2117/11057}, author = {Tello, M. and L{\'o}pez-Mart{\'\i}nez, C. and Mallorqui, J.J. and Salembier, P.} } @conference {cSchreer11, title = {Format-agnostic approach for production, delivery and rendering of immersive media}, booktitle = {Networked and Electronic Media Summit}, year = {2011}, pages = {{\textendash}}, isbn = {SN}, url = {http://nem-summit.eu/program/}, author = {Schreer, O. and Thomas, G. and Niamut, O. and Macq, J. and Kochale, A. and Batke, J. and Ruiz-Hidalgo, J. and Oldfield, R. and Shirley, B. and Thallinger, G.} } @conference {cBragos11a, title = {Implementation and first results of the Introduction to Engineering course in the ETSETB-UPC new degrees}, booktitle = {II Conferencia Internacional en Fomento e Innovaci{\'o}n con Nuevas Tecnolog{\'\i}as en la Docencia de la Ingenier{\'\i}a}, year = {2011}, pages = {1{\textendash}4}, isbn = {978-1-4577-0559-5}, doi = {10.1109/FINTDI.2011.5945971}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5945971}, author = {Bragos, R. and Pegueroles, J. and Alarc{\'o}n, E. and Camps, A. and Sarda, J. and Consolacion, C. and Mussons, J. and Pons, O. and Albert Oliveras and Garc{\'\i}a, M. and Onrubia, R. and Elisa Sayrol} } @article {aCanton-Ferrer11a, title = {Multi-camera multi-object voxel-based Monte Carlo 3D tracking strategies}, journal = {Eurasip journal on advances in signal processing}, volume = {2011}, number = {114}, year = {2011}, pages = {1{\textendash}15}, abstract = {

This article presents a new approach to the problem of simultaneous tracking of several people in low-resolution sequences from multiple calibrated cameras. Redundancy among cameras is exploited to generate a discrete 3D colored representation of the scene, being the starting point of the processing chain. We review how the initiation and termination of tracks influences the overall tracker performance, and present a Bayesian approach to efficiently create and destroy tracks. Two Monte Carlo-based schemes adapted to the incoming 3D discrete data are introduced. First, a particle filtering technique is proposed relying on a volume likelihood function taking into account both occupancy and color information. Sparse sampling is presented as an alternative based on a sampling of the surface voxels in order to estimate the centroid of the tracked people. In this case, the likelihood function is based on local neighborhoods computations thus dramatically decreasing the computational load of the algorithm. A discrete 3D re-sampling procedure is introduced to drive these samples along time. Multiple targets are tracked by means of multiple filters, and interaction among them is modeled through a 3D blocking scheme. Tests over CLEAR-annotated database yield quantitative results showing the effectiveness of the proposed algorithms in indoor scenarios, and a fair comparison with other state-of-the-art algorithms is presented. We also consider the real-time performance of the proposed algorithm.\ 

}, issn = {1687-6172}, doi = {10.1186/1687-6180-2011-114}, url = {http://asp.eurasipjournals.com/content/2011/1/114}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s and Monte, E.} } @mastersthesis {xVentura11, title = {Tools for Image Retrieval in Large Multimedia Databases}, year = {2011}, abstract = {

One of the challenges in the development of an image retrieval system is to achieve an efficient indexing scheme since both developers and users, who are used to make requests in order to find a multimedia element in a large database, can be frustrated due to the long computational time of the search.

The traditional indexing schemes neither fulfil the dynamic indexing requirement, which allows to add or remove elements from the structure, nor fit well in high dimensional feature spaces due to the phenomenon so called {\textquotedblleft}the curse of dimensionality.{\textquotedblright}

After analysing several indexing techniques from the literature, we have decided to implement an indexing scheme called Hierarchical Cellular Tree (HCT), which was designed to bring an effective solution especially for indexing large multimedia databases. The HCT has allowed to improve the performance of our implemented image retrieval system based on the MPEG-7 visual descriptors. We have also made some contributions by proposing some modifications to the original HCT which have resulted in an improvement of its performance. Thus, we have proposed a redefinition of the covering radius, which does not consider only the elements belonging to the cell, but also all the elements holding from that cell. Since this consideration implies a much more computationally costly algorithm, we have proposed an approximation by excess for the covering radius value. However, we have also implemented a method which allows to update the covering radius to its actual value whenever it is desired. In addition to this, the pre-emptive insertion method has been adapted as a searching technique in order to improve the performance given by the retrieval scheme called Progressive Query, which was originally proposed to be used over the HCT.

Furthermore, the HCT indexing scheme has been also adapted to a server/client architecture by using a messenger system called KSC, which allows to have the HCT loaded on a server waiting for the query requests which are launched for the several clients of the retrieval system. In addition to this, the tool used to request a search over the indexed database has been adapted to a graphic user interface, named GOS (Graphic Object Searcher), which allows the user to order the retrievals in a more friendly way.

[Thesis report in UPCommons]

}, url = {http://hdl.handle.net/2099.1/13011}, author = {Ventura, C.}, editor = {Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @inbook {bMarcotegui10, title = {Compression}, booktitle = {Mathematical morphology from theories to applications}, year = {2010}, pages = {385{\textendash}391}, publisher = {Wiley}, organization = {Wiley}, edition = {L. Najman and H. Talbot (Eds.)}, chapter = {16}, isbn = {978-1848212152}, url = {http://cataleg.upc.edu/record=b1376601~S1*cat}, author = {Marcotegui, B. and Salembier, P.} } @inbook {bMarcotegui10a, title = {Compression}, booktitle = {Morphologie Math{\'e}matique II: estimation choix et mise en oeuvre}, year = {2010}, publisher = {Hermes, Lavoisier}, organization = {Hermes, Lavoisier}, edition = {L. Najman and H. Talbot (Eds.)}, isbn = {978-2-7462-2593-0}, url = {http://cataleg.upc.edu/search~S1*cat/X?estimation\%2C+choix\&SORT=D\&searchscope=1}, author = {Marcotegui, B. and Salembier, P.} } @inbook {bWaibel10, title = {Computers in the Human Interaction Loop}, booktitle = {Handbook on Ambient Intelligence and Smart Environments (AISE)}, year = {2010}, pages = {1071{\textendash}1116}, publisher = {Springer}, organization = {Springer}, address = {Boston, MA}, abstract = {

It is a common experience in our modern world, for us humans to be overwhelmed by the complexities of technological artifacts around us, and by the attention they demand. While technology provides wonderful support and helpful assistance, it also causes an increased preoccupation with technology itself and a related fragmentation of attention. But as humans, we would rather attend to a meaningful dialog and interaction with other humans, than to control the operations of machines that serve us. The cause for such complexity and distraction, however, is a natural consequence of the flexibility and choice of functions and features that technology has to offer. Thus flexibility of choice and the availability of desirable functions are in conflict with ease of use and our very ability to enjoy their benefits.\ 

}, isbn = {978-0-387-93807-3}, doi = {10.1007/978-0-387-93808-0_40}, author = {Waibel, A. and Stiefelhagen, R. and Carlson, R. and Casas, J. and Kleindienst, J. and Lamel, L. and Lanz, O. and Mostefa, D. and Omologo, M. and Pianesi, F. and Polymenakos, L. and Potamianos, G. and Soldatos, J. and Sutschet, G. and Terken, J.} } @conference {cPont-Tuset10, title = {Contour detection using binary partition trees}, booktitle = {IEEE International Conference on Image Processing}, year = {2010}, pages = {1609{\textendash}1612}, isbn = {?}, doi = {10.1109/ICIP.2010.5652339}, url = {http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=\&arnumber=5652339\&queryText\%3DContour+detection+using+binary+partition+trees\%26openedRefinements\%3D*\%26searchField\%3DSearch+All}, author = {Jordi Pont-Tuset and Marqu{\'e}s, F.} } @mastersthesis {xmunoz-trallero10, title = {Extensi{\'o} d{\textquoteright}una interf{\'\i}cie de cerca d{\textquoteright}imatges a les consultes amb regions}, year = {2010}, abstract = {

Aquest {\'e}s un projecte universitat-empresa que presenta una interf{\'\i}cie de cerca multimodal a partir de la integraci{\'o} de diferents eines ja existents per crear una eina global que combina les cerques textuals amb les consultes mitjan{\c c}ant exemple, ja sigui una consulta basada en una imatge o en una regi{\'o} d{\textquoteright}aquesta imatge. L{\textquoteright}eina global s{\textquoteright}integra en l{\textquoteright}entorn del Digition, el gestor de continguts audiovisuals de la Corporaci{\'o} Catalana de Mitjans Audiovisuals. La principal aportaci{\'o} del present projecte consisteix en la creaci{\'o} d{\textquoteright}una interf{\'\i}cie de cerca d{\textquoteright}imatges basada en regions i la seva integraci{\'o} en l{\textquoteright}eina global.


}, url = {http://hdl.handle.net/2099.1/13525}, author = {Pia Mu{\~n}oz-Trallero}, editor = {Xavier Gir{\'o}-i-Nieto and Vives, X.} } @article {aGiro-i-Nieto10, title = {GAT, a Graphical Annotation Tool for semantic regions}, journal = {Multimedia tools and applications}, volume = {46}, number = {2/3 (2010)}, year = {2010}, pages = {155{\textendash}174}, abstract = {

This article presents GAT, a Graphical Annotation Tool based on a region-based hierarchical representation of images. The proposed solution uses Partition Trees to navigate through the image segments which are automatically defined at different spatial scales. Moreover, the system focuses on the navigation through ontologies for a semantic annotation of objects and of the parts that compose them. The tool has been designed under usability criteria to minimize the user interaction by trying to predict the future selection of regions and semantic classes. The implementation uses MPEG-7/XML input and output data to allow interoperability with any type of Partition Tree. This tool is publicly available and its source code can be downloaded under a free software license.

}, issn = {1380-7501}, doi = {10.1007/s11042-009-0389-2}, url = {http://www.springerlink.com/content/j78782k762617352/}, author = {Xavier Gir{\'o}-i-Nieto and Camps, N. and Marqu{\'e}s, F.} } @article {xCortes10, title = {GOS: b{\'u}squeda visual de im{\'a}genes}, number = {25}, year = {2010}, pages = {36{\textendash}44}, keywords = {i3media}, issn = {1698-7047}, url = {http://upcommons.upc.edu/e-prints/urlFiles?idDrac=2251008}, author = {Cort{\'e}s, S.}, editor = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @mastersthesis {xVentura10, title = {Image-Based Query by Example Using MPEG-7 Visual Descriptors}, year = {2010}, abstract = {

This project presents the design and implementation of a Content-Based Image Re- trieval (CBIR) system where queries are formulated by visual examples through a graphical interface. Visual descriptors and similarity measures implemented in this work followed mainly those defined in the MPEG-7 standard although, when necessary, extensions are proposed. Despite the fact that this is an image-based system, all the proposed descriptors have been implemented for both image and region queries, allowing the future system upgrade to support region-based queries. This way, even a contour shape descriptor has been developed, which has no sense for the whole image. The system has been assessed on different benchmark databases; namely, MPEG-7 Common Color Dataset, and Corel Dataset. The evaluation has been performed for isolated descriptors as well as for combinations of them. The strategy studied in this work to gather the information obtained from the whole set of computed descriptors is weighting the rank list for each isolated descriptor.\ 

}, url = {http://upcommons.upc.edu/pfc/handle/2099.1/9453}, author = {Ventura, C.}, editor = {Marqu{\'e}s, F. and Jordi Pont-Tuset} } @phdthesis {dCalderero10, title = {Information Theoretical Region Merging Approaches and Fusion of Hierarchical Image Segmentation Results}, year = {2010}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {This PhD. thesis addresses the unsupervised hierarchical segmentation of images and the automatic extraction of the image partitions providing the most semantically relevant explanations of the image at different levels of analysis. From a semantic and practical perspective, image segmentation is a first and key step for image analysis and pattern recognition since region-based image representations provide a first level of abstraction and a reduction of the number of primitives, leading to a more robust estimation of parameters and descriptors. The proposal of this dissertation is based on an important class of hierarchical bottomup segmentation approaches, known as region merging techniques. These approaches naturally provide a bottom-up hierarchy, more suitable when no a priori information about the image is available, and an excellent compromise between efficiency of computation and representation. The first part of the dissertation is devoted to the proposal, under a statistical framework, of a family of unsupervised region merging techniques. These techniques are characterized by general and non-parametric region models, with neither color nor texture homogeneity assumptions, and a set of innovative merging criteria, based on information theory statistical measures. The scale consistency of the partitions is assured through (i) a size regularization term into the merging criteria and a classical merging order, or (ii) using a novel scale-based merging order to avoid the region size homogeneity imposed by the use of a size regularization term. Moreover, a partition significance index is defined to automatically determine the subset of most representative partitions from the created hierarchy. Most significant automatically extracted partitions show the ability to represent the semantic content of the image. Results are promising, outperforming in most indicators both object-oriented and texture state-of-the-art segmentation techniques. The second part of the thesis is focused on the fusion of hierarchical segmentation results, obtained by different segmentation techniques or from different information channels of the same image, with the purpose of increasing the robustness and accuracy of the final solution. In this case, a generic and scalable segmentation scheme based on a cooperative principle, named cooperative region merging, is designed to combine in an unsupervised manner a set of hierarchical region-based representations. The intuition behind this cooperative approach is to iteratively establish a basic or conservative consensus between the independent techniques that can be used as the starting point from which further consensus may be built, similar to a negotiation process for decision making. In addition to the new fused hierarchy of partitions, the proposed scheme automatically provides a subset of partitions considered most relevant from the fusion viewpoint. The combination of hierarchical segmentation results applying different segmentation techniques over the same color image leads to a global improvement of the accuracy and the stability of the segmentation results. Moreover, the use of the cooperative approach for the fusion of segmentation results from heterogenous information channels is presented. Application examples demonstrate the high flexibility and potentiality of the cooperative region merging scheme into a wide range of applications and fusion problems (for instance, in multiview processing and remote sensing). The proposed fusion strategies are able to naturally incorporate a priori available knowledge on the types of information to combine, or on the specificities of the particular fusion problem, and to improve the efficiency and reduce the computational load of the fusion process without compromising the accuracy of the segmentation results.}, author = {Calderero, F.}, editor = {Marqu{\'e}s, F.} } @conference {cVilaplana10, title = {Object detection and segmentation on a hierarchical region-based image representation}, booktitle = {2010 IEEE International Conference on Image Processing}, year = {2010}, pages = {3393{\textendash}3396}, isbn = {0-8194-2103-0}, url = {http://hdl.handle.net/2117/11435}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F. and Le{\'o}n, M. and Gasull, A.} } @conference {cCalderero10, title = {Region merging parameter dependency as information diversity to create sparse hierarchies of partitions}, booktitle = {2010 IEEE International Conference on Image Processing}, year = {2010}, pages = {2237{\textendash}2240}, isbn = {1522-4880}, doi = {10.1109/ICIP.2010.5651720}, url = {http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=\&arnumber=5651720\&queryText\%3DRegion+merging+parameter+dependency+as+information+diversity+to+create+sparse+hierarchies+of+partitions\%26openedRefinements\%3D*\%26searchField\%3DSearch+All}, author = {Calderero, F. and Marqu{\'e}s, F.} } @article {aCalderero10, title = {Region merging techniques using information theory statistical measures}, journal = {IEEE transactions on image processing}, volume = {19}, number = {6}, year = {2010}, pages = {1567{\textendash}1586}, abstract = {

The purpose of the current work is to propose, under a statistical framework, a family of unsupervised region merging techniques providing a set of the most relevant region-based explanations of an image at different levels of analysis. These techniques are characterized by general and nonparametric region models, with neither color nor texture homogeneity assumptions, and a set of innovative merging criteria, based on information theory statistical measures. The scale consistency of the partitions is assured through i) a size regularization term into the merging criteria and a classical merging order, or ii) using a novel scale-based merging order to avoid the region size homogeneity imposed by the use of a size regularization term. Moreover, a partition significance index is defined to automatically determine the subset of most representative partitions from the created hierarchy. Most significant automatically extracted partitions show the ability to represent the semantic content of the image from a human point of view. Finally, a complete and exhaustive evaluation of the proposed techniques is performed, using not only different databases for the two main addressed problems (object-oriented segmentation of generic images and texture image segmentation), but also specific evaluation features in each case: under- and oversegmentation error, and a large set of region-based, pixel-based and error consistency indicators, respectively. Results are promising, outperforming in most indicators both object-oriented and texture state-of-the-art segmentation techniques.

}, issn = {1057-7149}, doi = {10.1109/TIP.2010.2043008}, url = {http://www.dfmf.uned.es/~daniel/www-imagen-dhp/biblio/region-merging-information.pdf}, author = {Calderero, F. and Marqu{\'e}s, F.} } @conference {cLeon10, title = {Region-based caption text extraction}, booktitle = {11th. International Workshop on Image Analysis for Multimedia Application Services}, year = {2010}, pages = {1{\textendash}4}, url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5608542}, author = {Le{\'o}n, M. and Ver{\'o}nica Vilaplana and Gasull, A. and Marqu{\'e}s, F.} } @phdthesis {dVilaplana10, title = {Region-based face detection, segmentation and tracking. framework definition and application to other objects}, year = {2010}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

One of the central problems in computer vision is the automatic recognition of object classes. In particular, the detection of the class of human faces is a problem that generates special interest due to the large number of applications that require face detection as a first step. In this thesis we approach the problem of face detection as a joint detection and segmentation problem, in order to precisely localize faces with pixel accurate masks. Even though this is our primary goal, in finding a solution we have tried to create a general framework as independent as possible of the type of object being searched. For that purpose, the technique relies on a hierarchical region-based image model, the Binary Partition Tree, where objects are obtained by the union of regions in an image partition. In this work, this model is optimized for the face detection and segmentation tasks. Different merging and stopping criteria are proposed and compared through a large set of experiments. In the proposed system the intra-class variability of faces is managed within a learning framework. The face class is characterized using a set of descriptors measured on the tree nodes, and a set of one-class classifiers. The system is formed by two strong classifiers. First, a cascade of binary classifiers simplifies the search space, and afterwards, an ensemble of more complex classifiers performs the final classification of the tree nodes. The system is extensively tested on different face data sets, producing accurate segmentations and proving to be quite robust to variations in scale, position, orientation, lighting conditions and background complexity. We show that the technique proposed for faces can be easily adapted to detect other object classes. Since the construction of the image model does not depend on any object class, different objects can be detected and segmented using the appropriate object model on the same image model. New object models can be easily built by selecting and training a suitable set of descriptors and classifiers. Finally, a tracking mechanism is proposed. It combines the efficiency of the mean-shift algorithm with the use of regions to track and segment faces through a video sequence, where both the face and the camera may move. The method is extended to deal with other deformable objects, using a region-based graph-cut method for the final object segmentation at each frame. Experiments show that both mean-shift based trackers produce accurate segmentations even in difficult scenarios such as those with similar object and background colors and fast camera and object movements.

}, url = {http://hdl.handle.net/10803/33330}, author = {Ver{\'o}nica Vilaplana}, editor = {Marqu{\'e}s, F.} } @conference {cGiro10a, title = {System architecture of a web service for Content-Based Image Retrieval}, booktitle = {ACM International Conference On Image And Video Retrieval 2010}, year = {2010}, pages = {358{\textendash}365}, abstract = {

This paper presents the system architecture of a Content-Based Image Retrieval system implemented as a web service. The proposed solution is composed of two parts, a client running a graphical user interface for query formulation and a server where the search engine explores an image repository. The separation of the user interface and the search engine follows a Service as a Software (SaaS) model, a type of cloud computing design where a single core system is online and available to authorized clients. The proposed architecture follows the REST software architecture and HTTP protocol for communications, two solutions that combined with metadata coded in RDF, make the proposed system ready for its integration in the semantic web. User queries are formulated by visual examples through a graphical interface and content is remotely accessed also through HTTP communication. Visual descriptors and similarity measures implemented in this work are mostly defined in the MPEG-7 standard, while textual metadata is coded according to the Dublin Core specifications.

}, isbn = {978-1-4503-0117-6}, doi = {10.1145/1816041.1816093}, url = {http://doi.acm.org/10.1145/1816041.1816093}, author = {Xavier Gir{\'o}-i-Nieto and Ventura, C. and Jordi Pont-Tuset and Cort{\'e}s, S. and Marqu{\'e}s, F.} } @mastersthesis {xVaras10, title = {Type of view estimation in football sequences}, year = {2010}, abstract = {

Due to the huge repercussion of football broadcast in society, an enormous number of applications can be derived to both analyze the match and enhance the visual experience of the spectator. These applications request semantical information about the content of the images. In particular, the type of view in a football image contains valuable information about the game. Thus, the type of view must be automatically computed to be able to process the large amount of information extracted from each football match. In this work, we propose a robust classification system that estimates the type ofview in football images in real time. For each frame of the sequence, a set of descriptors is extracted to characterize a specific part of the scene: the grass field. Gathering all these descriptors and a few ones related with texture, a decision tree determines the view that is shown in that frame. In order to improve the robustness of the algorithm, the redundancy of the temporal domain is exploited. The validity of the proposed algorithm has been tested on a large amount of frames from broadcasted football sequences in a wide variety of scenarios (stadiums, light conditions, ...). Promising results have been obtained with a 96\% of accuracy in the classification of these images.

}, author = {David Varas}, editor = {Marqu{\'e}s, F.} } @book {eDutoit09, title = {Applied signal processing: A Matlab-based proof of concept}, year = {2009}, publisher = {Springer}, organization = {Springer}, isbn = {978-0-387-74534-3}, url = {http://cataleg.upc.edu/record=b1353617~S1*cat}, author = {Dutoit, T. and Marqu{\'e}s, F.} } @conference {cLeon09, title = {Caption text extraction for indexing purposes using a hierarchical region-based image model}, booktitle = {16th International Conference on Image Processing}, year = {2009}, pages = {1869{\textendash}1872}, isbn = {978-1-4244-5655-0}, doi = {http://dx.doi.org/10.1109/ICIP.2009.5413607}, url = {http://hdl.handle.net/2117/7940}, author = {Le{\'o}n, M. and Ver{\'o}nica Vilaplana and Gasull, A. and Marqu{\'e}s, F.} } @conference {cMarcello09, title = {Cloud motion estimation in seviri image sequences}, booktitle = {2009 IEEE International Geoscience and Remote Sensing Symposium}, year = {2009}, pages = {642{\textendash}645}, isbn = {978-1-4244-3394-0}, doi = {10.1109/IGARSS.2009.5417842}, url = {http://hdl.handle.net/2117/9492}, author = {Marcello, J. and F. Eugenio and Marqu{\'e}s, F.} } @conference {cRolon09a, title = {Generalized lifting with adaptive local pdf estimation for image coding}, booktitle = {Picture coding symposium, PCS 2009}, year = {2009}, address = {Chicago, USA}, author = {Rol{\'o}n, J. and Mendon{\c c}a, E. and Salembier, P.} } @conference {cFrias-Velazquez09, title = {Gray-scale erosion algorithm based on image bitwise decomposition: application to focal plane processors}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing 2009}, year = {2009}, pages = {845{\textendash}848}, doi = {10.1109/ICASSP.2009.4959716}, url = {http://hdl.handle.net/2117/9156}, author = {Frias-Velazquez, A. and Morros, J.R.} } @conference {cCalderero09a, title = {Hierarchical fusion of color and depth information at partition level by cooperative region merging}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing 2009}, year = {2009}, pages = {973{\textendash}976}, doi = {10.1109/ICASSP.2009.4959748}, url = {http://hdl.handle.net/2117/9491}, author = {Calderero, F. and Marqu{\'e}s, F.} } @conference {cCalderero09, title = {Hierarchical segmentation of vegetation areas in high spatial resolution images by fusion of multispectral information}, booktitle = {2009 IEEE International Geoscience and Remote Sensing Symposium}, year = {2009}, pages = {232{\textendash}235}, isbn = {978-1-4244-3394-0}, doi = {10.1109/IGARSS.2009.5417329}, url = {http://hdl.handle.net/2117/9494}, author = {Calderero, F. and Marqu{\'e}s, F. and Marcello, J. and F. Eugenio} } @conference {cFrias-Velazquez09a, title = {Histogram computation based on image bitwise decomposition}, booktitle = {ICIP 2009}, year = {2009}, isbn = {978-1-4244-5653-6}, doi = {10.1109/ICIP.2009.5413981}, url = {http://hdl.handle.net/2117/9144}, author = {Frias-Velazquez, A. and Morros, J.R.} } @inbook {bMarques09, title = {How are digital images compressed in the web?}, booktitle = {Applied signal processing}, year = {2009}, pages = {265{\textendash}310}, isbn = {978-0-387-74534-3}, url = {http://cataleg.upc.edu/record=b1353617~S1*cat}, author = {Marqu{\'e}s, F. and Menezes, M. and Ruiz-Hidalgo, J.}, editor = {Dutoit, T. and Marqu{\'e}s, F.} } @inbook {bMarques09a, title = {How are digital TV programs compressed to allow broadcasting?}, booktitle = {Applied signal processing}, year = {2009}, pages = {311{\textendash}359}, isbn = {978-0-387-74534-3}, url = {http://cataleg.upc.edu/record=b1353617~S1*cat}, author = {Marqu{\'e}s, F. and Menezes, M. and Ruiz-Hidalgo, J.}, editor = {Dutoit, T. and Marqu{\'e}s, F.} } @inbook {bBach09, title = {How can physicians quantify brain degeneration?}, booktitle = {Applied signal processing}, year = {2009}, pages = {411{\textendash}449}, isbn = {978-0-387-74534-3}, url = {http://cataleg.upc.edu/record=b1353617~S1*cat}, author = {Bach, M. and Thiran, J. and Marqu{\'e}s, F.}, editor = {Dutoit, T. and Marqu{\'e}s, F.} } @inbook {bDescamps09, title = {How does digital cinema compress images?}, booktitle = {Applied signal processing}, year = {2009}, pages = {361{\textendash}410}, isbn = {978-0-387-74534-3}, url = {http://cataleg.upc.edu/record=b1353617~S1*cat}, author = {Descamps, A. and De Vleeschouwer, C. and Jacques, L. and Marqu{\'e}s, F.}, editor = {Dutoit, T. and Marqu{\'e}s, F.} } @article {aNeumann09, title = {Integration of audiovisual sensors and technologies in a smart room}, journal = {Personal and ubiquitous computing}, volume = {13}, number = {1}, year = {2009}, pages = {15{\textendash}23}, abstract = {

At the Technical University of Catalonia\ (UPC), a smart room has been equipped with 85 microphones and 8 cameras. This paper describes the setup of the\ sensors, gives an overview of the underlying hardware and\ software infrastructure and indicates possibilities for highand low-level multi-modal interaction. An example of\ usage of the information collected from the distributed\ sensor network is explained in detail: the system supports\ a group of students that have to solve a lab assignment\ related problem.

}, keywords = {CHIL, PROVEC}, issn = {1617-4909}, doi = {10.1007/s00779-007-0172-1}, url = {http://hdl.handle.net/2117/9468}, author = {Neumann, J. and Casas, J. and Macho, D. and Ruiz-Hidalgo, J.} } @book {eMarques09, title = {Multimodal signal processing: theory and applications for human-computer interaction}, year = {2009}, isbn = {9780123748256}, editor = {Marqu{\'e}s, F.} } @conference {cCalderero09b, title = {Performance evaluation of probability density estimators for unsupervised information theoretical region merging}, booktitle = {16th International Conference on Image Processing}, year = {2009}, pages = {4397{\textendash}4400}, isbn = {978-1-4244-5655-6}, doi = {10.1109/ICIP.2009.5413621}, url = {http://hdl.handle.net/2117/9510}, author = {Calderero, F. and Marqu{\'e}s, F. and Ortega, A.} } @article {aChang09, title = {Trajectory tree as an object-oriented hierarchical representation for video}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {19}, number = {4}, year = {2009}, pages = {547{\textendash}560}, issn = {1051-8215}, author = {Chang, C. and M. Pard{\`a}s and Marqu{\'e}s, F.} } @article {aCorrea08, title = {3D posture estimation using geodesic distance maps}, journal = {Multimedia tools and applications}, volume = {38}, number = {3}, year = {2008}, pages = {365{\textendash}384}, issn = {1380-7501}, author = {Correa, P. and Marqu{\'e}s, F. and Marichal, X. and Macq, B.} } @article {aVilaplana08, title = {Binary partition trees for object detection}, journal = {IEEE transactions on image processing}, volume = {17}, number = {11}, year = {2008}, pages = {1{\textendash}16}, abstract = {

This paper discusses the use of binary partition trees (BPTs) for object detection. BPTs are hierarchical region-based representations of images. They define a reduced set of regions that covers the image support and that spans various levels of resolution. They are attractive for object detection as they tremendously reduce the search space. In this paper, several issues related to the use of BPT for object detection are studied. Concerning the tree construction, we analyze the compromise between computational complexity reduction and accuracy. This will lead us to define two parts in the BPT: one providing accuracy and one representing the search space for the object detection task. Then we analyze and objectively compare various similarity measures for the tree construction. We conclude that different similarity criteria should be used for the part providing accuracy in the BPT and for the part defining the search space and specific criteria are proposed for each case. Then we discuss the object detection strategy based on BPT. The notion of node extension is proposed and discussed. Finally, several object detection examples illustrating the generality of the approach and its efficiency are reported.

}, issn = {1057-7149}, doi = {10.1109/TIP.2008.2002841}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F. and Salembier, P.} } @phdthesis {dIregui08, title = {Efficient strategies for navigation through very large JPEG2000 image}, year = {2008}, school = {Universit{\'e} Catholique de Louvain (UCL)}, type = {phd}, url = {http://hdl.handle.net/2078.1/19637 }, author = {Iregui, M.}, editor = {Marqu{\'e}s, F. and Macq, B.} } @inbook {bVilaplana08, title = {Face and speech interaction}, booktitle = {Multimodal user interfaces: from signals to interaction}, year = {2008}, pages = {85{\textendash}118}, isbn = {978-3-540-78344-2}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F. and Gurban, M. and Thiran, J.} } @conference {cCalderero08a, title = {General region merging approaches based on information theory statistical measures}, booktitle = {IEEE International Conference on Image Processing}, year = {2008}, pages = {3016{\textendash}3019}, isbn = {1-4244-1764-3}, author = {Calderero, F. and Marqu{\'e}s, F.} } @conference {cCalderero08, title = {General Region Merging Based on First Order Markov Information Theory Statistical Measures}, booktitle = {16th European Signal Processing Conference}, year = {2008}, author = {Calderero, F. and Marqu{\'e}s, F.} } @conference {cDimiccoli08, title = {Monocular Depth by Nonlinear Diffusion}, booktitle = {IEEE Sixth Indian Conference on Computer Vision, Graphics \& Image Processing, ICVGIP 2008}, year = {2008}, pages = {140{\textendash}148}, address = {Bhubaneswar, India}, author = {Dimiccoli, M. and Morel, J. and Salembier, P.} } @article {aMarcello08, title = {Motion estimation techniques to automatically track oceanographic thermal structures in multi-sensor image sequences}, journal = {IEEE transactions on geoscience and remote sensing}, volume = {46}, number = {9}, year = {2008}, pages = {2743{\textendash}2762}, issn = {0196-2892}, author = {Marcello, J. and F. Eugenio and Marqu{\'e}s, F. and Hernandez-Guerra, A. and Gasull, A.} } @article {aSalah08, title = {Multimodal identification and localization of users in a smart environment}, journal = {Journal on Multimodal user interfaces}, volume = {2}, number = {2}, year = {2008}, pages = {75{\textendash}91}, issn = {1783-7677}, doi = {10.1007/s12193-008-0008-y}, url = {http://hdl.handle.net/2117/6326}, author = {Salah, A. and Morros, J.R. and Luque, J. and Segura, C. and Hernando, J. and Ambekar, O. and Schouten, B. and Pauwels, E.} } @conference {cVilaplana08, title = {Region-based mean shift tracking: Application to face tracking}, booktitle = {IEEE International Conference on Image Processing}, year = {2008}, pages = {2712{\textendash}2715}, isbn = {978-1-4577-0538-0}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @conference {cGiro-i-Nieto08, title = {System architecture for indexing regions in keyframes}, booktitle = {3rd International Conference on Semantic and digital Media Technologies}, year = {2008}, month = {12/2008}, pages = {1{\textendash}2}, address = {Koblenz, Germany}, abstract = {

This paper describes the design of an indexing system for a video database. The system uses region-based manual an- notations of keyframes to create models to automatically annotate new keyframes also at the region level. The pre- sented architecture includes user interfaces for training and querying the system, internal databases to manage ingested content and modelled semantic classes, as well as communi- cation interfaces to allow the system interconnection. The scheme is designed to work as a plug-in to an external Mul- timedia Asset Management (MAM) system.\ 

}, keywords = {architecture, Indexing, keyframe, MAM, region}, url = {http://hdl.handle.net/2117/10893}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @article {aCraene08, title = {Unbiased group-wise alignment by iterative central tendency estimations}, journal = {Mathematical modeling of natural phenomena}, volume = {3}, number = {6}, year = {2008}, pages = {2{\textendash}32}, issn = {0973-5348}, url = {http://hdl.handle.net/2117/8140}, author = {de Craene, M. and Macq, B. and Marqu{\'e}s, F. and Salembier, P. and Warfield, S.} } @conference {cLuque06a, title = {Audio, Video and Multimodal Person Identification in a Smart Room}, booktitle = {CLEAR{\textquoteright}06 Evaluation Campaign and Workshop - Classification of Events, Activities and Relationships}, year = {2007}, pages = {258{\textendash}269}, isbn = {978-3-540-69567-7}, author = {Luque, J. and Morros, J.R. and Garde, A. and Anguita, J. and Farr{\'u}s, M. and Macho, D. and Marqu{\'e}s, F. and Mart{\'\i}nez, C. and Ver{\'o}nica Vilaplana and Hernando, J.} } @article {aCorrea07, title = {Bayesian approach for morphology based 2D human motion capture}, journal = {IEEE transactions on multimedia}, volume = {9}, number = {4}, year = {2007}, pages = {754{\textendash}765}, issn = {1520-9210}, author = {Correa, P. and Czyz, J. and Marqu{\'e}s, F. and Umeda, T. and Marichal, X. and Macq, B.} } @conference {cVilaplana07b, title = {On building a hierarchical region-based representation for generic image analysis}, booktitle = {IEEE International Conference on Image Processing}, year = {2007}, isbn = {978-1-61284-467-1}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @article {aMostefa07, title = {The CHIL Audiovisual Corpus for Lecture and Meeting Analysis inside Smart Rooms}, journal = {Language resources and evaluation}, volume = {41}, number = {3}, year = {2007}, month = {01/2008}, pages = {389{\textendash}407}, abstract = {

The analysis of lectures and meetings inside smart rooms has recently attracted much interest in the literature, being the focus of international projects and technology evaluations. A key enabler for progress in this area is the availability of appropriate multimodal and multi-sensory corpora, annotated with rich human activity information during lectures and meetings. This paper is devoted to exactly such a corpus, developed in the framework of the European project CHIL, {\textquotedblleft}Computers in the Human Interaction Loop{\textquotedblright}. The resulting data set has the potential to drastically advance the state-of-the-art, by providing numerous synchronized audio and video streams of real lectures and meetings, captured in multiple recording sites over the past 4 years. It particularly overcomes typical shortcomings of other existing databases that may contain limited sensory or monomodal data, exhibit constrained human behavior and interaction patterns, or lack data variability. The CHIL corpus is accompanied by rich manual annotations of both its audio and visual modalities. These provide a detailed multi-channel verbatim orthographic transcription that includes speaker turns and identities, acoustic condition information, and named entities, as well as video labels in multiple camera views that provide multi-person 3D head and 2D facial feature location information. Over the past 3 years, the corpus has been crucial to the evaluation of a multitude of audiovisual perception technologies for human activity analysis in lecture and meeting scenarios, demonstrating its utility during internal evaluations of the CHIL consortium, as well as at the recent international CLEAR and Rich Transcription evaluations. The CHIL corpus is publicly available to the research community

}, issn = {1574-020X}, doi = {10.1007/s10579-007-9054-4}, author = {Mostefa, D. and Moreau, N. and Choukri, K. and Potamianos, G. and Chu, S. and Tyagi, A. and Casas, J. and Turmo, J. and Cristoforetti, L. and Tobia, F. and Pnevmatikakis, A. and Mylonakis, V. and Talantzis, F. and Burger, S. and Stiefelhagen, R. and Bernardin, K. and Rochet, C.} } @conference {cGiro-i-Nieto07, title = {Composite object detection in video sequences: Applications to controlled environments}, booktitle = {8th International Workshop on Image Analysis for Multimedia Interactive Services}, year = {2007}, month = {06/2007}, publisher = {IEEE}, organization = {IEEE}, address = {Santorini, Greece}, abstract = {

This paper presents a set of techniques for the detection of composite objects in video recordings of a controlled environ- ment. Firstly, a selective region-based analysis is performed by tuning the algorithm to the perceptual characteristics of the object in the environment. Secondly, the controlled per- ceptual and semantic variabilities of the object are addressed by the detection analysis thanks to a frame by frame update of the object models, and by allowing multiple models for a single object. The proposed techniques are illustrated in the detection of laptops from a zenithal view in a smart room.\ 

}, keywords = {image region analysis, image sequence analysis, Object detection, object recognition, pattern recognition}, isbn = {0-7695-2818-X}, doi = {10.1109/WIAMIS.2007.31}, url = {http://dx.doi.org/10.1109/WIAMIS.2007.31}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @book {eAguilar07, title = {Diccionari de Telecomunicacions}, year = {2007}, isbn = {978-84-412-1459-0}, author = {Aguilar, M. and Alcober, J. and Altes, J. and Aragones, X. and Artigas, D. and Bardes, D. and Barlabe, A. and Bragos, R. and Calderer, J. and Cardama, A. and Casademont, J. and Casals, L. and Comer{\'o}n, A. and Cotrina, J. and Cruz, L. and Federico Dios and Duxans, H. and Esparza, O. and Esquerra, I. and Garcia, D. and Garcias, P. and Gomez, C. and Gorricho, J. and Guinjoan, F. and Hesselbach, X. and Liria, A. and Lopez, J. and Madrenas, J. and Madue{\~n}o, M. and Mestre, F. and Monte, E. and Morros, J.R. and Mu{\~n}oz, J. and Pallar{\'e}s, E. and Pons, J. and Recolons, J. and Rincon, D. and Riu, P. and Pradell, L. and Pascual-Iserte, A. and Prat, L. and Rey, F. and Villares, J.} } @conference {cMorros07, title = {Event recognition for meaningful Human-Computer interaction in smart environments}, booktitle = {Proceedings of the eNTERFACE{\textquoteright}07 Workshop on Multimodal Interfaces}, year = {2007}, month = {08/2007}, abstract = {

The aim of this project is to monitor a room for the purposes of analysing the interactions and identities of a small set of individ- uals. We work with multiple uncalibrated sensors that observe a single environment and generate multimodal data streams. These streams are processed with the help of a generic client-server middleware called SmartFlow. Modules for visual motion de- tection, visual face tracking, visual face identification, visual op- portunistic sensing, audio-based localization, and audio-based identification are implemented and integrated under SmartFlow to work in coordination across different platforms.

}, keywords = {Audio systems, Image motion analysis, Image sensors, pattern recognition}, isbn = { 978-2874631054}, author = {Morros, J.R. and Albert Ali Salah and Ben Schouten and Carlos Segura Perales and Jordi Luque Serrano and Onkar Ambekar and Ceren Kayalar and Cem Keskin and Lale Akarun} } @conference {cVilaplana07a, title = {Face detection and segmentation on a hierarchical image representation}, booktitle = {15th European Signal Processing Conference}, year = {2007}, pages = {1955{\textendash}1959}, isbn = {0-7803-7403-7}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @conference {cDorea07, title = {Hierarchical partition-based representations for image sequences using trajectory merging criteria}, booktitle = {2007 International Conference on Acoustics, Speech and Signal Processing}, year = {2007}, author = {Dorea, C. and M. Pard{\`a}s and Marqu{\'e}s, F.} } @phdthesis {dChang07, title = {Hierarchical Partition-Based Representations of Motion-Coherent Regions For Video Object Segmentation}, year = {2007}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, author = {Chang, C.}, editor = {M. Pard{\`a}s and Marqu{\'e}s, F.} } @conference {cDorea07a, title = {A hierarchical trajectory-based representation for video}, booktitle = {Fith International Workshop on Content-Based Multimedia Indexing}, year = {2007}, pages = {275{\textendash}282}, isbn = {1-4244-1011-8}, author = {Dorea, C. and M. Pard{\`a}s and Marqu{\'e}s, F.} } @conference {cCalderero07, title = {Multiple view region matching using as a Lagrangian optimization problem}, booktitle = {2007 International Conference on Acoustics, Speech and Signal Processing}, year = {2007}, author = {Calderero, F. and Marqu{\'e}s, F. and Ortega, A.} } @article {aDescampe07, title = {Prefetching and caching strategies for remote and interactive browsing of JPEG2000 images}, journal = {IEEE transactions on image processing}, volume = {16}, number = {5}, year = {2007}, pages = {1339{\textendash}1354}, issn = {1057-7149}, author = {Descampe, A. and Vleeschouwer, D. and Iregui, M. and Macq, B. and Marqu{\'e}s, F.} } @conference {cGiro-i-Nieto07a, title = {Region-based annotation tool using partition trees}, booktitle = {International Conference on Semantic and Digital Media Technologies}, year = {2007}, month = {12/2007}, pages = {3{\textendash}4}, address = {Genova, Italy}, abstract = {

This paper presents an annotation tool for the man- ual and region-based annotation of still images. The selection of regions is achieved by navigating through a Partition Tree, a data structure that offers a multiscale representation of the image. The user interface provides a framework for the annotation of both atomic and composite semantic classes and generates an MPEG-7 XML compliant file.\ 

}, url = {http://hdl.handle.net/2117/10890}, author = {Xavier Gir{\'o}-i-Nieto and Camps, N. and Marqu{\'e}s, F.} } @conference {cVilaplana07, title = {Region-based hierarchical representation for object detection}, booktitle = {Fith International Workshop on Content-Based Multimedia Indexing}, year = {2007}, pages = {157{\textendash}164}, isbn = {0-7803-6727-8}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @inbook {bAbad07, title = {UPC Audio, Video and Multimodal Person Tracking Systems in the CLEAR Evaluation Campaign}, booktitle = {Lecture notes in computer science}, volume = {4122}, year = {2007}, pages = {93{\textendash}104}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

Reliable measures of person positions are needed for computational perception of human activities taking place in a smart-room environment. In this work, we present the Person Tracking systems developed at UPC for audio, video and audio-video modalities in the context of the EU funded CHIL project research activities. The aim of the designed systems, and particularly of the new contributions proposed, is to deal robustly in both single and multiperson localization tasks independently on the environmental conditions. Besides the technology description, experimental results conducted for the CLEAR evaluation workshop are also reported.

}, isbn = {978-3-540-69567-7}, doi = {10.1007/978-3-540-69568-4_6}, author = {Abad, A. and Cristian Canton-Ferrer and Segura, C. and Landabaso, J. and Macho, D. and Casas, J. and Hernando, J. and M. Pard{\`a}s and Nadeu, C.} } @inbook {bLuque06, title = {Audio, Video and Multimodal Person Identification in a Smart Room}, booktitle = {Lecture notes in computer science - Multimodal Technologies for Perception of Humans}, volume = {4122}, year = {2006}, pages = {258{\textendash}269}, issn = {0302-9743}, doi = {10.1007/978-3-540-69568-4_23}, author = {Luque, J. and Morros, J.R. and Garde, A. and Anguita, J. and Farr{\'u}s, M. and Macho, D. and Marqu{\'e}s, F. and Mart{\'\i}nez, C. and Ver{\'o}nica Vilaplana and Hernando, J.} } @inbook {bFerran06, title = {BPT Enhancement based on Syntactic and Semantic criteria}, booktitle = {Semantic Multimedia}, volume = {4306}, year = {2006}, pages = {184{\textendash}198}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

This paper presents two enhancements for the creation and analysis of Binary Partition Trees (BPTs). Firstly, the classic creation of BPT based on colour is expanded to include syntactic criteria derived from human perception. Secondly, a method to include semantic information in the BPT analysis is shown thanks to the definition of the BPT Semantic Neighborhood and the introduction of Semantic Trees. Both techniques aim at bridging the semantic gap between signal and semantics following a bottom-up and a top-down approach, respectively.

}, isbn = {978-3-540-49335-8}, doi = {10.1007/11930334_15}, url = {http://www.springerlink.com/content/u7201mw06545w057/}, author = {Ferran, C. and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Casas, J.} } @conference {cFerran06, title = {BPT Enhancement based on Syntactic and Semantic criteria}, booktitle = {1st International Conference on Semantic and Digital Media Technologies}, year = {2006}, pages = {184{\textendash}198}, abstract = {

This paper presents two enhancements for the creation and analysis of Binary Partition Trees (BPTs). Firstly, the classic creation of BPT based on colour is expanded to include syntactic criteria derived from human perception. Secondly, a method to include semantic information in the BPT analysis is shown thanks to the definition of the BPT Semantic Neighborhood and the introduction of Semantic Trees. Both techniques aim at bridging the semantic gap between signal and semantics following a bottom-up and a top-down approach, respectively.

}, isbn = {3-540-49335-2}, author = {Ferran, C. and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Casas, J.} } @phdthesis {dMarcello06, title = {Desarrollo de t{\'e}cnicas de procesado de im{\'a}genes, multitemporales y multisensoriales, de teledetecci{\'o}n para la detecci{\'o}n y seguimiento de estructuras oceanogr{\'a}ficas}, year = {2006}, school = {Universidad de Las Palmas de Gran Canaria (ULPGC)}, type = {phd}, author = {Marcello, J.}, editor = {Marqu{\'e}s, F. and F. Eugenio} } @phdthesis {dCorrea06, title = {Dual morphology-based and Bayesian approach for markerless human motion capture in natural interaction environments}, year = {2006}, school = {Universit{\'e} Catholique de Louvain (UCL)}, type = {phd}, url = {http://hdl.handle.net/2078.1/5033}, author = {Correa, P.}, editor = {Marqu{\'e}s, F. and Macq, B.} } @conference {cGiro-i-Nieto06, title = {The edition of the Wikipedia as an academic activity}, booktitle = {4rt. Congr{\'e}s Internacional de Doc{\`e}ncia Unversit{\`a}ria i Innovaci{\'o}}, year = {2006}, month = {07/2006}, pages = {{\textendash}}, address = {Barcelona, Catalonia}, abstract = {

This paper presents a learning activity around the Wikipedia, a free online encyclopaedia written by its users. Students are asked to write and review entries related to the course topics following a collaborative environment provided by the wiki tools. This paper proposes a seventeensteps methodology for this task in the framework of an academic course organized by topics. The activity has been successfully introduced in a two different schools of the Technical University of Catalonia. In its first edition, 81 new articles were added by 64 students from the EUETIT, in the second experience 60 articles were created and 14 reviewed by 43 students from the ETSETB.

[CIDUI 2006 website]

}, keywords = {wikipedia, ~cooperative, ~online}, isbn = {84-8458-240-X}, url = {http://hdl.handle.net/2117/13157}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Casas, J.} } @conference {cVilaplana06, title = {Face Recognition Using Groups of Images in Smart Room Scenarios}, booktitle = {International Conference on Image Processing}, year = {2006}, pages = {2071{\textendash}2074}, author = {Ver{\'o}nica Vilaplana and Mart{\'\i}nez, C. and Javier, C. and Marqu{\'e}s, F.} } @conference {cGiro-i-Nieto06a, title = {From Partition Trees to Semantic Trees}, booktitle = {International Workshop on Multimedia Content Representation, Classification and Security}, year = {2006}, pages = {306{\textendash}313}, isbn = {3-540-39392-7}, doi = {10.1007/11848035}, url = {http://dx.doi.org/10.1007/11848035_41}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @inbook {bGiro-i-Nieto06, title = {From partition trees to semantic trees}, booktitle = {Multimedia Content Representation, Classification and Security}, volume = {4105/2006}, number = {4105}, year = {2006}, pages = {306{\textendash}313}, abstract = {

This paper proposes a solution to bridge the gap between semantic and visual information formulated as a structural pattern recognition problem. Instances of semantic classes expressed by Description Graphs are detected on a region-based representation of visual data expressed with a Binary Partition Tree. The detection process builds instances of Semantic Trees on the top of the Binary Partition Tree using an encyclopedia of models organised as a hierarchy. At the leaves of the Semantic Tree, classes are defined by perceptual models containing a list of low-level descriptors. The proposed solution is assessed in different environments to show its flexibility.

}, issn = {0302-9743}, doi = {10.1007/11848035_41}, url = {http://www.springerlink.com/content/j148713624k48u3r/}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cGiro-i-Nieto06b, title = {From Partition Trees to Semantic Trees}, booktitle = {2ndas Jornades UPC de Investigaci{\'o}n en Autom{\'a}tica, Visi{\'o}n y Rob{\'o}tica}, year = {2006}, pages = {187{\textendash}194}, isbn = {84-7653-885-5}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cDorea06, title = {Generation of long-term color and motion coherent partitions}, booktitle = {International Conference on Image Processing}, year = {2006}, pages = {581{\textendash}584}, author = {Dorea, C. and M. Pard{\`a}s and Marqu{\'e}s, F.} } @conference {cNeumann06, title = {Multimodal Integration of Sensor Network}, booktitle = {Proceedings of 3rd IFIP Conference on Artificial Intelligence Applications \& Innovations}, year = {2006}, month = {09/2006}, publisher = {Springer}, organization = {Springer}, address = {Athens, Greece}, isbn = {978-0-387-34223}, author = {Neumann, J. and Casas, J. and Macho, D. and Ruiz-Hidalgo, J.} } @inbook {bNeumann06, title = {Multimodal Integration of Sensor Network}, booktitle = {Artificial Intelligence Applications and Innovations}, volume = {204}, year = {2006}, pages = {312{\textendash}323}, publisher = {Springer}, organization = {Springer}, address = {Boston}, abstract = {

At the Universitat Polit{\`e}cnica de Catalunya (UPC), a Smart Room has been equipped with 85 microphones and 8 cameras. This paper describes the setup of the sensors, gives an overview of the underlying hardware and software infrastructure and indicates possibilities for high- and low-level multi-modal interaction. An example of usage of the information collected from the distributed sensor network is explained in detail: the system supports a group of students that have to solve a lab assignment related problem.

}, isbn = {978-0-387-34223-8}, doi = {10.1007/0-387-34224-9_36}, author = {Neumann, J. and Casas, J. and Macho, D. and Ruiz-Hidalgo, J.} } @conference {cLuque06, title = {Multimodal Person Identification in a Smart Room}, booktitle = {IV Jornadas en Tecnolog{\'\i}a del Habla}, year = {2006}, pages = {327{\textendash}331}, isbn = {84-96214-82-6}, author = {Luque, J. and Morros, J.R. and Anguita, J. and Farr{\'u}s, M. and Macho, D. and Marqu{\'e}s, F. and Mart{\'\i}nez, C. and Ver{\'o}nica Vilaplana and Hernando, J.} } @conference {cDescampe06, title = {Pre-fetching strategies for remote and interactive browsing of JPEG2000 images}, booktitle = {International Conference on Image Processing}, year = {2006}, pages = {3203{\textendash}3206}, author = {Descampe, A. and De Vleeschouwer, C. and Iregui, M. and Macq, B. and Marqu{\'e}s, F.} } @conference {cLopez06, title = {Rotating convection: Eckhauss-Benjamin-Feir instability}, booktitle = {59th Annual Meeting of the APS Division of Fluid Dynamics}, year = {2006}, pages = {2004{\textendash}2004}, isbn = {-}, author = {Lopez, J. and Mercader, M. and Marqu{\'e}s, F. and Batiste, O.} } @conference {cAbad06, title = {UPC Audio, Video and Multimodal Person Tracking Systems in the CLEAR Evaluation Campaign}, booktitle = {CLEAR{\textquoteright}06 Evaluation Campaign and Workshop - Classification of Events, Activities and Relationships}, year = {2006}, pages = {93{\textendash}104}, isbn = {978-3-540-69567-7}, author = {Abad, A. and Cristian Canton-Ferrer and Cristian Canton-Ferrer and Segura, C. and Landabaso, J. and Macho, D. and Casas, J. and Hernando, J. and M. Pard{\`a}s and Nadeu, C.} } @inbook {bGiro-i-Nieto05, title = {Automatic extraction and analysis of visual objects information}, booktitle = {Multimedia content and the semantic web}, year = {2005}, pages = {203{\textendash}221}, publisher = {Wiley}, organization = {Wiley}, chapter = {7}, isbn = {978-0-470-85753-3}, doi = {10.1002/0470012617.ch7}, url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-0470857536.html}, author = {Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Marqu{\'e}s, F. and Salembier, P.} } @article {aMarcello05, title = {Automatic tool for the precise detection of upwelling and filaments in remote sensing imagery}, journal = {IEEE transactions on geoscience and remote sensing}, volume = {43}, number = {7}, year = {2005}, pages = {1605{\textendash}1616}, issn = {0196-2892}, author = {Marcello, J. and Marqu{\'e}s, F. and F. Eugenio} } @conference {cAnton05, title = {Collaborative Network Space: Infrastructure and Learning Application}, booktitle = {IEEE Region 8 EUROCON 2005 Conference: Computer as a tool.}, year = {2005}, pages = {803{\textendash}806}, isbn = {1-4244-0050-3}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01630054}, author = {Anton, M. and Vall-Llosera, M. and Jordi Torres and Romeu, J. and Jofre, L. and Sole, F. and Marqu{\'e}s, F. and Sabate, F. and Berenguer, J.} } @conference {cGiro-i-Nieto05, title = {Detection of Semantic Objects using Description Graphs}, booktitle = {IEEE International Conference on Image Processing}, year = {2005}, address = {Genova, Italy}, abstract = {

This paper presents a technique to detect instances of classes (objects) according to their semantic definition in the form of a description graph. Classes are defined as combinations of instances of lower level semantic classes and allow the definition of a semantic tree that organizes classes in semantic levels. At the bottom level of the semantic tree, classes are defined by a perceptual model containing a list of low-level descriptors. The proposed detection algorithm follows a bottom-up/top-down approach, building semantic trees on a region-based representation of the media. The flexibility of the approach is assessed on different examples of planar objects, such as frontal faces, groups of islands, flags and traffic signs.

}, keywords = {Detection algorithms, Explosions, Face detection, Image databases, Indexing, MPEG 7 Standard, Object detection, Testing, Traffic control, Tree graphs}, isbn = {0-7803-9135-7}, doi = {10.1109/ICIP.2005.1529972}, url = {http://dx.doi.org/10.1109/ICIP.2005.1529972}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cMarques05, title = {El concepto NetCampus}, booktitle = {3es Jornadas de la C{\'a}tedra Telef{\'o}nica-UPC}, year = {2005}, pages = {15{\textendash}20}, url = {https://catedratelefonica.upc.edu/documents/llibres/docs/el_espacio_innovador_y_la_red.pdf}, author = {Marqu{\'e}s, F. and Jofre, L. and Sole, F. and Sabate, F. and Berenguer, J. and Romeu, J. and Jordi Torres} } @book {eJofre05, title = {El "Espacio Innovador" y la red}, year = {2005}, url = {http://www.upc.edu/web/CatedraTelefonicaUPC}, author = {Jofre, L. and Sole, F. and Sabate, F. and Berenguer, J. and Marqu{\'e}s, F. and Romeu, J. and Jordi Torres} } @conference {cChang05, title = {A Motion-based Binary Partition Tree approach to Video Object Segmentation}, booktitle = {IEEE International Conference on Image Processing}, year = {2005}, pages = {430{\textendash}433}, isbn = {0-7803-9135-7}, author = {Chang, C. and M. Pard{\`a}s and Marqu{\'e}s, F.} } @conference {cVilaplana05, title = {Region-based extraction and analysis of visual objects information}, booktitle = {Fourth International Workshop on Content-Based Multimedia Indexing, CBMI 2005}, year = {2005}, address = {Riga, Letonia}, abstract = {

In this paper, we propose a strategy to detect objects from\ still images that relies on combining two types of models: a\ perceptual and a structural model. The algorithms that are\ proposed for both types of models make use of a regionbased description of the image relying on a Binary Partition\ Tree. Perceptual models link the low-level signal description with semantic classes of limited variability. Structural\ models represent the common structure of all instances by\ decomposing the semantic object into simpler objects and\ by defining the relations between them using a DescriptionGraph.

}, isbn = {0-7803-6293-4}, author = {Ver{\'o}nica Vilaplana and Xavier Gir{\'o}-i-Nieto and Salembier, P. and Marqu{\'e}s, F.} } @conference {cCorrea05, title = {Silhouette-based Probabilistic 2D Human Motion Estimation for Real-Time Applications}, booktitle = {IEEE International Conference on Image Processing}, year = {2005}, pages = {836{\textendash}839}, isbn = {0-7803-9135-7}, author = {Correa, P. and Czyz, J. and Umeda, T. and Marqu{\'e}s, F. and Marichal, X. and Macq, B.} } @conference {cVilaplana05a, title = {Support Vector Data Description Based on PCA Features for Face Detection}, booktitle = {13th European Signal Processing Conference}, year = {2005}, isbn = {0-7803-6300-0}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @conference {cEugenio04, title = {An automated multisensor satellite imagery registration technique based on the optimization of contour features}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium 2004}, year = {2004}, pages = {1410{\textendash}1413}, author = {F. Eugenio and Marcello, J. and Marqu{\'e}s, F.} } @conference {cBroquetas04, title = {Definici{\'o} d{\textquoteright}un Master Internacional de Recerca: la proposta del Departament de Teoria del Senyal i Comunicacions}, booktitle = {Jornada de reflexi{\'o}n y trabajo sobre el modelo docente de la UPC en el Espacio Europeo de ecuaci{\'o}n Superior (EEES)}, year = {2004}, pages = {1{\textendash}3}, author = {Broquetas, A. and Hernando, J. and Marqu{\'e}s, F. and Romeu, J.} } @book {eJofre04, title = {El {\textquoteright}Profesional Innovador{\textquoteright} y la red}, year = {2004}, url = {http://catedratelefonica.upc.edu/documents/llibres/docs/jornada_2004_catedra_telf_upc.pdf}, author = {Jofre, L. and Sole, F. and Sabate, F. and Marqu{\'e}s, F. and Romeu, J. and Jordi Torres} } @conference {cLobo04, title = {Enhanced audio data hiding synchronization using non-linear filter}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing}, year = {2004}, pages = {885{\textendash}888}, author = {Lobo, A. and Bas, P. and Marqu{\'e}s, F.} } @inbook {bPardas04, title = {The InterFace Software Platform for Interactive Virtual Characters}, booktitle = {Mpeg-4 facial animation: the standard, implementation and applications}, year = {2004}, pages = {169{\textendash}183}, isbn = {0-470-84465-5}, author = {M. Pard{\`a}s and Pandzic, I. and Cannella, M. and Davoine, F. and Forchheimer, R. and Lavagetto, F. and Marriott, A. and Malassiotis, S.} } @conference {cSalerno04, title = {Object recognition based on Binary Partition Trees}, booktitle = {IEEE International Conference on Image Processing}, year = {2004}, pages = {929{\textendash}932}, author = {Salerno, O. and M. Pard{\`a}s and Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @phdthesis {dMorros04, title = {Optimization of Segmentation Based Video Sequence Coding Techniques: Application to Content Based Functionalities}, year = {2004}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

This work addresses the problem of video compression with content-based functionalities in the framework of segmentation-based video coding systems. Two major problems are considered. The first one is related with coding optimality in segmentation-based coding systems. Regarding this subject, the feasibility of a rate-distortion approach for a complete region-based coding system is shown. The second one is how to address content-based functionalities in the coding system proposed as a solution of the first problem. Optimality, as defined in the framework of rate-distortion theory, deals with obtaining a representation of the video sequence that leads to a minimum distortion of the coded signal for a given bit budget. In the case of segmentation-based coding systems this means to obtain an {\textquoteright}optimal{\textquoteright} partition together with the best coding technique for each region of this partition so that the result is optimal in an operational rate-distortion sense. The problem is formalized for independent, non-scalable coding. An algorithm to solve this problem is provided as well. This algorithms is applied to a specific segmentation-based coding system, the so called SESAME. In SESAME, each frame is segmented into a set of regions, that are coded independently. Segmentation involves both spatial and motion homogeneity criteria. To exploit temporal redundancy, a prediction for both the partition and the texture of the current frame is created by using motion information. The time evolution of each region is defined along the sequence (time tracking). The results are optimal (or near-optimal) for the given framework in a rate-distortion sense. The definition of the coding strategy involves a global optimization of the partition as well as of the coding technique/quality level for each region. Later, the investigation is also extended to the problem of video coding optimization in the framework of a scalable video coding system that can address content-based functionalities. The focus is set in the various types of content-based scalability and object tracking. The generality of the problem has also been extended by including the spatial and temporal dependencies between frames and scalability layers into the optimization schema. In this case the solution implies finding the optimal partition and set of quantizers for both the base and the enhancement layers. Due to the coding dependencies of the enhancement layer with respect to the base layer, the partition and the set of quantizers of the enhancement layer depend on the decisions made on the base layer. Also, a solution for the independent optimization problem (i.e. without tacking into account dependencies between different frames of scalability layers) has been proposed to reduce the computational complexity. These solutions are used to extend the SESAME coding system. The extended coding system, named XSESAME, supports different types of scalability (PSNR, Spatial and temporal) as well as content-based functionalities, such as content-based scalability and object tracking. Two different operating modes for region selection in the enhancement layer have been presented: One (supervised) aimed at providing content-based functionalities at the enhancement layer and the other (unsupervised) aimed at coding efficiency, without content-based functionalities. Integration of object tracking into the segmentation-based coding system is also investigated. In the general case, tracking is a very complex problem. If this capability has to be integrated into a coding system, additional problems arise due to conflicting requirements between coding efficiency and tracking accuracy. This is solved by using a double partition approach, where pure spatial criteria are used to re-segment the partition used for coding. The projection of the re-segmented partition results in more precise adaptation to object contours. A merging step is performed a posteriori to eliminate the excess of regions originated by the re-segmentation.

}, url = {http://hdl.handle.net/10803/6888}, author = {Morros, J.R.}, editor = {Marqu{\'e}s, F.} } @conference {cMarcello04, title = {Precise upwelling and filaments automatic extraction from multisensorial imagery}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium 2004}, year = {2004}, pages = {2018{\textendash}2021}, author = {Marcello, J. and F. Eugenio and Marqu{\'e}s, F.} } @conference {cDorea04, title = {A region-based algorithm for image segmentation and parametric motion estimation}, booktitle = {Image Analysis for Multimedia Interactive Services}, year = {2004}, isbn = {972-98115-7-1}, author = {Dorea, C. and M. Pard{\`a}s and Marqu{\'e}s, F.} } @conference {cJofre04, title = {Una Enginyeria per a la Societat del Coneixement}, booktitle = {II Congr{\'e}s d{\textquoteright}Enginyeria en Llengua Catalana}, year = {2004}, url = {http://www.eicc.cat/celc/formacio.htm}, author = {Jofre, L. and Sole, F. and Sabate, F. and Marqu{\'e}s, F. and Romeu, J. and Jordi Torres} } @conference {cCasas03, title = {Advanced content-based semantic scene analysis and information retrieval: the Schema Project}, booktitle = {4th European Workshop on Image Analysis for Multimedia Interactive Services_x000D_}, year = {2003}, pages = {43{\textendash}45}, abstract = {

The aim of the SCHEMA Network of Excellence is to bring together a critical mass of universities, research centers, industrial partners and end users, in order to design a reference system for content-based semantic scene analysis, interpretation and understanding. Relevant research areas include: contentbased multimedia analysis and automatic annotation of semantic multimedia content, combined textual and multimedia information retrieval, semantic-web, MPEG-7 and MPEG-21 standards, user interfaces and human factors. In this paper, recent advances in content-based analysis, indexing and retrieval of digital media within the SCHEMA Network are presented. These advances will
be integrated in the SCHEMA module-based, expandable reference system.

}, author = {Izquierdo, E. and Casas, J. and Leonardi, R. and Migliorati, P. and O{\textquoteright}Connor, N. and Kompatsiaris, I. and Strintzis, M.} } @article {aEugenio03, title = {Automatic satellite image georeferencing using a contour matching approach}, journal = {IEEE transactions on geoscience and remote sensing}, volume = {41}, number = {12}, year = {2003}, pages = {2869{\textendash}2880}, issn = {0196-2892}, author = {F. Eugenio and Marqu{\'e}s, F.} } @conference {cEugenio03a, title = {Automatic structures detection and spatial registration using multisensor satellite imagery}, booktitle = {Proceedings of the International Geoscience and Remote Sensing Symposium,}, year = {2003}, pages = {1038{\textendash}1040}, author = {F. Eugenio and Rovaris, E. and Marcello, J. and Marqu{\'e}s, F.} } @conference {cGiro-i-Nieto03, title = {Detection of Semantic Entities using Description Graphs}, booktitle = {4th European Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS)}, year = {2003}, month = {04/2003}, address = {London, England}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cEugenio03, title = {Marine coastal dynamic study using an automatic structure detection and spatial registration tool}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS-03)}, year = {2003}, pages = {1{\textendash}3}, isbn = {0-7803-7930-6}, author = {F. Eugenio and Marcello, J. and Marqu{\'e}s, F.} } @conference {cO{\textquoteright}Connor03, title = {Region and object segmentation algorithms in the Qimera segmentation platform}, booktitle = {Third International Workshop on Content-Based Multimedia Indexing}, year = {2003}, pages = {95{\textendash}103}, abstract = {

In this paper we present the Qimera segmentation platform and describe the different approaches to segmentation that have been implemented in the system to date. Analysis techniques have been implemented for both region-based and object-based segmentation. The region-based segmentation algorithms include: a colour segmentation algorithm based on a modified Recursive Shortest Spanning Tree (RSST) approach, an implementation of a colour image segmentation algorithm based on the K-Means-with-Connectivity-Constraint (KMCC) algorithm and an approach based on the Expectation Maximization (EM) algorithm applied in a 6D colour/texture space. A semi-automatic approach to object segmentation that uses the modified RSST approach is outlined. An automatic object segmentation approach via snake propagation within a level-set framework is also described. Illustrative segmentation results are presented in all cases. Plans for future research within the Qimera project are also discussed.

}, isbn = {978-84-612-2373-2}, author = {O{\textquoteright}Connor, N. and Sav, S. and Adamek, T. and Mezaris, V. and Kompatsiaris, I. and Lui, T. and Izquierdo, E. and Ferran, C. and Casas, J.} } @inbook {cGiro-i-Nieto03a, title = {Semantic Entity Detection Using Description Graphs}, booktitle = {Digital Media Processing for Multimedia Interactive Services}, year = {2003}, pages = {39{\textendash}42}, publisher = {World Scientific Publishing Co.}, organization = {World Scientific Publishing Co.}, address = {Singapore}, abstract = {

This paper presents a technique for the detection of Semantic Entities (SEs) in multimedia content. A definition of a SE in terms of lower-level SEs and their Relations (Rs) is proposed using Description Graphs (DGs). By analyzing the a/v information, an instance DG is built to be compared with a model DG of the SE. As a result, a confidence value is computed to express how well the SE is represented in the content. Examples of the use of this approach are presented in two different applications: detection of frontal faces and recognition of clusters of islands.

}, isbn = {981-238-355-7}, url = {http://books.google.es/books?id=vVvJINURimIC\&printsec=frontcover\&hl=ca\&source=gbs_ge_summary_r\&cad=0$\#$v=onepage\&q\&f=false}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @article {aComas03, title = {Unbalanced multiple description video coding based on a rate-distortion optimization}, journal = {Eurasip Journal on Applied Signal Processing}, volume = {2003}, number = {1}, year = {2003}, pages = {81{\textendash}90}, issn = {1110-8657}, author = {Comas, D. and Singh, R. and Ortega, A. and Marqu{\'e}s, F.} } @conference {cAvrithis03, title = {Unified access to heterogeneous audiovisual archives}, booktitle = {International Conference on Knowledge Management}, year = {2003}, pages = {1{\textendash}2}, address = {Graz, Austria}, author = {Avrithis, Y. and Stamou, G. and Wallace, M. and Marqu{\'e}s, F. and Salembier, P. and Xavier Gir{\'o}-i-Nieto and Haas, W. and Vallant, H. and Zufferey, M.} } @article {aAvrithis03, title = {Unified Access to Heterogeneous Audiovisual Archives}, journal = {Journal of universal computer science}, volume = {9}, number = {6}, year = {2003}, pages = {510{\textendash}519}, abstract = {

In this paper, an integrated information system is presented that offers enhanced search and retrieval capabilities to users of heterogeneous digital audiovisual (a/v) archives. This innovative system exploits the advances in handlings a/v content and related metadata, as introduced by MPEG-4 and worked out by MPEG-7, to offer advanced services characterized by the tri-fold semantic phrasing of the request (query), unified handling and personalized response. The proposed system is targeting the intelligent extraction of semantic information from a/v and text related data taking into account the nature of the queries that users my issue, and the context determined by user profiles. It also provides a personalization process of the response in order to provide end_users with desired information. From a technical point of view, the FAETHON system plays the role of an intermediate access server residing between the end users and multiple heterogeneous audiovisual archives organized according to the new MPEG standards.

}, issn = {0948-6968}, doi = {10.3217/jucs-009-06-0510}, url = {http://www.jucs.org/jucs_9_6}, author = {Avrithis, Y. and Stamou, G. and Wallace, M. and Marqu{\'e}s, F. and Salembier, P. and Xavier Gir{\'o}-i-Nieto and Haas, W. and Vallant, H. and Zufferey, M.} } @article {aSchelkens03, title = {Wavelet coding of volumetric medical datasets}, journal = {Medical Imaging, IEEE Transactions on}, volume = {22}, year = {2003}, month = {march}, pages = {441 -458}, abstract = {

Several techniques based on the three-dimensional (3-D) discrete cosine transform (DCT) have been proposed for volumetric data coding. These techniques fail to provide lossless coding coupled with quality and resolution scalability, which is a significant drawback for medical applications. This paper gives an overview of several state-of-the-art 3-D wavelet coders that do meet these requirements and proposes new compression methods exploiting the quadtree and block-based coding concepts, layered zero-coding principles, and context-based arithmetic coding. Additionally, a new 3-D DCT-based coding scheme is designed and used for benchmarking. The proposed wavelet-based coding algorithms produce embedded data streams that can be decoded up to the lossless level and support the desired set of functionality constraints. Moreover, objective and subjective quality evaluation on various medical volumetric datasets shows that the proposed algorithms provide competitive lossy and lossless compression results when compared with the state-of-the-art.

}, keywords = {Algorithms, Computer-Assisted, Data Compression, discrete cosine transforms, embedded coding, embedded data streams, functionality constraints, image coding, Image Enhancement, Image Interpretation, Imaging, JPEG2000, layered zero coding, lossless compression, medical diagnostic imaging, medical image compression, medical image processing, Numerical Analysis, objective quality evaluation, progressive image transmission, quadtree coding, reviews, Signal Processing, subjective quality evaluation, Three-Dimensional, volumetric coding, wavelet transforms}, issn = {0278-0062}, doi = {10.1109/TMI.2003.809582}, author = {Schelkens, P. and Munteanu, A. and Barbarien, J. and Galca, M. and Xavier Gir{\'o}-i-Nieto and Cornelis, J.} } @conference {cMarcelo02, title = {Automatic feature extraction from multisensorial oceanographic imagery}, booktitle = {International Geoscience and Remote Sensing Symposium, 2002. IGARSS {\textquoteright}02. 2002 IEEE}, year = {2002}, pages = {4{\textendash}8}, isbn = {0-7803-7536-0}, author = {Marcello, J. and Marqu{\'e}s, F. and F. Eugenio} } @conference {cVidal02, title = {Color Image Watermarking Using Channel-State Knowledge}, booktitle = {SPIE Electronic Imaging 2002}, year = {2002}, pages = {214{\textendash}221}, isbn = {0-8194-4415-4}, author = {Vidal, J. and Maribel, M. and Elisa Sayrol} } @conference {cEugenio02, title = {A contour-based approach to automatic and accurate registration of multitemporal and multisensor satellite imagery}, booktitle = {International Geoscience and Remote Sensing Symposium, 2002. IGARSS {\textquoteright}02. 2002 IEEE}, year = {2002}, pages = {1{\textendash}3}, isbn = {0-7803-7536-0}, author = {F. Eugenio and Marqu{\'e}s, F. and Marcello, J.} } @inbook {bSalembier02b, title = {Description of a Single Multimedia Document}, booktitle = {Introduction to the mpeg-7: multimedia content description interface}, year = {2002}, pages = {111{\textendash}138}, publisher = {Wiley}, organization = {Wiley}, edition = {B. S. Manjunath, P. Salembier, T. Sikora (Eds.)}, chapter = {8}, isbn = {0471486787}, author = {Benitez, A.B and Martinez, J.M and Rising, H and Salembier, P.} } @article {aMarques02, title = {Face segmentation and tracking based on connected operators and partition projection}, journal = {Pattern recognition}, volume = {35}, number = {3}, year = {2002}, pages = {601{\textendash}614}, issn = {0031-3203}, author = {Marqu{\'e}s, F. and Ver{\'o}nica Vilaplana} } @conference {cMarques02a, title = {Facial Feature Segmentation from Frontal View Images}, booktitle = {11th European Signal Processing Conference (EUSIPCO 2002)}, year = {2002}, pages = {33{\textendash}36}, author = {Marqu{\'e}s, F. and Sobrevals, C.} } @conference {cVallverdu02, title = {Graphical study of signals and systems}, booktitle = {14th annual World Conference on Educational Multimedia, Hypermedia \& Telecommunications}, year = {2002}, isbn = {0-8186-7919-0}, author = {Vallverdu, F. and Elisa Sayrol and Gasull, A. and Salavedra, J. and Moreno, A.} } @book {eSalembier02, title = {Introduction to the mpeg-7: multimedia content description interface}, year = {2002}, publisher = {Wiley}, organization = {Wiley}, isbn = {0471486787}, author = {Manjunath, B.S and Salembier, P. and Sikora, T} } @conference {cGiro-i-Nieto02, title = {MPEG-7 Descriptors for Earth Observation Satellites}, booktitle = {International Astronautical Congress}, year = {2002}, month = {09/2002}, pages = {1{\textendash}4}, publisher = {Inernational Astronautical Federation}, organization = {Inernational Astronautical Federation}, address = {Houston, Texas (USA)}, abstract = {

The amount of digital multimedia information has experienced a spectacular growth during the last years thanks to the advances on digital systems of image, video and audio acquisition. As a response to the need of organizing all this information, ISO/IEC has developed a new standard for multimedia content description called MPEG-7. Among other topics, MPEG-7 defines a set of multimedia descriptors that can be automatically generated using signal processing techniques. Earth Observation Satellites generate large quantities of images stored on enormous databases that can take advantage of the new standard. An automatic indexation of these images using MPEG-7 meta-data can improve their contents management as well as simplify interaction between independent databases. This paper gives an overall description on MPEG-7 standard focusing on the low-level Visual Descriptors. These descriptors can be grouped into four categories: color, texture, shape and motion. Visual Color Descriptors represent the color distribution of an image in terms of a specified color space. Visual Texture Descriptors define the visual pattern of an image according to its homogeneities and non-homogeneities. Visual Shape Descriptors describe the shape of 2D and 3D objects being, at the same time, invariant to scaling, rotation and translation. Motion Descriptors give the essential characteristics of objects and camera motions.

These descriptors can be used individually or in combination to index and retrieve satellite images of the Earth from a database. For example, oceans and glaciers can be discerned based on their Color Descriptors, also cities and desert based on the Texture Descriptors, island images can be grouped using the Shape descriptors and cyclone trajectories studied and compared using Motion Descriptors.

}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Marcello, J. and F. Eugenio} } @conference {cMarques02, title = {Object matching based on partition information}, booktitle = {IEEE International Conference on Image Processing}, year = {2002}, pages = {829{\textendash}832}, isbn = {0-7803-7623-4}, doi = {10.1109/icip.2002.1040079}, author = {Marqu{\'e}s, F. and M. Pard{\`a}s and Morros, J.R.} } @article {aMech02, title = {Objective evaluation criteria for 2-D shape estimation results of moving objects}, journal = {Eurasip Journal on Applied Signal Processing}, volume = {2002}, number = {4}, year = {2002}, pages = {401{\textendash}409}, issn = {1110-8657}, author = {Mech, R. and Marqu{\'e}s, F.} } @conference {cGasull02, title = {Oil Spills Detection in SAR Images using Mathematical Morphology}, booktitle = {11th European Signal Processing Conference (EUSIPCO 2002)}, year = {2002}, pages = {25{\textendash}28}, author = {Gasull, A. and F{\'a}bregas, F.X. and Jim{\'e}nez, J. and Marqu{\'e}s, F. and Moreno, V. and Herrero, M.} } @book {eSayrol02, title = {Senyals i sistemes anal{\`o}gics: una introducci{\'o} pr{\`a}ctica}, year = {2002}, isbn = {84-8301-610-9}, url = {http://www.edicionsupc.es}, author = {Elisa Sayrol and Gasull, A. and Moreno, A. and Salavedra, J. and Vallverdu, F.} } @conference {cPardas01a, title = {Facial Parameter Extraction System based on Active Contours}, booktitle = {IEEE International Conference on Image Processing}, year = {2001}, pages = {1058{\textendash}1061}, author = {M. Pard{\`a}s and Marcos, L.} } @conference {cSayrol01a, title = {Graphical Study of Signals and Systems}, booktitle = {International Conference on Acoustics, Speech and Signal Processing ICASSP{\textquoteright}01}, year = {2001}, isbn = {0-7803-1775-0}, author = {Elisa Sayrol and Gasull, A. and Moreno, A. and Salavedra, J. and Vallverdu, F.} } @conference {cVilaplana01, title = {Join detection and segmentation of human faces in color images}, booktitle = {International Conference on Augmented, Virtual Environments and 3D Imaging}, year = {2001}, pages = {347{\textendash}350}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @conference {cBescos01, title = {An open integrated system for video indexing}, booktitle = {International Workshop on Content-Based Multimedia Indexing}, year = {2001}, pages = {322{\textendash}333}, author = {Bescos, J. and Torres, L. and Menendez, J. and Vil{\`a}, J. and Cisneros, G. and Marqu{\'e}s, F. and Cabrera, J. and Mart{\'\i}nez, J.} } @conference {cEugenio01, title = {Pixel and sub-pixel accuracy in satellite image georeferencing using an automatic contour matching approach}, booktitle = {IEEE International Conference on Image Processing}, year = {2001}, isbn = {0-7803-6727-8}, author = {F. Eugenio and Marqu{\'e}s, F. and Marcello, J.} } @article {aEugenio01, title = {A real-time automatic acquisition, processing and distribution system for AVHRR and SeaWIFS imagery}, journal = {IEEE geoscience electronics society newsletter}, volume = {-}, number = {Issue 20}, year = {2001}, pages = {10{\textendash}15}, issn = {0161-7869}, author = {F. Eugenio and Marcello, J. and Marqu{\'e}s, F. and Hernandez-Guerra, A. and Rovaris, E.} } @article {aVilaplana01, title = {A region-based approach to face segmentation and tracking in video sequences}, journal = {Latin american applied research}, volume = {31}, number = {2}, year = {2001}, pages = {99{\textendash}106}, issn = {0327-0793}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @book {eSayrol01, title = {Senyals i sistemes anal{\`o}gics: una introducci{\'o} pr{\`a}ctica}, year = {2001}, isbn = {84-8301-510-2}, url = {www.edicionsupc.es}, author = {Elisa Sayrol and Gasull, A. and Moreno, A. and Salavedra, J. and Vallverdu, F.} } @conference {cGasull00, title = {619 - un ejemplo de dise{\~n}o del laboratorio para asignaturas con cr{\'e}ditos te{\'o}ricos y pr{\'a}cticos}, booktitle = {VIII Congreso Universitario de Innovaci{\'o}n Educativa en las Ense{\~n}anzas T{\'e}cnicas}, year = {2000}, pages = {233{\textendash}240}, isbn = {84-7585-380-3}, author = {Gasull, A. and Moreno, A. and Salavedra, J. and Elisa Sayrol and Vallverdu, F.} } @conference {cEugenio00a, title = {Accurate and automatic NOAA-AVHRR image navigation using a global contour matching approach}, booktitle = {International Geoscience and remote Sensing Symposium}, year = {2000}, pages = {639{\textendash}642}, isbn = {0-7803-6362-0}, author = {F. Eugenio and Marqu{\'e}s, F. and G{\'o}mez, L. and Suarez, E. and Rovaris, E.} } @inbook {bMarques00, title = {CODING-ORIENTED SEGMENTATION OF VIDEO SEQUENCES}, booktitle = {Video coding: the second generation approach}, year = {2000}, pages = {79{\textendash}124}, isbn = {079239680}, author = {Marqu{\'e}s, F.} } @conference {cEugenio00, title = {A contour matching approach for accurate NOAA-AVHRR image navigation}, booktitle = {10th European Signal Processing Conference (EUSIPCO 2000)}, year = {2000}, isbn = {952-15-0447-1}, author = {F. Eugenio and Marqu{\'e}s, F. and Suarez, E. and Rovaris, E.} } @article {aNunes00, title = {A contour-based approach to binary shape coding using a multiple grid chain code}, journal = {Signal processing: image communication}, volume = {15}, number = {7-8}, year = {2000}, pages = {585{\textendash}599}, issn = {0923-5965}, author = {Nunes, P. and Marqu{\'e}s, F. and Pereira, F. and Gasull, A.} } @conference {cVidal00, title = {Data Hidding in Color Images using Perceptual Models}, booktitle = {COST 254 Intelligent Processing and Facilities for Communications Terminals}, year = {2000}, pages = {21{\textendash}25}, author = {Vidal, J. and Elisa Sayrol and Maribel, M.} } @conference {cBroquetas00, title = {Detecci{\'o}n de vertidos de petr{\'o}leo en el mar a partir de im{\'a}genes SAR}, booktitle = {XV Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {2000}, pages = {327{\textendash}328}, isbn = {84-600-9597-5}, author = {Broquetas, A. and Toronjo, C. and Gasull, A. and F{\'a}bregas, F.X. and Moreno, V. and Herrero, M.} } @book {eMarques00, title = {Introduction fo interpretation of graphic images}, year = {2000}, isbn = {0-8194-2380-7}, editor = {Marqu{\'e}s, F.} } @conference {cMarques00, title = {A morphological approach for segmentation and tracking of human faces}, booktitle = {International Symposium on Image/Video Communications over Fixed and Mobile Networks}, year = {2000}, pages = {38{\textendash}43}, author = {Marqu{\'e}s, F. and Ver{\'o}nica Vilaplana} } @conference {cMarques00a, title = {A morphological approach for segmentation and tracking of human faces}, booktitle = {15th IAPR International Conference on Pattern Recognition}, year = {2000}, isbn = {0-7695-0750-6}, author = {Marqu{\'e}s, F. and Ver{\'o}nica Vilaplana} } @conference {cMarcotegui00, title = {Partition-based image representation as basis for user-assisted segmentation}, booktitle = {IEEE International Conference on Image Processing}, year = {2000}, isbn = {0-7803-6300-0}, author = {Marcotegui, B. and Marqu{\'e}s, F. and Wollborn, M.} } @conference {cMaziere00, title = {Segmentation and tracking of video objects for content-based video indexing}, booktitle = {IEEE International Conference on Multimedia and Expo, ICME{\textquoteright}2000}, year = {2000}, pages = {305{\textendash}309}, address = {New York City, NY, USA}, author = {Maziere, M. and Chassaing, F. and Garrido, L. and Salembier, P.} } @conference {cSayrol00, title = {Una Aproximaci{\'o}n experimental a las Se{\~n}ales y Sistemas Anal{\'o}gicos}, booktitle = {XV Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {2000}, pages = {329{\textendash}330}, isbn = {978-972-8865-74-0}, author = {Elisa Sayrol and Gasull, A. and Moreno, A. and Salavedra, J. and Vallverdu, F.} } @conference {cGasull99, title = {Editor gr{\'a}fico de figuras MATLAB}, booktitle = {III Congreso de Usuarios de MATLAB}, year = {1999}, pages = {219{\textendash}227}, isbn = {84-699-1358-1}, author = {Gasull, A. and Elisa Sayrol and Moreno, A. and Vallverdu, F. and Salavedra, J. and Albert Oliveras} } @inbook {aTorres99, title = {Face Location and Recognition for Video Indexing in the Hypermedia Project}, booktitle = {Lecture Notes in Computer Science}, volume = {1629}, year = {1999}, issn = {364-377}, doi = {DOI: 10.1007/3-540-48757-3_25}, author = {Torres, L. and Marqu{\'e}s, F. and Lorente, L. and Ver{\'o}nica Vilaplana} } @conference {cTorres99, title = {Face location and recognition for video indexing in the Hypermedia project}, booktitle = {European Conference on Multimedia Applications, Services and Techniques}, year = {1999}, pages = {364{\textendash}377}, author = {Torres, L. and Marqu{\'e}s, F. and Lorente, L. and Ver{\'o}nica Vilaplana} } @conference {cMarques99a, title = {Human face segmentation and tracking using connected components and partition projection}, booktitle = {1999 IEEE INternational Conference on Image Processing}, year = {1999}, isbn = {0-7803-5470-2}, author = {Marqu{\'e}s, F. and Ver{\'o}nica Vilaplana and Buxes, A.} } @conference {cMarques99, title = {A morphological approach for human face segmentation and tracking}, booktitle = {Workshop on Image Analysis for Multimedia Services}, year = {1999}, pages = {41{\textendash}44}, author = {Marqu{\'e}s, F. and Ver{\'o}nica Vilaplana and Buxes, A.} } @article {pMarques99, title = {Partition Coding Method and Device}, number = {994004364}, year = {1999}, month = {02/1999}, type = {Invention patent}, address = {International}, issn = {994004364}, author = {Marqu{\'e}s, F.} } @article {pMarques99a, title = {Partition Decoding Method and Device}, number = {994017614}, year = {1999}, month = {07/1999}, type = {Invention patent}, address = {International}, issn = {994017614}, author = {Marqu{\'e}s, F. and Gomila, C. and Gasull, A.} } @conference {cMorros99, title = {A proposal for dependent optimization in scalabale region-based coding systems}, booktitle = {1999 IEEE International Conference on Image Processing}, year = {1999}, pages = {295{\textendash}299}, abstract = {

We address in this paper the problem of optimal coding in the framework of region-based video coding systems, with a special stress on content-basedfunctionalities. We present a coding system that can provide scaled layers (using PSNR or temporal content-based scalability) such that each one has an optimal partition with optimal bit allocation among the resulting regions. This coding system is based on a de- pendent optimization algorithm that can provide joint opti- mality for a group of layers or a group of frames.

}, isbn = {978-1-59593-733-9}, doi = {10.1109/ICIP.1999.819598}, author = {Morros, J.R. and Marqu{\'e}s, F.} } @conference {cVilaplana99, title = {A region-based approach to face segmentation and tracking in video sequences}, booktitle = {VIII Reuni{\'o}n de Trabajo en Procesamiento de la Informaci{\'o}n y Control}, year = {1999}, pages = {345{\textendash}350}, isbn = {0-8186-8821-1}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @article {aSalembier99, title = {Region-based representations of image and video : segmentation tools for multimedia services}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {9}, number = {8}, year = {1999}, pages = {1147{\textendash}1169}, issn = {1051-8215}, author = {Salembier, P. and Marqu{\'e}s, F.} } @conference {cSayrol99a, title = {Simulaci{\'o}n digital de se{\~n}ales y sistemas anal{\'o}gicos}, booktitle = {III Congreso de Usuarios de MATLAB}, year = {1999}, pages = {67{\textendash}76}, isbn = {84-699-1358-1}, author = {Sayrol E. and Gasull, A. and Moreno, A. and Vallverdu, F. and Salavedra, J. and Albert Oliveras} } @conference {cRuiz-Hidalgo99, title = {Towards stereo from scale-trees}, booktitle = {7th International Conference on Image Processing and its Applications}, year = {1999}, pages = {52{\textendash}56}, author = {Moravec, K. and Ruiz-Hidalgo, J. and Harvey, R. and Bangham, J.} } @inbook {bMarques99, title = {Video compresssion standards}, booktitle = {Electronic imaging technology}, year = {1999}, pages = {31{\textendash}64}, publisher = {SPIE Optical Engineering Press}, organization = {SPIE Optical Engineering Press}, edition = {Edward R. Dougherty (Ed.)}, isbn = {0819430374}, author = {Marqu{\'e}s, F. and Salembier, P.} } @conference {cMarcotegui99, title = {A video generation tool allowing friendly user interaction}, booktitle = {1999 IEEE INternational Conference on Image Processing}, year = {1999}, isbn = {0-7803-5470-2}, author = {Marcotegui, B. and Correia, P. and Marqu{\'e}s, F. and Mech, R. and Rosa, R. and Wollborn, M. and Zanoguera, F.} } @conference {cVilaplana98, title = {Face segmentation using connected operators}, booktitle = {Mathematical Morphology and its applications to image and signal processing}, year = {1998}, pages = {207{\textendash}214}, isbn = {0 7923 5133 9}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @article {aMarques98, title = {Prediction of image partitions using fourier descriptors : application to segmentation-based coding schemes}, journal = {IEEE transactions on image processing}, volume = {7}, number = {4}, year = {1998}, pages = {529{\textendash}542}, issn = {1057-7149}, author = {Marqu{\'e}s, F. and Llorens, B. and Gasull, A.} } @conference {cVilaplana98, title = {Region-based segmentation segmentation and tracking of human faces}, booktitle = {9th European Signal Processing Conference, EUSIPCO 1998}, year = {1998}, pages = {311{\textendash}314}, address = {Rhodes, Greece}, isbn = {960-7620-06-4}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F. and Salembier, P. and Garrido, L.} } @article {aMarcotegui97a, title = {Allowing content-based functionalities in segmentation-based coding schemes}, journal = {Annales des t{\'e}lecommunications. Annals of telecommunications}, volume = {52}, number = {7-8}, year = {1997}, pages = {398{\textendash}407}, issn = {0003-4347}, author = {Marcotegui, B. and Marqu{\'e}s, F. and Meyer, F.} } @article {aMarques97, title = {General requirements for coding oriented segmentation of video sequences}, journal = {Annales des t{\'e}lecommunications. Annals of telecommunications}, volume = {52}, number = {7-8}, year = {1997}, pages = {359{\textendash}366}, issn = {0003-4347}, author = {Marqu{\'e}s, F. and Meyer, F. and M. Pard{\`a}s and Salembier, P.} } @conference {cGarrido97b, title = {A hierarchical technique for image sequence analysis}, booktitle = {Workshop on Image Analysis for Multimedia Application Services, WIAMIS{\textquoteright}97}, year = {1997}, pages = {13{\textendash}20}, address = {Louvain-la-Neuve, Belgium}, author = {Garrido, L. and Marqu{\'e}s, F. and M. Pard{\`a}s and Salembier, P. and Ver{\'o}nica Vilaplana} } @article {pMarques97, title = {Image Segmentation and Object Tracking Method and Corresponding System}, number = {974025587}, year = {1997}, month = {10/1997}, type = {Invention patent}, address = {International}, issn = {974025587}, author = {Marqu{\'e}s, F. and Molina, C.} } @article {aMeyer97, title = {Morphological tools for segmentation: connected operators and watersheds}, journal = {Annales des t{\'e}lecommunications. Annals of telecommunications}, volume = {52}, number = {7-8}, year = {1997}, pages = {366{\textendash}379}, issn = {0003-4347}, author = {Meyer, F. and Albert Oliveras and Salembier, P. and Vachier, C.} } @conference {cMorros97, title = {Scalable segmentation-based coding of video sequences addressing content-based functionalities}, booktitle = {IEEE International Conference on Image Processing}, year = {1997}, month = {10/1997}, pages = {1-4}, publisher = {IEEE}, organization = {IEEE}, address = {Santa Barbara, USA}, abstract = {

In this paper, we\ addressvideo\ scalability in the framework\ of\ a region-basedcoding\ system, allowingcontent-basedfunctionalities. The proposed algorithm can construct scaled layers from a\ videosequence, each one with either fixed bit-rate or fixed quality, allowing\ contentbased\ manipulation. Two modes\ of\ operation have been defined: a supervised mode, that allows the user to select the objects to be\ coded\ in the enhancement layer, and an unsupervised mode, where this selection is done by the algorithm itself

}, doi = {10.1109/ICIP.1997.638658}, author = {Morros, J.R. and Marqu{\'e}s, F.} } @article {aMarcotegui97, title = {Segmentation of video sequences and rate control}, journal = {Annales des t{\'e}lecommunications. Annals of telecommunications}, volume = {52}, number = {7-8}, year = {1997}, pages = {380{\textendash}388}, issn = {0003-4347}, author = {Marcotegui, B. and Marqu{\'e}s, F. and Morros, J.R. and M. Pard{\`a}s and Salembier, P.} } @article {aSalembier97, title = {Segmentation-based video coding system allowing the manipulation of objects}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {7}, number = {1}, year = {1997}, pages = {60{\textendash}74}, issn = {1051-8215}, doi = {10.1109/76.554418}, author = {Salembier, P. and Marqu{\'e}s, F. and M. Pard{\`a}s and Morros, J.R. and Corset, I. and Jeannin, S. and Bouchard, L. and Meyer, F. and Marcotegui, B.} } @article {pPardas97, title = {Video coding method and corresponding coding and decoding systems}, number = {9693276.5-}, year = {1997}, type = {Invention patent}, author = {M. Pard{\`a}s and Salembier, P. and Ayuso, X. and Mart{\'\i}, E.} } @conference {cSalembier96d, title = {Active mesh coding and rate distortion theory}, booktitle = {IEEE International Conference on Image Processing, ICIP 1996}, year = {1996}, address = {Lausanne, Switzerland}, isbn = {0-7803-7043-0}, author = {Salembier, P. and Mart{\'\i}, E. and M. Pard{\`a}s} } @inbook {bSalembier96, title = {Coding of partition sequences}, booktitle = {Video coding: the second generation approach}, year = {1996}, pages = {125{\textendash}170}, publisher = {Kluwer}, organization = {Kluwer}, edition = {L. Torres and M. Kunt (Eds.)}, isbn = {0 7923 9680 4}, author = {Salembier, P. and Marqu{\'e}s, F. and Gasull, A.} } @inbook {bMarques96, title = {Coding-oriented segmentation of video sequences}, booktitle = {Video coding: the second generation approach}, year = {1996}, pages = {79{\textendash}124}, publisher = {Kluwer}, organization = {Kluwer}, edition = {L. Torres and M. Kunt (Eds.)}, isbn = {0 7923 9680 4}, author = {Marqu{\'e}s, F. and M. Pard{\`a}s and Salembier, P.} } @conference {cSalembier96e, title = {Morphological operators for very low bit rate video coding}, booktitle = {IEEE International Conference on Image Processing, ICIP 1996}, year = {1996}, pages = {659{\textendash}662}, address = {Lausanne, Switzerland}, isbn = {0-8194-5654-3}, author = {Salembier, P. and Meyer, F. and Brigger, P. and Bouchard, L.} } @conference {cMarques96a, title = {Partition coding using multigrid chain code and motion compensation}, booktitle = {IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING}, year = {1996}, author = {Marqu{\'e}s, F. and Gasull, A.} } @conference {cPardas96, title = {Partition tree for a segmentation-based video coding system}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1996}, year = {1996}, pages = {1982{\textendash}1985}, address = {Atlanta (GA), USA}, isbn = {-}, author = {M. Pard{\`a}s and Salembier, P. and Marqu{\'e}s, F. and Morros, J.R.} } @conference {cMarques96, title = {A segmentation-based coding system allowing manipulation of objects}, booktitle = {IEEE International Conference on Image Processing, ICIP 1996}, year = {1996}, pages = {145{\textendash}175}, address = {Lausanne, Switzerland}, abstract = {

We present a coding scheme that achieves, for each image in the sequence, the best segmentation in terms of rate-distortion theory. It is obtained from a set of initial regions and a set of available coding techniques. The segmentation combines spatial and motion criteria. It selects at each area of the image the most adequate criterion for defining a partition in order to obtain the best compromise between cost and quality. In addition, the proposed scheme is very suitable for addressing content-based functionalities.

}, isbn = {0-7803-3258-X}, doi = {10.1109/ICIP.1996.560741}, author = {Marqu{\'e}s, F. and Salembier, P. and M. Pard{\`a}s and Morros, J.R. and Corset, I. and Jeannin, S. and Marcotegui, B. and Meyer, F.} } @conference {cMarques96b, title = {A segmentation-based coding system allowing manipulation of objects (sesame)}, booktitle = {IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING}, year = {1996}, author = {Marqu{\'e}s, F.} } @conference {cSalembier96c, title = {Segmentation-based video coding: temporal links and rate control}, booktitle = {VIII European Signal Processing Conference, EUSIPCO{\textquoteright}96}, year = {1996}, pages = {455{\textendash}458}, address = {Trieste, Italy}, author = {Salembier, P. and Marqu{\'e}s, F. and M. Pard{\`a}s} } @article {pSalembier96a, title = {Segmented Picture Coding Method and System and Corresponding Decoding Method and System}, number = {964009161-}, year = {1996}, type = {Invention patent}, author = {Salembier, P. and Marqu{\'e}s, F. and Corset, I. and Bouchard, L. and Jeannin, S. and M. Pard{\`a}s and Morros, J.R. and Meyer, F. and Marcotegui, B.} } @inbook {bCasas96c, title = {Strong edge features for image coding}, booktitle = {Mathematical Morphology and its Applications to Image and Signal Processing}, volume = {5}, year = {1996}, pages = {443{\textendash}450}, publisher = {Springer}, organization = {Springer}, address = {Boston}, abstract = {

A two component model is proposed for perceptual image coding For the first component of the model the watershed operator is used to detect strong edge features Then an efficientmorphological interpolation algorithm reconstructs the smooth areas of the image from the extracted edge information also known as sketch data The residual component containing ne textures is separately coded by a subband coding scheme The morphological operators involved in the coding of the primary component perform very e ciently compared to conventional techniques like the LGO operator used for the edge extraction or the di usion lters iteratively applied for the interpolation of smooth areas in previously reported sketch based coding schemes.

}, isbn = {0-793-9733-9}, doi = {10.1007/978-1-4613-0469-2_52}, author = {Casas, J. and Torres, L.}, editor = {Schafer, R. W. and Maragos, P. and Butt, M. A.} } @conference {cMorros96, title = {Video sequence segmentation based on rate-distortion theory}, booktitle = {SPIE Visual Communication and Image Processing, VCIP{\textquoteright}96}, year = {1996}, month = {02/1996}, publisher = {Proc. SPIE 2727, 1185}, organization = {Proc. SPIE 2727, 1185}, address = {Orlando, Florida, USA}, abstract = {
This paper describes a coding-oriented segmentation technique for video schemes using an optimization strategy to address the problem of bit allocation. The optimization is based on the rate-distortion theory. Our purpose is to define a method to obtain an {\textquoteright}optimal{\textquoteright} partition together with the best coding technique for each region of this partition so that the result is optimal in a rate-distortion sense.

}, author = {Morros, J.R. and Marqu{\'e}s, F. and M. Pard{\`a}s and Salembier, P.} } @conference {cMarques95, title = {Coding of image partitions by morphological skeleton using overlapping structuring elements}, booktitle = {IEEE WORKSHOP ON NONLINEAR SIGNAL AND IMAGE PROCESSING}, year = {1995}, pages = {250{\textendash}253}, author = {Marqu{\'e}s, F. and Fioravanti, S. and Brigger, P.} } @conference {cMarques95a, title = {Interpolation and extrapolation of iomage partitions using fourier descriptions: to segmentation-based coding schemes}, booktitle = {IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING{\textquoteright}95.}, year = {1995}, pages = {584{\textendash}587}, author = {Marqu{\'e}s, F. and Llorens, B. and Gasull, A.} } @article {aSalembier95, title = {Region-based video coding using mathematical morphology}, journal = {Proceedings of the IEEE}, volume = {83}, number = {6}, year = {1995}, pages = {843{\textendash}857}, issn = {0018-9219}, author = {Salembier, P. and Torres, L. and Meyer, F. and Gu, C.} } @inbook {bMarques94a, title = {AUTOMATIC QUANTIFICATION OF SPINE PARAMETERS FROM X-RAY IMAGES BY MEANS OF MORPHOLOGICAL TOOLS.}, booktitle = {Mathematical morphology and its applications to image processing}, year = {1994}, pages = {330{\textendash}340}, isbn = {0-7923-3093-5}, author = {Marqu{\'e}s, F.} } @conference {cMarques94a, title = {Hierarchical image sequence model for segmentation: application to region-based sequence coding}, booktitle = {VISUAL COMMUNICATION AND IMAGE PROCESSING}, year = {1994}, pages = {554{\textendash}563}, author = {Marqu{\'e}s, F. and Gasull, A. and Vera, V.} } @conference {cMarques94c, title = {Interleaved segmentation and motion estimation by means of morphological tools}, booktitle = {Workshop on Image Analysis and Synthesis in Image Coding}, year = {1994}, address = {Berlin, Germany}, author = {Marqu{\'e}s, F. and Bouchard, L. and Corset, I. and Jeannin, S. and Morros, J.R. and M. Pard{\`a}s and Salembier, P. and Torres, L.} } @conference {cCasas94d, title = {Morphological scheme for morphometric analysis of epidermal biopsy images}, booktitle = {International Symposium on Mathematical Morphology and its Applications to Image Processing II}, year = {1994}, address = {Fontainebleau}, abstract = {

This paper addresses the problem of morphometric analysis of microscope images from cutaneous
biopsy samples. A morphological scheme is applied for the automatic measurement of
histologic parameters of the epidermis. It consists in an unsupervised segmentation approach that
is strongly based on an {\textquoteleft}a priori{\textquoteright} model of the images. The watershed algorithm has proven to be
a very powerful tool for the introduction of such {\textquoteleft}a priori{\textquoteright} information, because the segmentation
process can be conveniently guided by some strategic markers in order to perform the detection
of the desired structures. This permits an automatic measurement of some objective parameters
which are highly correlated with the evolution of some skin diseases.

}, isbn = {0-7923-3093-5}, author = {Casas, J. and Esteban, P. and Moreno, A. and Carrera, M.} } @inbook {bCasas94, title = {Morphological scheme for morphometric analysis of epidermal biopsy images}, booktitle = {Mathematical Morphology and its Applications to Image Processing}, volume = {2}, number = {Computational Imaging and Vision}, year = {1994}, pages = {325{\textendash}331}, publisher = {Springer}, organization = {Springer}, address = {Dordrecht}, abstract = {

This paper addresses the problem of morphometric analysis of microscope images from cutaneous biopsy samples. A morphological scheme is applied for the automatic measurement of histologic parameters of the epidermis. It consists in an unsupervised segmentation approach that is strongly based on an {\textquoteright}a priori{\textquoteright} model of the images. The watershed algorithm has proven to be a very powerful tool for the introduction of such {\textquoteright}a priori{\textquoteright} information, because the segmentation process can be conveniently guided by some strategic markers in order to perform the detection of the desired structures. This permits an automatic measurement of some objective parameters which are highly correlated with the evolution of some skin diseases.

}, doi = {10.1007/978-94-011-1040-2_42}, author = {Casas, J. and Esteban, P. and Moreno, A. and Carrera, M.}, editor = {Serra, J. and Soille, P.} } @conference {cMarques94b, title = {Recursive image sequence segmentation by hierarchical models}, booktitle = {12th IAPR International Conference on Pattern Recognition}, year = {1994}, pages = {523{\textendash}525}, isbn = {0-8186-6265-4}, author = {Marqu{\'e}s, F. and Vera, V. and Gasull, A.} } @conference {cMarques94, title = {Top-down 3d image sequence segmentation technique controlled by morphological}, booktitle = {EUSIPCO-94.}, year = {1994}, pages = {415{\textendash}418}, author = {Marqu{\'e}s, F. and Vera, V. and Gasull, A.} } @inbook {bMarques94, title = {UNSUPERVISED HIERARCHICAL IMAGE SEGMENTATION USING COMPOUND RANDOM FIELDS}, booktitle = {A Robust Method for Computing 2D Projective invariants of 3D Object Vertices}, year = {1994}, pages = {125{\textendash}136}, isbn = {981-02-1872-9}, author = {Marqu{\'e}s, F.} } @conference {cMontolio93, title = {Character recognition and document analysis by morphological techniques}, booktitle = {WORKSHOP ON MATHEMATICAL MORPHOLOGY AND ITS APPLICATIONS TO SIGNAL PROCESSING}, year = {1993}, pages = {198{\textendash}203}, author = {Montolio, P. and Gasull, A. and Corbera, L. and Marqu{\'e}s, F.} } @conference {cGasull93, title = {Character recognition and document analysis by morphological techniq}, booktitle = {MATHEMATICAL MORPHOLOGY}, year = {1993}, pages = {198{\textendash}203}, author = {Gasull, A. and Corbera, L. and Marqu{\'e}s, F.} } @conference {cMarques93d, title = {Cuantificacion de lesiones de columna vertebral a partir de imagenes}, booktitle = {VIII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1993}, pages = {433{\textendash}437}, author = {Marqu{\'e}s, F.} } @conference {cMarques93e, title = {Cuantificador vectorial con clasificador difuso para la codificacion}, booktitle = {VIII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1993}, pages = {369{\textendash}373}, author = {Marqu{\'e}s, F.} } @conference {cMarques93c, title = {Morphological scheme for myalinated nerve fiber morphometric analysis}, booktitle = {International Symposium on Mathematical Morphology and its applications to image and signal processing, ISMM 1993}, year = {1993}, pages = {122{\textendash}126}, address = {Barcelona, Spain}, url = {Problemes de connexi{\'o} a l{\textquoteright}Eprints}, author = {Marqu{\'e}s, F. and Salembier, P. and Navarro, X. and Sa{\~n}udo, J.} } @conference {cSalembier93f, title = {Morphological segmentation-based coding of image sequences}, booktitle = {IEEE European Conference on Circuits Theory and Design}, year = {1993}, pages = {1245{\textendash}1250}, address = {Davos, Switzerland}, isbn = {0-7803-5470-2}, url = {Problemes de connexi{\'o} a l{\textquoteright}Eprints}, author = {Salembier, P. and Torres, L. and M. Pard{\`a}s and Marqu{\'e}s, F. and HIERRO, P. and Gasull, A.} } @conference {cSalembier93e, title = {Object-based image coding with morphological segmentation and efficient contour coding}, booktitle = {IEEE International Conference on Signal Processing, ICSP{\textquoteright}93}, year = {1993}, address = {Beijing, China}, author = {Salembier, P. and Marqu{\'e}s, F.} } @conference {cMarques93, title = {Shape and location coding for contour images}, booktitle = {PROC. OF THE 1993 PICTURE CODING SYMPOSIUM}, year = {1993}, pages = {61{\textendash}63}, author = {Marqu{\'e}s, F. and Gasull, A.} } @conference {cMarques93b, title = {Stochastic image model for segmentation. application to image coding}, booktitle = {SCANDINAVIAN CONFERENCE ON IMAGE ANALYSIS}, year = {1993}, pages = {265{\textendash}272}, author = {Marqu{\'e}s, F. and Gasull, A.} } @conference {cMarques93a, title = {Unsupervised segmentation controlled by morphological contrast ext}, booktitle = {ICASSP}, year = {1993}, pages = {517{\textendash}520}, author = {Marqu{\'e}s, F. and Gasull, A.} } @inbook {bGasull92, title = {Analysis and optimization of the K-Means algorithm for remote sensing applications}, booktitle = {Pattern recognition and image analysis}, year = {1992}, pages = {0{\textendash}0}, isbn = {9810208812}, url = {http://cataleg.upc.edu/search~S1*cat/?searchtype=i\&searcharg=+9810208812\&searchscope=1\&SORT=D\&extended=0\&SUBMIT=Cerca\&searchlimits=\&searchorigarg=t+Proceedings+of++Workshop+on+Network+Robot+Systems+2009}, author = {Gasull, A. and Monte, E. and Torres, L. and Montolio, P. and Marqu{\'e}s, F.} } @conference {cMarques92, title = {Classified vector quantization with fuzzy theory}, booktitle = {INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS}, year = {1992}, pages = {237{\textendash}244}, isbn = {0-7803-3192-3}, author = {Marqu{\'e}s, F.} } @conference {cGasull92c, title = {Coagulation time detection by means of a real-time image processing}, booktitle = {14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {1992}, pages = {1948{\textendash}1949}, author = {Gasull, A. and Vallverdu, F. and Marqu{\'e}s, F.} } @conference {cMarques92b, title = {Codificacion de imagenes: un metodo de segunda generacion}, booktitle = {VII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1992}, pages = {144{\textendash}148}, author = {Marqu{\'e}s, F. and Gasull, A.} } @conference {cMarques92d, title = {Codificacion de imagenes:un metodo de segunda generacion}, booktitle = {VII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1992}, pages = {144{\textendash}148}, author = {Marqu{\'e}s, F. and Gasull, A.} } @article {aTorres92, title = {Compresi{\'o}n de audio e imagen para sistemas multimedia}, journal = {Bit numerical mathematics}, number = {76}, year = {1992}, pages = {50{\textendash}58}, issn = {0006-3835}, author = {Torres, L. and Moreno, A. and Masgrau, E. and Gasull, A.} } @conference {cBonafonte92a, title = {Efficient integration of coarticulation and lexical information ...}, booktitle = {INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING}, year = {1992}, pages = {45{\textendash}48}, author = {Bonafonte, A. and Mari{\~n}o, J. and M. Pard{\`a}s} } @conference {cSalembier92b, title = {Morphological detection based on size and contrast criteria}, booktitle = {14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {1992}, pages = {1930{\textendash}1031}, address = {Paris, France}, isbn = {?}, author = {Salembier, P. and Gasull, A. and Marqu{\'e}s, F. and Elisa Sayrol} } @phdthesis {dMarques92, title = {Multiresolution image segmentation based on camporend random fields: Application to image coding}, year = {1992}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, url = {http://hdl.handle.net/10803/6910}, author = {Marqu{\'e}s, F.}, editor = {Gasull, A.} } @conference {cBonafonte92, title = {N-best hypotheses:an approach taking into account coarticulation}, booktitle = {EUROPEAN SIGNAL PROCESSING CONFERENCE}, year = {1992}, pages = {379{\textendash}382}, author = {Bonafonte, A. and M. Pard{\`a}s and Mari{\~n}o, J.} } @conference {cGasull92, title = {Non-linear techniques for image interpolation}, booktitle = {VI European Signal Processing Conference}, year = {1992}, pages = {1473{\textendash}1476}, isbn = {0 444 89587 6}, author = {Gasull, A. and Marqu{\'e}s, F. and Torres, L.} } @conference {cMarques92c, title = {Segmentacion de imagenes multiespectrales con tecnicas piramidales}, booktitle = {VII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1992}, pages = {372{\textendash}376}, author = {Marqu{\'e}s, F. and Gasull, A.} } @conference {cMarques92a, title = {Segmentacion no supervisada de imagenes mediante campos aleatorios}, booktitle = {SIMPOSIUM NACIONAL DE RECONOZIMIENTO DE FORMAS Y ANALISIS IMAGENES}, year = {1992}, pages = {55{\textendash}62}, isbn = {3-8007-2300-X}, author = {Marqu{\'e}s, F. and Gasull, A.} } @conference {cGasull92b, title = {T{\'e}cnicas de preprocesado para la segmentaci{\'o}n de im{\'a}genes}, booktitle = {VII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1992}, pages = {367{\textendash}371}, author = {Gasull, A. and Marqu{\'e}s, F. and Torres, L.} } @conference {cGasull92a, title = {T{\'e}cnicas de preprocesado para la segmentaci{\'o}n de im{\'a}genes}, booktitle = {U.R.S.I. 92}, year = {1992}, pages = {367{\textendash}371}, isbn = {84-600-8219-9}, author = {Gasull, A. and Marqu{\'e}s, F. and Montolio, P. and Torres, L.} } @conference {cGasull91, title = {Analisis de no estacionariedades en la interpolacion de imagenes}, booktitle = {VI Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1991}, pages = {985{\textendash}989}, isbn = {84-600-7766-7}, author = {Gasull, A. and Marqu{\'e}s, F. and Torres, L.} } @conference {cMarques91, title = {Codig-oriented segmentation based on g-m random}, booktitle = {International Conference on Acoustics, Speech and Signal Processing 1991}, year = {1991}, pages = {2749{\textendash}2752}, author = {Marqu{\'e}s, F. and Gasull, A.} } @conference {cMarques91b, title = {Obtencion de un esqueleto morfologico sin puntos redundantes}, booktitle = {VI Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1991}, pages = {980{\textendash}984}, author = {Marqu{\'e}s, F. and Gasull, A. and Torres, L.} } @conference {cMarques91a, title = {Segmentacion de imagenes mediante modelos de gibbs-markov}, booktitle = {VI Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1991}, pages = {975{\textendash}979}, author = {Marqu{\'e}s, F. and Gasull, A.} } @conference {cLleida91, title = {Two level c.s.r. using demisyllable..}, booktitle = {EUROPEAN CONF. ON SPEECH COMMUN. AND TECHN. EUROSPEECH{\textquoteright}91}, year = {1991}, pages = {1199{\textendash}1202}, isbn = {10184074}, author = {Lleida, E. and Albert Oliveras and Nadeu, C. and Mari{\~n}o, J.} } @article {aSanz90, title = {Aspectos Cl{\'\i}nicos-toxicol{\'o}gicos en los trabajadores de una industria productora de cromatos}, journal = {Annual review of pharmacology and toxicology}, volume = {7}, year = {1990}, pages = {1:13{\textendash}1:20}, issn = {0362-1642}, author = {Sanz, P. and Ribas, B. and Cobo, E. and Gadea, E. and Marqu{\'e}s, F. and Sol{\'e}, E. and Corbella, J.} } @inbook {bMarques90, title = {NEW GENERATION METHODS FOR HTE HIGH-COMPRESSION CODING OF DIGITAL SEQUENCES}, booktitle = {From pixels to features ii}, year = {1990}, pages = {363{\textendash}375}, isbn = {0444890033}, author = {Marqu{\'e}s, F.} } @conference {cNadeu90, title = {Simbad:a tool for speech analysis and synthesis}, booktitle = {IASTED INT.CONF.SIGNAL PROC.\&DIG.FILT.}, year = {1990}, isbn = {1522-4880}, author = {Nadeu, C. and Albert Oliveras and Mari{\~n}o, J.} } @conference {cGasull89a, title = {Automated left ventricular contour extraction and volume calculation form echocardiographic images}, booktitle = {Ultrasonics International 89}, year = {1989}, pages = {1{\textendash}4}, isbn = {SN.}, author = {Gasull, A. and Alquezar, R. and Torres, L. and Sallent, S. and Marqu{\'e}s, F. and Vidal, J. and Sangr{\'a}, E.} } @conference {cGasull88, title = {Detecci{\'o}n autom{\'a}tica de contornos en im{\'a}genes ecogr{\'a}ficas}, booktitle = {III Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1988}, pages = {1{\textendash}4}, isbn = {?}, author = {Gasull, A. and Marqu{\'e}s, F. and Sallent, S. and Torres, L. and Vidal, J.} }