@article {aPinaa, title = {Layer-Wise Training of Graph Neural Networks With Self-Supervised Learning}, journal = { IEEE Transactions on Neural Networks and Learning Systems,}, year = {In Press}, doi = {10.1109/TNNLS.2025.3577702}, author = {Oscar Pina and Ver{\'o}nica Vilaplana} } @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} } @conference {cLems25, title = {Towards a multicentric open DigitAL PatHology assIstant beNchmark: Initial Results from the DALPHIN Study}, booktitle = {United States \& Canadian Academy of Pathology 114th Annual Meeting (USCAP 2025)}, year = {2025}, month = {03/2025}, publisher = {Laboratory Investigation, Volume 105, Issue 3, Supplement, 2025, 103609, ISSN 0023-6837}, organization = {Laboratory Investigation, Volume 105, Issue 3, Supplement, 2025, 103609, ISSN 0023-6837}, address = {Boston, USA}, doi = {https://doi.org/10.1016/j.labinv.2024.103609.}, url = {https://www.sciencedirect.com/science/article/pii/S0023683724032872}, author = {Carlijn M Lems and Nat{\'a}lie Klub{\'\i}{\v c}kov{\'a} and Biagio Brattoli, and Taebum Lee and Ver{\'o}nica Vilaplana and Pedro Luis Fern{\'a}ndez and Laura Pons and Milda Poceviciute and Nadieh Khalili and Francesco Ciompi} } @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 {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} } @conference {cPinad, title = {Cell-DETR: Efficient cell detection and classification in WSIs with transformers}, booktitle = {Medical Imaging with Deep Learning (MIDL 2024)}, year = {2024}, month = {07/2024}, abstract = {

Understanding cell interactions and subpopulation distribution is crucial for pathologists to support their diagnoses. This cell information is traditionally extracted from segmentation methods, which poses significant computational challenges on processing Whole Slide Images (WSIs) due to their giga-size nature. Nonetheless, the clinically relevant tasks are nuclei detection and classification rather than segmentation. In this manuscript, we undertake a comprehensive exploration of the applicability of detection transformers for cell detection and classification (Cell-DETR). Not only do we demonstrate the effectiveness of the method by achieving state-of-the-art performance on well-established benchmarks, but we also develop a pipeline to tackle these tasks on WSIs at scale to enable the development of downstream applications. We show its efficiency and feasibility by reporting a significantly faster inference time on a dataset featuring large WSIs. By addressing the challenges associated with large-scale cell detection, our work contributes valuable insights that pave the way for improved diagnostics and downstream applications leveraging cell-level information.

}, author = {Oscar Pina and Eduard Dorca and Ver{\'o}nica Vilaplana} } @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 {cBlanco24, title = {Estimation of 3D Shape and Volume of Fire Plumes from Multiple Views}, booktitle = {4th European Symposium on Fire Safety Science}, volume = {2885}, year = {2024}, month = {10/2024}, publisher = {IOP J. Phys.: Conf. Ser.}, organization = {IOP J. Phys.: Conf. Ser.}, type = { 4th European Symposium on Fire Safety Science 09/10/2024 - 11/10/2024 Barcelona, Spain}, chapter = {012075}, address = {Barcelona}, abstract = {

This study evaluates deep-learning and Shape from Silhouette (SfS) methods for 3D reconstruction of smoke plumes. It demonstrates the deep-learning method{\textquoteright}s superiority in cases with limited camera views and calibration data, achieving high-quality reconstructions of semi-transparent smoke without precise calibration. The research emphasizes the significance of pre-processing and data appearance for neural network efficacy. By improving 3D reconstruction techniques, this work aids in advancing wildfire tracking and environmental analysis, offering a practical approach for real-world applications in fire science.

}, url = {https://esfss2024.com/}, author = {J{\'u}lia Ariadna Blanco and M. Pard{\`a}s and Casas, J. and Ronan Paugam and Agueda, Alba and Wagner, Joel and Russell Parsons and Planas, Eul{\`a}lia} } @article {a24, title = {Estimation of 3D Shape and Volume of Fire Plumes from Multiple Views}, journal = {IOP J. Phys.: Conf. Ser.}, volume = {2885}, year = {2024}, month = {09/2024}, chapter = {012075}, abstract = {

This study evaluates deep-learning and Shape from Silhouette (SfS) methods for 3D reconstruction of smoke plumes. It demonstrates the deep-learning method{\textquoteright}s superiority in cases with limited camera views and calibration data, achieving high-quality reconstructions of semitransparent smoke without precise calibration. The research emphasizes the significance of preprocessing and data appearance for neural network efficacy. By improving 3D reconstruction techniques, this work aids in advancing wildfire tracking and environmental analysis, offering a practical approach for real-world applications in fire science.

}, doi = {10.1088/1742-6596/2885/1/012075}, url = {https://iopscience.iop.org/article/10.1088/1742-6596/2885/1/012075}, author = {J{\'u}lia Ariadna Blanco and M. Pard{\`a}s and Casas, J. and Ronan Paugam and Agueda, Alba and Wagner, Joel and Russell Parsons and Planas, Eul{\`a}lia} } @article {aDios24, title = {Experimental evaluation of a MIMO radar performance for ADAS application}, journal = {Telecom}, volume = {5}, year = {2024}, month = {06/2024}, pages = {508-521}, chapter = {508}, abstract = {

Among the sensors necessary to equip vehicles with an autonomous driving system, there is a tacit agreement that cameras and some type of radar would be essential. The ability of radar to spatially locate objects (pedestrians, other vehicles, trees, street furniture, and traffic signs) makes it the most economical complement to the cameras in the visible spectrum in order to give the correct depth to scenes. From the echoes obtained by the radar, some data fusion algorithms will try to locate each object in its correct place within the space surrounding the vehicle. In any case, the usefulness of the radar will be determined by several performance parameters, such as its average error in distance, the maximum errors, and the number of echoes per second it can provide. In this work, we have tested experimentally the AWR1843 MIMO radar from Texas Instruments to measure those parameters.

}, keywords = {advanced driver assistance systems, autonomous vehicles, MIMO radar, radar}, doi = {10.3390/telecom5030026}, author = {Federico Dios and Sergio Torres-Benito and Jos{\'e} Antonio L{\'a}zaro and Casas, J. and Jorge Pinazo and Adolfo Ler{\'\i}n} } @article {aPina, title = {Feature propagation as self-supervision signals on graphs}, journal = {Knowledge-Based Systems}, volume = {289}, year = {2024}, month = {04/2024}, doi = {https://doi.org/10.1016/j.knosys.2024.111512}, author = {Oscar Pina and Ver{\'o}nica Vilaplana} } @inbook {bHernandez, title = {Interpreting Machine Learning Models for Survival Analysis: A Study of Cutaneous Melanoma Using the SEER Database}, booktitle = {Explainable Artificial Intelligence and Process Mining Applications for Healthcare,}, volume = {2020}, number = {Communications in Computer and Information Science}, year = {2024}, publisher = {Springer, Cham}, organization = {Springer, Cham}, abstract = {

In this study, we train and compare three types of machine learning algorithms for Survival Analysis: Random Survival Forest, DeepSurv and DeepHit, using the SEER database to model cutaneous malignant melanoma. Additionally, we employ SurvLIMEpy library, a Python package designed to provide explainability for survival machine learning models, to analyse feature importance. The results demonstrate that machine learning algorithms outperform the Cox Proportional Hazards Model. Our work underscores the importance of explainability methods for interpreting black-box models and provides insights into important features related to melanoma prognosis.

}, isbn = {978-3-031-54302-9}, issn = {978-3-031-54303-6}, doi = {https://doi.org/10.1007/978-3-031-54303-6_6}, author = {Carlos Hernandez and Cristian Pach{\'o}n-Garc{\'\i}a and Pedro Delicado and Ver{\'o}nica Vilaplana} } @article {aPachon-Garcia, title = {SurvLIMEpy: A Python package implementing SurvLIME}, journal = {Expert Systems With Applications}, volume = {237, Part C}, year = {2024}, month = {03/2024}, abstract = {

In this paper we present SurvLIMEpy, an open-source Python package that implements the SurvLIME algorithm. This method allows to compute local feature importance for machine learning algorithms designed for modelling Survival Analysis data. Our\ implementation takes advantage of the parallelisation paradigm as all computations are performed in a matrix-wise fashion which speeds up execution time. Additionally, SurvLIMEpy assists the user with visualization tools to better understand the result of the algorithm. The package supports a wide variety of survival models, from the Cox Proportional Hazards Model to deep learning models such as DeepHit or DeepSurv. Two types of experiments are presented in this paper. First, by means of simulated data, we study the ability of the algorithm to capture the importance of the features. Second, we use three open source survival datasets together with a set of survival algorithms in order to demonstrate how SurvLIMEpy behaves when applied to different models.

}, keywords = {eXplainable Artificial Intelligence, Interpretable Machine Learning, machine learning, python, Survival Analysis}, author = {Cristian Pach{\'o}n-Garc{\'\i}a and Carlos Hernandez and Pedro Delicado and Ver{\'o}nica Vilaplana} } @conference {cGiardina, title = {Synthesis of Prostate MRI Scans: A Comparison of StyleGAN2-ADA and Latent Diffusion Models}, booktitle = {Conferencia de la Asociaci{\'o}n Espa{\~n}ola para la Inteligencia Artificial, IABioMed workshop}, year = {2024}, month = {07/2024}, abstract = {

Prostate cancer is a leading cause of cancer-related death in men globally. Deep learning algorithms hold significant promise for disease classification and detection, but their performance heavily relies on the availability of large training datasets. Generative Adversarial Networks (GANs) and Diffusion Models offer cutting-edge solutions to address this challenge by synthesizing realistic medical data.

This work investigates the potential of StyleGAN2-ADA and Latent Diffusion Models (LDMs) for generating synthetic T2-weighted prostate MRI slices. We leverage a publicly available dataset to train these models and evaluate their ability to create realistic MRI data. Our initial findings demonstrate that both frameworks can produce slices visually similar to real data. However, a crucial distinction lies in their training and inference efficiency.\ 

}, author = {Claudia Giardina and Ver{\'o}nica Vilaplana and M. Pard{\`a}s and Oriol Guardia} } @conference {cPinac, title = {Unsupervised Domain Adaptation for Multi-Stain Cell Detection in Breast Cancer with Transformers}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (DEF-AI-MIA workshop)}, year = {2024}, month = {07/2024}, abstract = {

The complexity in digital pathology image analysis arises from the inherent variability in histopathological slides including tissue specimen differences and stain variations. While publicly available datasets are increasing they primarily focus on hematoxylin and eosin (H\&E) staining yet pathologists often require analysis across multiple stains for comprehensive diagnosis. The poor cross-domain generalization of deep learning models intensifies these challenges making it unfeasible to implement without exhaustive annotations in each stain. This manual curation process is time-consuming and requires the creation of sufficiently large and precise datasets. In this work we address these challenges focusing on breast cancer across four crucial stains: ER PR HER2 and Ki-67. Given the necessity of cell-level information for diagnosis we concentrate on cell detection tasks. Leveraging unsupervised domain adaptation techniques we bridge the gap between publicly available annotated H\&E datasets and unlabeled data in other stains. Utilizing detection transformers we demonstrate the superiority of adversarial feature learning over source-only and image-level generative methods. Our work contributes to improving digital pathology analysis by enabling robust and efficient pipelines across multiple stains. By leveraging unsupervised domain adaptation we facilitate the development of accurate computer-aided diagnosis pipelines capable of handling stain variations and enhancing diagnostic accuracy in practical settings.

}, url = {https://openaccess.thecvf.com/content/CVPR2024W/DEF-AI-MIA/papers/Pina_Unsupervised_Domain_Adaptation_for_Multi-Stain_Cell_Detection_in_Breast_Cancer_CVPRW_2024_paper.pdf}, author = {Oscar Pina and Ver{\'o}nica Vilaplana} } @conference {cBaldrich23, title = {Deep learning-based methodology for smoke plume segmentation of wildfire images}, booktitle = {14th International Symposium on Fire Safety Science (IAFSS2023)}, year = {2023}, month = {10/2023}, publisher = {International Association for Fire Safety Science (IAFSS)}, organization = {International Association for Fire Safety Science (IAFSS)}, address = {Tsukuba, Japan}, abstract = {

Physics based models used to predict wildfire behavior and smoke plume rise need to be validated against empirical datasets. The acquisition of data pertaining to the evolution of smoke plume envelopes from wildfires presents significant challenges due to the highly variable 3D dynamics of the plume and associated optical properties involved. Besides the use of active remote sensing sensor like lidar [1], capturing still images or videos of smoke plume from multiple perspectives can be coupled with novel computer vision techniques to obtain comprehensive measurements of the plume as a three-dimensional entity. The first step to approach the 3D smoke plume reconstruction is being able to accurately segment the smoke from each image frame. In this study, we explore the effectiveness of deep learning-based methodologies, with particular emphasis on the UNet architecture [2], for tasks related to smoke image segmentation. We aim to leverage the UNet architecture to enhance segmentation performance and provide reliable analysis of smoke plumes in real-world scenarios.
The UNet model is a convolutional neural network based on the structure of autoencoders with skip connections, which develops self-learning tasks to finally obtain a prediction of the class trained, in this case smoke plumes. Different backbones in the UNet architecture were introduced to modify the encoder structure in order to obtain better performance. Tests of different architectures and hyperparameters configurations are done. This segmentation approach demonstrates promising results in accurately segmenting smoke plumes from wildfire images (see Figure 1). Results show that with the ResNet50 [3] and MobileNetV2 [4] backbones, the output obtained is similar in terms of performance, but MobileNetV2 has less computational cost. A validation accuracy of 93.45 \% is achieved. The successful application of the UNet architecture enables the detection and segmentation of smoke plumes with high precision.

}, url = {https://iafss2023.com}, author = {Alba Baldrich and Ronan Paugam and Casas, J. and M. Pard{\`a}s and Alba {\`A}gueda and Russell Parsons and Planas, Eul{\`a}lia} } @conference {cHernandez23, title = {Interpreting Machine Learning models for Survival Analysis: A study of Cutaneous Melanoma using the SEER Database}, booktitle = {XAI-Healthcare 2023 Workshop at 21st International Conference of Artificial Intelligence in Medicine (AIME 2023)}, year = {2023}, month = {06/2023}, address = {Portoroz, Slovenia}, author = {Carlos Hernandez and Cristian Pach{\'o}n-Garc{\'\i}a and Pedro Delicado and Ver{\'o}nica Vilaplana} } @conference {cPinab, title = {Layer-wise self-supervised learning on graphs}, booktitle = {KDD 2023 Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD 2023)}, year = {2023}, month = {08/2023}, address = {Long Beach, USA}, abstract = {

End-to-end training of graph neural networks (GNN) on large graphs presents several memory and computational challenges, and limits the application to shallow architectures as depth exponentially increases the memory and space complexities. In this manuscript, we propose Layer-wise Regularized Graph Infomax, an algorithm to train GNNs layer by layer in a self-supervised manner. We decouple the feature propagation and feature transformation carried out by GNNs to learn node representations in order to derive a loss function based on the prediction of future inputs. We evaluate the algorithm in inductive large graphs and show similar performance to other end to end methods and a substantially increased efficiency, which enables the training of more sophisticated models in one single device. We also show that our algorithm avoids the oversmoothing of the representations, another common challenge of deep GNNs.

}, author = {Oscar Pina and Ver{\'o}nica Vilaplana} } @conference {cLozano23, title = {Optical Phased Array Antenna Apodization for Lidar in Autonomous Vehicles}, booktitle = {XIII Reuni{\'o}n OptoElectr{\'o}nica}, year = {2023}, month = {06/2023}, publisher = {OPTOEL}, organization = {OPTOEL}, address = {Sevilla, Spain}, abstract = {

This paper presents the specific design of an Optical Phased Array antenna (OPA) to apodize the emission of a lidar in the context of a project where diverse optoelectronic sensors such as cameras, radars, and commercial lidars are used to provide data in order to fuse them and develop perception for robots as future autonomous vehicles. While mechanical based lidars are already commercially available, this work focuses on designing much more robust and potentially cheaper lidars based on photonic integrated circuits and energy optimization through the apodization of the emission of the OPA.

}, url = {https://www.optoel2023.es/94299/section/44205/optoel-2023.html}, author = {Jos{\'e} Lozano and Humberto Jim{\'e}nez and Sergio Torres and Pau Biosca and Bernat Fontanet and Jorge Pinazo and Adolfo Ler{\'\i}n and Federico Dios and Casas, J. and Jos{\'e} Antonio L{\'a}zaro} } @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 {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.} } @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} } @article {canet2022context, title = {Context-unsupervised adversarial network for video sensors}, journal = {Sensors}, volume = {22}, year = {2022}, pages = {3171}, abstract = {

Foreground object segmentation is a crucial first step for surveillance systems based on networks of video sensors. This problem in the context of dynamic scenes has been widely explored in the last two decades, but it still has open research questions due to challenges such as strong shadows, background clutter and illumination changes. After years of solid work based on statistical background pixel modeling, most current proposals use convolutional neural networks (CNNs) either to model the background or to make the foreground/background decision. Although these new techniques achieve outstanding results, they usually require specific training for each scene, which is unfeasible if we aim at designing software for embedded video systems and smart cameras. Our approach to the problem does not require specific context or scene training, and thus no manual labeling. We propose a network for a refinement step on top of conventional state-of-the-art background subtraction systems. By using a statistical technique to produce a rough mask, we do not need to train the network for each scene. The proposed method can take advantage of the specificity of the classic techniques, while obtaining the highly accurate segmentation that a deep learning system provides. We also show the advantage of using an adversarial network to improve the generalization ability of the network and produce more consistent results than an equivalent non-adversarial network. The results provided were obtained by training the network on a common database, without fine-tuning for specific scenes. Experiments on the unseen part of the CDNet database provided 0.82 a F-score, and 0.87 was achieved for LASIESTA databases, which is a database unrelated to the training one. On this last database, the results outperformed by 8.75\% those available in the official table. The results achieved for CDNet are well above those of the methods not based on CNNs, and according to the literature, among the best for the context-unsupervised CNNs systems.

}, author = {Canet Tarr{\'e}s, Gemma and M. Pard{\`a}s} } @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} } @article {aPedersen22, title = {Experimental confirmation of efficient island divertor operation and successful neoclassical transport optimization in Wendelstein 7-X}, journal = {Nuclear Fusion}, volume = {62}, year = {2022}, month = {04/2022}, issn = {1741-4326}, doi = {10.1088/1741-4326/ac2cf5}, author = {Thomas Sunn Pedersen and al et and Casas, J. and Salembier, P.} } @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} } @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 {cPinaa, title = {Self-supervised graph representations of WSIs}, booktitle = {Geometric Deep Learning in Medical Image Analysis}, year = {2022}, month = {2022}, abstract = {

In this manuscript we propose a framework for the analysis of whole slide images (WSI) on the cell entity space with self-supervised deep learning on graphs and explore its representation quality at different levels of application. It consists of a two step process in which the cell level analysis is performed locally, by clusters of nearby cells that can be seen as small regions of the image, in order to learn representations that capture the cell environment and distribution. In a second stage, a WSI graph is generated with these regions as nodes and the representations learned as initial node embeddings. The graph is leveraged for a downstream task, region of interest (ROI) detection addressed as a graph clustering. The representations outperform the evaluation baselines at both levels of application, which has been carried out predicting whether a cell, or region, is tumor or not based on its learned representations with a logistic regressor.

}, url = {https://proceedings.mlr.press/v194/pina22a/pina22a.pdf}, author = {Oscar Pina 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 {cPina, title = {Structural Networks for Brain Age Prediction}, booktitle = {Medical Imaging with Deep Learning (MIDL 2022)}, year = {2022}, month = {08/2022}, abstract = {

Biological networks have gained considerable attention within the Deep Learning community because of the promising framework of Graph Neural Networks (GNN), neural models that operate in complex networks. In the context of neuroimaging, GNNs have successfully been employed for functional MRI processing but their application to ROI-level structural MRI (sMRI) remains mostly unexplored. In this work we analyze the implementation of these geometric models with sMRI by building graphs of ROIs (ROI graphs) using tools from Graph Signal Processing literature and evaluate their performance in a downstream supervised task, age prediction. We first make a qualitative and quantitative comparison of the resulting networks obtained with common graph topology learning strategies. In a second stage, we train GNN-based models for brain age prediction. Since the order of every ROI graph is exactly the same and each vertex is an entity by itself (a ROI), we evaluate whether including ROI information during message-passing or global pooling operations is beneficial and compare the performance of GNNs against a Fully-Connected Neural Network baseline. The results show that ROI-level information is needed during the global pooling operation in order to achieve competitive results. However, no relevant improvement has been detected when it is incorporated during the message passing. These models achieve a MAE of 4.27 in hold-out test data, which is a performance very similar to the baseline, suggesting that the inductive bias included with the obtained graph connectivity is relevant and useful to reduce the dimensionality of the problem

}, author = {Oscar Pina and Irene Cumplido-Mayoral and Raffaele Cacciaglia and Jos{\'e} Mar{\'\i}a Gonz{\'a}lez-de-Ech{\'a}varri and Juan D. Gispert and Ver{\'o}nica Vilaplana} } @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 {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 {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} } @article {aPuig-Sitjes21, title = {Real-time detection of overloads on the plasma-facing components of Wendelstein 7-X}, journal = {Applied sciences (Basel)}, volume = {11}, year = {2021}, month = {12/2021}, chapter = {1}, issn = {2076-3417}, doi = {10.3390/app112411969}, url = {http://hdl.handle.net/2117/361558}, author = {Puig-Sitjes, A. and Jakubowski, M. and Naujoks, D. and Gao, Y. and Drewelow, P. and Niemann, H. and Felinger, J. and Casas, J. and Salembier, P. and Clemente, R.} } @conference {cPuig-Sitjes21, title = {Spatio-temporal Detection and Tracking of Thermal Events on the Plasma Facing Components of Wendelstein 7-X}, booktitle = {4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis}, year = {2021}, month = {11/2021}, publisher = {iaea.org}, organization = {iaea.org}, address = {Shanghai (online)}, abstract = {

In steady-state fusion devices like Wendelstein 7-X (W7-X), the active control of heat loads is mandatory to attain long-plasma operation. An intelligent feedback control system that mitigates the risk of overheating is required to avoid a premature plasma termination by the safety system. To keep the plasma within the safe operational limits of the plasma facing components, the feedback control system must be informed of the ongoing thermal events and their evolution in time. Then it can take effectives countermeasures to prevent the thermal events from reaching a critical point. These countermeasures may include reducing the heating power, changing the strike-line position or inducing detachment. With reaction times of the order of a hundred milliseconds, a fully automated real-time image analysis algorithm is required.
In this work, we present a spatio-temporal algorithm to detect, classify and track the thermal events observed by the thermography diagnostic on the plasma facing components of W7-X. The system detects and distinguishes between strike-lines and isolated hot spots as well as leading edges. The segmentation of the strike-line is specially challenging at W7-X. As a 3-dimensional helically-shaped stellarator equipped with 10 island divertors, the strike-lines have a complex heat load distribution with a high-dynamic range. The use of morphological tools and, in particular, the use of the Max-tree transform allow us to segment the thermal events in a hierarchical way preserving the inclusion relationship between different events, like hot spots and leading edges embedded in the strike-line structure. The thermal events are segmented for each frame and tracked over time in order to forecast their temporal evolution and to evaluate their risk. To this end, a spatio-temporal graph is built and spatio-temporal connected components are used to track the thermal events across the sequence frames. The spatio-temporal components in the graph are used to label the events in the sequence preserving temporal coherence and minimizing discontinuities, solving splits and merges. Spatio-temporal descriptors are then generated for each event to assess their risk.
The algorithm was tested offline on the infrared data acquired during the last operation phase OP1.2 and the results are presented here. Further work will follow to accelerate the code with GPUs to reach real-time processing and be ready to protect the water-cooled plasma facing components in the forthcoming operation phase OP2.

}, url = {https://conferences.iaea.org/event/251/contributions/20704/}, author = {Puig-Sitjes, A. and Casas, J. and Salembier, P. and Vizcarro, D. and Clemente, R. and Jakubowski, M. and Gao, Y. and W7-X Team} } @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} } @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} } @phdthesis {dPujol-Miro20, title = {Learning to extract features for 2D-3D multimodal registration}, year = {2020}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

The ability to capture depth information form an scene has greatly increased in the recent years. 3D sensors, traditionally high cost and low resolution sensors, are being democratized and 3D scans of indoor and outdoor scenes are becoming more and more common.

However, there is still a great data gap between the amount of captures being per- formed with 2D and 3D sensors. Although the 3D sensors provide more information about the scene, 2D sensors are still more accessible and widely used. This trade-off between availability and information between sensors brings us to a multimodal scenario of mixed 2D and 3D data.

This thesis explores the fundamental block of this multimodal scenario: the reg- istration between a single 2D image and a single unorganized point cloud. An unorganized 3D point cloud is the basic representation of a 3D capture. In this representation the surveyed points are represented only by their real word coordi- nates and, optionally, by their colour information. This simplistic representation brings multiple challenges to the registration, since most of the state of the art works leverage the existence of metadata about the scene or prior knowledges.

Two different techniques are explored to perform the registration: a keypoint-based technique and an edge-based technique. The keypoint-based technique estimates the transformation by means of correspondences detected using Deep Learning, whilst the edge-based technique refines a transformation using a multimodal edge detection to establish anchor points to perform the estimation.

An extensive evaluation of the proposed methodologies is performed. Albeit further research is needed to achieve adequate performances, the obtained results show the potential of the usage of deep learning techniques to learn 2D and 3D similari- ties. The results also show the good performance of the proposed 2D-3D iterative refinement, up to the state of the art on 3D-3D registration.

}, url = {http://hdl.handle.net/2117/330132}, author = {A. Pujol-Mir{\'o}}, editor = {Casas, J. and Ruiz-Hidalgo, J.} } @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} } @conference {cPardas20, title = {Refinement network for unsupervised on the scene foreground segmentation}, booktitle = {EUSIPCO European Signal Processing Conference}, year = {2020}, month = {08/2020}, publisher = {European Association for Signal Processing (EURASIP)}, organization = {European Association for Signal Processing (EURASIP)}, abstract = {

In this paper we present a network for foreground segmentation based on background subtraction which does not require specific scene training. The network is built as a refinement step on top of classic state of the art background subtraction systems. In this way, the system combines the possibility to define application oriented specifications as background subtraction systems do, and the highly accurate object segmentation abilities of deep learning systems. The refinement system is based on a semantic segmentation network. The network is trained on a common database and is not fine-tuned for the specific scenes, unlike existing solutions for foreground segmentation based on CNNs. Experiments on available databases show top results among unsupervised methods.

}, url = {https://www.eurasip.org/Proceedings/Eusipco/Eusipco2020/pdfs/0000705.pdf}, author = {M. Pard{\`a}s and G. Canet} } @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.} } @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 {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} } @article {aPujol-Miro19, title = {Correspondence matching in unorganized 3D point clouds using Convolutional Neural Networks}, journal = {Image and Vision Computing}, volume = {83-84}, year = {2019}, month = {04/2019}, chapter = {51-60}, abstract = {

This document presents a novel method based in Convolutional Neural Networks (CNN) to obtain correspondence matchings between sets of keypoints of several unorganized 3D point cloud captures, independently of the sensor used. The proposed technique extends a state-of-the-art method for correspondence matching in standard 2D images to sets of unorganized 3D point clouds. The strategy consists in projecting the 3D neighborhood of the keypoint onto an RGBD patch, and the classification of patch pairs using CNNs. The objective evaluation of the proposed 3D point matching based in CNNs outperforms existing 3D feature descriptors, especially when intensity or color data is available.

}, doi = {https://doi.org/10.1016/j.imavis.2019.02.013}, author = {A. Pujol-Mir{\'o} and Casas, J. and Ruiz-Hidalgo, J.} } @article {aLin19, title = {Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network}, journal = {Sensors}, volume = {19}, year = {2019}, month = {04/2019}, chapter = {1795}, abstract = {

Semantic segmentation and depth estimation are two important tasks in computer vision, and many methods have been developed to tackle them. Commonly these two tasks are addressed independently, but recently the idea of merging these two problems into a sole framework has been studied under the assumption that integrating two highly correlated tasks may benefit each other to improve the estimation accuracy. In this paper, depth estimation and semantic segmentation are jointly addressed using a single RGB input image under a unified convolutional neural network. We analyze two different architectures to evaluate which features are more relevant when shared by the two tasks and which features should be kept separated to achieve a mutual improvement. Likewise, our approaches are evaluated under two different scenarios designed to review our results versus single-task and multi-task methods. Qualitative and quantitative experiments demonstrate that the performance of our methodology outperforms the state of the art on single-task approaches, while obtaining competitive results compared with other multi-task methods.

}, keywords = {depth estimation; semantic segmentation; convolutional neural networks; hybrid architecture}, issn = {1424-8220}, doi = {10.3390/s19081795}, url = {https://www.mdpi.com/1424-8220/19/8/1795}, author = {X. Lin and D. Sanchez-Escobedo and Casas, J. and M. Pard{\`a}s} } @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} } @conference {cLin19, title = {One Shot Learning for Generic Instance Segmentation in RGBD Videos}, booktitle = {International Conference on Computer Vision, Theory and Applications}, year = {2019}, month = {02/2019}, publisher = {SciTePress}, organization = {SciTePress}, address = {Prague}, abstract = {

Hand-crafted features employed in classical generic instance segmentation methods have limited discriminative power to distinguish different objects in the scene, while Convolutional Neural Networks (CNNs) based semantic segmentation is restricted to predefined semantics and not aware of object instances. In this paper, we combine the advantages of the two methodologies and apply the combined approach to solve a generic instance segmentation problem in RGBD video sequences. In practice, a classical generic instance segmentation method is employed to initially detect object instances and build temporal correspondences, whereas instance models are trained based on the few detected instance samples via CNNs to generate robust features for instance segmentation. We exploit the idea of one shot learning to deal with the small training sample size problem when training CNNs. Experiment results illustrate the promising performance of the proposed approach.

}, keywords = {Convolutional Neural Network, Instance Segmentation, One Shot Learning}, isbn = {978-989-758-354-4}, doi = {10.5220/0007259902330239}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @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} } @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} } @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 {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 {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} } @mastersthesis {xPelegri18, title = {Clasificaci{\'o}n de im{\'a}genes histol{\'o}gicas mediante redes neuronales convolucionales}, year = {2018}, author = {Joel Bustos Pelegr{\'\i}}, editor = {Marc Combalia and Ver{\'o}nica Vilaplana} } @conference {cSanchez-Escobedo, title = {HybridNet for Depth Estimation and Semantic Segmentation}, booktitle = {ICASSP 2018}, year = {2018}, month = {04/2018}, publisher = {IEEE}, organization = {IEEE}, address = {Calgary, Alberta, Canada}, abstract = {

Semantic segmentation and depth estimation are two important tasks in the area of image processing. Traditionally, these two tasks are addressed in an independent manner. However, for those applications where geometric and semantic information is required, such as robotics or autonomous navigation, depth or semantic segmentation alone are not sufficient. In this paper, depth estimation and semantic segmentation are addressed together from a single input image through a hybrid convolutional network. Different from the state of the art methods where features are extracted by a sole feature extraction network for both tasks, the proposed HybridNet improves the features extraction by separating the relevant features for one task from those which are relevant for both. Experimental results demonstrate that HybridNet results are comparable with the state of the art methods, as well as the single task methods that HybridNet is based on.

}, doi = {10.1109/ICASSP.2018.8462433}, url = {https://ieeexplore.ieee.org/abstract/document/8462433}, author = {D. Sanchez-Escobedo and X. Lin and Casas, J. and M. Pard{\`a}s} } @article {pPerez-Pellitero17, title = {Method for upscaling an image and apparatus for upscaling an image}, number = {US 20170132759 A1}, year = {2018}, month = {05/2017}, address = {US 20170132759 A1}, abstract = {

Image super-resolution (SR) generally enhance the resolution of images. One of SR{\textquoteright}s main challenge is discovering mappings between low-resolution (LR) and high-resolution (HR) image patches. The invention learns patch upscaling projection matrices from a training set of images. Input images are divided into overlapping patches, which are normalized and transformed to a defined orientation. Different transformations can be recognized and dealt with by using a simple 2D-projection. The transformed patches are clustered, and cluster specific upscaling projection matrices and corresponding cluster centroids determined during training are applied to obtain upscaled patches. The upscaled patches are assembled to an upscaled image.

https://worldwide.espacenet.com/publicationDetails/biblio?II=2\&ND=3\&adjacent=true\&locale=en_EP\&FT=D\&date=20170511\&CC=US\&NR=2017132759A1\&KC=A1

}, issn = {US 20170132759 A1}, url = {https://register.epo.org/ipfwretrieve?apn=US.201615341080.A\&lng=en}, author = {E. Perez-Pellitero and Salvador, J. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @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} } @mastersthesis {xCanaves18, title = {Prevention of Alzheimer{\textquoteright}s Disease: a contribution from MRI and machine learning}, year = {2018}, abstract = {

Alzheimer{\textquoteright}s disease (AD) is a neurodegenerative disease and the leading cause of dementia (50-70\% of cases). Despite worldwide efforts, there is no progress in developing a cure for AD and dementia. Machine learning, hand in hand with magnetic resonance imaging (MRI), come to the aid of disease diagnostics. In the scope of AD, many efforts have been dedicated to the automated detection of mild-cognitive impairment and dementia. In our research, instead we focus on the prediction of AD in its preclinical stage using machine learning classification. Another key innovation is that we will work with a longitudinal pipeline. In addition to classification, the project focuses on detecting the most relevant imaging voxels for classification, that is, to help us locate where AD-specific structural brain changes occur. We have improved classification performance i n comparison with results obtained with cross-sectional datasets in previous studies and we have identified possible regions of interest based on feature scores obtained from feature selection.

}, author = {Miguel Artigues C{\`a}naves}, editor = {Paula Petrone 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} } @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} } @phdthesis {dLin18, title = {Semantic and Generic Object Segmentation for Scene Analysis Using RGB-D Data}, year = {2018}, month = {07/2018}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {PhD}, abstract = {

In this thesis, we study RGB-D based segmentation problems from different per- spectives in terms of the input data. Apart from the basic photometric and geometric information contained in the RGB-D data, also semantic and temporal information are usually considered in an RGB-D based segmentation system.

The first part of this thesis focuses on an RGB-D based semantic segmentation problem, where the predefined semantics and annotated training data are available. First, we review how RGB-D data has been exploited in the state-of-the-art to help training classifiers in semantic segmentation task. Inspired by these works, we follow a multi-task learning schema, where semantic segmentation and depth estimation are jointly tackled in a Convolutional Neural Network (CNN). Since semantic segmenta- tion and depth estimation are two highly correlated tasks, approaching them jointly can be mutually beneficial. In this case, depth information along with the segmenta- tion annotation in the training data helps better defining the target of the training process of the classifier, instead of feeding the system blindly with an extra input channel. We design a novel hybrid CNN architecture by investigating the common attributes as well as the distinction for depth estimation and semantic segmentation. The proposed architecture is tested and compared with state-of-the-art approaches in different datasets.

Although outstanding results are achieved in semantic segmentation, the limita- tions in these approaches are also obvious. Semantic segmentation strongly relies on predefined semantics and a large amount of annotated data, which may not be avail- able in more general applications. On the other hand, classical image segmentation tackles the segmentation task in a more general way. But classical approaches hardly obtain object level segmentation due to the lack of higher level knowledge. Thus, in the second part of this thesis, we focus on an RGB-D based generic instance segmenta- tion problem where temporal information is available from the RGB-D video while no semantic information is provided. We present a novel generic segmentation approach for 3D point cloud video (stream data) thoroughly exploiting the explicit geometry iii and temporal correspondences in RGB-D. The proposed approach is validated and compared with state-of-the-art generic segmentation approaches in different datasets.

Finally, in the third part of this thesis, we present a method which combines the advantages in both semantic segmentation and generic segmentation, where we discover object instances using the generic approach and model them by learning from the few discovered examples by applying the approach of semantic segmentation. To do so, we employ the one shot learning technique, which performs knowledge transfer from a generally trained model to a specific instance model. The learned instance models generate robust features in distinguishing dierent instances, which is fed to the generic segmentation approach to perform improved segmentation. The approach is validated with experiments conducted on a carefully selected dataset.

}, url = {http://hdl.handle.net/10803/620762}, author = {X. Lin}, editor = {Casas, J. and M. Pard{\`a}s} } @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} } @article {aLin, title = {Temporally Coherent 3D Point Cloud Video Segmentation in Generic Scenes}, journal = {IEEE Transactions on Image Processing}, volume = {27}, year = {2018}, month = {06/2018}, pages = {3087 - 3099}, abstract = {

Video segmentation is an important building block for high level applications such as scene understanding and interaction analysis. While outstanding results are achieved in this field by state-of-the-art learning and model based methods, they are restricted to certain types of scenes or require a large amount of annotated training data to achieve object segmentation in generic scenes. On the other hand, RGBD data, widely available with the introduction of consumer depth sensors, provides actual world 3D geometry compared to 2D images. The explicit geometry in RGBD data greatly helps in computer vision tasks, but the lack of annotations in this type of data may also hinder the extension of learning based methods to RGBD. In this paper, we present a novel generic segmentation approach for 3D point cloud video (stream data) thoroughly exploiting the explicit geometry in RGBD. Our proposal is only based on low level features, such as connectivity and compactness. We exploit temporal coherence by representing the rough estimation of objects in a single frame with a hierarchical structure, and propagating this hierarchy along time. The hierarchical structure provides an efficient way to establish temporal correspondences at different scales of object-connectivity, and to temporally manage the splits and merges of objects. This allows updating the segmentation according to the evidence observed in the history. The proposed method is evaluated on several challenging datasets, with promising results for the presented approach.

}, issn = {1057-7149}, doi = {10.1109/TIP.2018.2811541}, url = {https://ieeexplore.ieee.org/document/8306148/}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @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.} } @mastersthesis {xArenas, title = {Video Understanding through the Disentanglement of Appearance and Motion}, year = {2018}, abstract = {

Understanding the inner workings of deep learning algorithms is key to eciently exploit the\ large number of videos that are generated every day. For the self-supervised learning of the spatiotemporal\ information contained within these videos, there are several types of algorithms based\ on convolutional neural networks (CNNs) following an auto-encoder style architecture. However,\ we have checked that this type of models, trained for the frame prediction task, learn jointly\ these spatio-temporal information, so the model is not able to recognize appearance-motion\ combinations not seen during training. Our proposed model, called DisNet, can learn separately\ the appearance and motion through disentanglement, so that it solves the generalization and\ scalability problems. To demonstrate this, we conducted numerous experiments under highly\ controlled conditions, generating speci c datasets that make the conventional model fail for\ the appearance and motion classi cation tasks, and analyzing how well our proposal behaves\ under the same conditions.

}, author = {Arenas, Carlos}, editor = {Xavier Gir{\'o}-i-Nieto and V{\'\i}ctor Campos and Palacio, Sebastian} } @inbook {bCasamitjana, title = {3D Convolutional Neural Networks for Brain Tumor Segmentation: a comparison of multi-resolution architectures}, booktitle = {Lecture Notes in Computer Vision}, volume = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries}, year = {2017}, pages = {150-161}, publisher = {Springer}, organization = {Springer}, issn = {978-3-319-55524-9}, author = {Adri{\`a} Casamitjana and Santi Puch and Asier Aduriz and Ver{\'o}nica Vilaplana} } @conference {cLinb, title = {3D Point Cloud Segmentation Using a Fully Connected Conditional Random Field}, booktitle = {The 25th European Signal Processing Conference (EUSIPCO 2017)}, year = {2017}, month = {08/2017}, publisher = {Eurasip/IEEE}, organization = {Eurasip/IEEE}, address = {Kos island, Greece}, abstract = {

Traditional image segmentation methods working with low level image features are usually difficult to be adapted to higher level tasks, such as object recognition and scene understanding. Object segmentation emerges as a new challenge in this research filed. It aims at obtaining more meaningful segments related to semantic objects in the scene by analyzing a combination of different information. 3D point cloud data obtained from consumer depth sensors has been exploited to tackle many computer vision problems due to its richer information about the geometry of 3D scenes compared to 2D images. Meanwhile, new challenges have also emerged as the depth information is usually noisy, sparse and unorganized. In this paper, we present a novel point cloud segmentation approach for segmenting interacting objects in a stream of point clouds by exploiting spatio-temporal coherence. We pose the problem as an energy minimization task in a fully connected conditional random field with the energy function defined based on both current and previous information. We compare different methods and prove the better segmentation performance and robustness of the proposed approach in sequences with over 2k frames.

}, keywords = {Image color analysis, Image segmentation, Labeling, Object segmentation, Three-dimensional displays, Two dimensional displays}, doi = {10.23919/EUSIPCO.2017.8081170}, url = {https://www.eusipco2017.org/}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @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} } @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} } @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 {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 {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} } @phdthesis {dPerez-Pellitero17, title = {Manifold Learning for Super Resolution}, year = {2017}, school = {Leibniz Universit{\"a}t Hannover}, address = {Hannover}, abstract = {

The development pace of high-resolution displays has been so fast in the recent years that many images acquired with low-end capture devices are already outdated or will be shortly in time. Super Resolution is central to match the resolution of the already existing image content to that of current and future high resolution displays and applications. This dissertation is focused on learning how to upscale images from the statistics of natural images. We build on a sparsity model that uses learned coupled low- and high-resolution dictionaries in order to upscale images.

Firstly, we study how to adaptively build coupled dictionaries so that their content is semantically related with the input image. We do so by using a Bayesian selection stage which finds the best-fitted texture regions from the training dataset for each input image. The resulting adapted subset of patches is compressed into a coupled dictionary via sparse coding techniques.

We then shift from l1 to a more efficient l2 regularization, as introduced by Timofte et al. Instead of using their patch-to-dictionary decomposition, we propose a fully collaborative neighbor embedding approach. In this novel scheme, for each atom in the dictionary we create a densely populated neighborhood from an extensive training set of raw patches (i.e. in the order of hundreds of thousands). This generates more accurate regression functions.

We additionally propose using sublinear search structures such as spherical hashing and trees to speed up the nearest neighbor search involved in regression-based Super Resolution. We study the positive impact of antipodally invariant metrics for linear regression frameworks, and we propose two efficient solutions: (a) the Half Hypersphere Confinement, which enables antipodal invariance within the Euclidean space, and (b) the bimodal tree, whose split functions are designed to be antipodally invariant and which we use in the context of a Bayesian Super Resolution forest.

In our last contribution, we extend antipodal invariance by also taking into consideration the dihedral group of transforms (i.e. rotations and reflections). We study them as a group of symmetries within the high-dimensional manifold. We obtain the respective set of mirror-symmetry axes by means of a frequency analysis, and we use them to collapse the redundant variability, resulting in a reduced manifold span which, in turn, greatly improves quality performance and reduces the dictionary sizes.

}, author = {E. Perez-Pellitero}, editor = {Rosenhahn, B. and Ruiz-Hidalgo, J.} } @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} } @conference {cPujol-Miro17, title = {Registration of Images to Unorganized 3D Point Clouds Using Contour Cues}, booktitle = {The 25th European Signal Processing Conference (EUSIPCO 2017)}, year = {2017}, month = {08/2017}, publisher = {Eurasip}, organization = {Eurasip}, address = {Kos island, Greece}, abstract = {

Low resolution commercial 3D sensors contribute to computer vision tasks even better when the analysis is carried out in a combination with higher resolution image data. This requires registration of 2D images to unorganized 3D point clouds. In this paper we present a framework for 2D-3D data fusion to obtain directly the camera pose of a 2D color image in relation to a 3D point cloud. It includes a novel multiscale intensity feature detection algorithm and a modified ICP procedure based on point-to-line distances. The framework is generic for several data types (such as CAD designs or LiDAR data without photometric information), and results show that performance is comparable to the state of the art, while avoiding manual markers or specific patterns on the data.

}, keywords = {Cameras, Feature extraction, Iterative closest point algorithm, Sensors, Signal processing algorithms, Three-dimensional displays}, doi = {10.23919/EUSIPCO.2017.8081173}, url = {https://www.eusipco2017.org/}, author = {A. Pujol-Mir{\'o} and Ruiz-Hidalgo, J. and Casas, J.} } @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 {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} } @conference {cCasamitjana16, title = {3D Convolutional Neural Networks for Brain Tumor Segmentation}, booktitle = {MICCAI 2016 - Brain Lesion Workshop (BrainLes), Multimodal Brain Tumor Segmentation Challenge (BRATS)}, year = {2016}, month = {11/2016}, author = {Adri{\`a} Casamitjana and Santi Puch and Asier Aduriz and Elisa Sayrol and Ver{\'o}nica Vilaplana} } @conference {cLin16a, title = {3D Point Cloud Segmentation Oriented to The Analysis of Interactions}, booktitle = {The 24th European Signal Processing Conference (EUSIPCO 2016)}, year = {2016}, month = {08/2016}, publisher = {Eurasip}, organization = {Eurasip}, address = {Budapest, Hungary}, abstract = {

Given the widespread availability of point cloud data from consumer depth sensors, 3D point cloud segmentation becomes a promising building block for high level applications such as scene understanding and interaction analysis. It benefits from the richer information contained in real world 3D data compared to 2D images. This also implies that the classical color segmentation challenges have shifted to RGBD data, and new challenges have also emerged as the depth information is usually noisy, sparse and unorganized. Meanwhile, the lack of 3D point cloud ground truth labeling also limits the development and comparison among methods in 3D point cloud segmentation. In this paper, we present two contributions: a novel graph based point cloud segmentation method for RGBD stream data with interacting objects and a new ground truth labeling for a previously published data set. This data set focuses on interaction (merge and split between {\textquoteright}object{\textquoteright} point clouds), which differentiates itself from the few existing labeled RGBD data sets which are more oriented to Simultaneous Localization And Mapping (SLAM) tasks. The proposed point cloud segmentation method is evaluated with the 3D point cloud ground truth labeling. Experiments show the promising result of our approach.

}, url = {http://www.eusipco2016.org/$\#$Wed1030}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @conference {cLin, title = {3D Point Cloud Video Segmentation Based on Interaction Analysis}, booktitle = {ECCV 2016: Computer Vision {\textendash} ECCV 2016 Workshops}, volume = {III}, number = {III}, year = {2016}, month = {10/2016}, pages = {821 - 835}, publisher = {Springer}, organization = {Springer}, address = {Amsterdam}, abstract = {

Given the widespread availability of point cloud data from consumer depth sensors, 3D segmentation becomes a promising building block for high level applications such as scene understanding and interaction analysis. It benefits from the richer information contained in actual world 3D data compared to apparent (projected) data in 2D images. This also implies that the classical color segmentation challenges have recently shifted to RGBD data, whereas new emerging challenges are added as 3D information from depth measurements is usually noisy, sparse and unorganized. We present a novel segmentation approach for 3D point cloud video based on low level features and oriented to the analysis of object interactions. A hierarchical representation of the input point cloud is proposed to efficiently segment 3D data at the finer level, and to temporally establish the correspondence between segments, while dynamically managing the object split and merge at the coarser level. Experiments illustrate promising results and its potential application in object interaction analysis.

}, issn = {978-3-319-49409-8}, doi = {10.1007/978-3-319-49409-8}, url = {http://eecs.oregonstate.edu/IWVS_workshop/}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @article {aPerez-Pellitero16, title = {Antipodally Invariant Metrics For Fast Regression-Based Super-Resolution}, journal = {IEEE Transactions on Image Processing}, volume = {25}, year = {2016}, month = {06/2016}, pages = {2468}, chapter = {2456}, abstract = {

Dictionary-based Super-Resolution algorithms usually select dictionary atoms based on distance or similarity metrics. Although the optimal selection of nearest neighbors is of central importance for such methods, the impact of using proper metrics for Super-Resolution (SR) has been overlooked in literature, mainly due to the vast usage of Euclidean distance. In this paper we present a very fast regression-based algorithm which builds on densely populated anchored neighborhoods and sublinear search structures. We perform a study of the nature of the features commonly used for SR, observing that those features usually lie in the unitary hypersphere, where every point has a diametrically opposite one, i.e. its antipode, with same module and angle, but opposite direction. Even though we validate the benefits of using antipodally invariant metrics, most of the binary splits use Euclidean distance, which does not handle antipodes optimally. In order to benefit from both worlds, we propose a simple yet effective Antipodally Invariant Transform (AIT) that can be easily included in the Euclidean distance calculation. We modify the original Spherical Hashing algorithm with this metric in our Antipodally Invariant Spherical Hashing scheme, obtaining the same performance as a pure antipodally invariant metric. We round up our contributions with a novel feature transform that obtains a better coarse approximation of the input image thanks to Iterative Back Projection. The performance of our method, which we named Antipodally Invariant Super-Resolution (AIS), improves quality (PSNR) and it is faster than any other state-of-the-art method.

}, doi = {10.1109/TIP.2016.2549362}, url = {http://perezpellitero.github.io/project_websites/ais_sr.html}, author = {E. Perez-Pellitero and Salvador, J. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @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} } @conference {cLin16, title = {Graph based Dynamic Segmentation of Generic Objects in 3D}, booktitle = {CVPR SUNw: Scene Understanding Workshop}, year = {2016}, month = {06/2016}, address = {Las Vegas, US}, abstract = {

We propose a novel 3D segmentation method for RBGD stream data to deal with 3D object segmentation task in a generic scenario with frequent object interactions. It mainly contributes in two aspects, while being generic and not requiring initialization: firstly, a novel tree structure representation for the point cloud of the scene is proposed. Then, a dynamic manangement mechanism for connected component splits and merges exploits the tree structure representation.

}, url = {http://sunw.csail.mit.edu/posters.html}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @conference {cPerez-Pellitero16, title = {Half Hypersphere Confinement for Piecewise Linear Regression}, booktitle = {IEEE Winter Conference on Applications of Computer Vision}, year = {2016}, month = {03/2016}, address = {Lake Placid, NY, USA}, abstract = {

2016 WACV\  \  \ 

IIn this paper we study the characteristics of the metrics best suited for the piecewise regression algorithms, in which comparisons are usually made between normalized vectors that lie on the unitary hypersphere. Even though Euclidean distance has been widely used for this purpose, it is suboptimal since it does not handle antipodal points (i.e. diametrically opposite points) properly. Therefore, we propose the usage of antipodally invariant metrics and introduce the Half Hypersphere Confinement (HHC), a fast alternative to Multidimensional Scaling (MDS) that allows to map antipodally invariant distances in the Euclidean space with very little approximation error. The performance of our method, which we named HHC Regression (HHCR), applied to Super-Resolution (SR) improves both in quality (PSNR) and it is faster than any other state-of-the-art method. Additionally, under an application-agnostic interpretation of our regression framework, we also test our algorithm for denoising and depth upscaling with promising results.

}, doi = {10.1109/WACV.2016.7477651}, author = {E. Perez-Pellitero and Salvador, J. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @conference {cCasanova16, title = {Interactive Registration Method for 3D data Fusion}, booktitle = {IC3D}, year = {2016}, month = {12/2016}, publisher = {IEEE}, organization = {IEEE}, address = {Li{\`e}ge, Belgium}, abstract = {

Commercial depth sensors represent an opportunity for automation of certain 3D production and analysis tasks. One way to overcome some of their inherent limitations is by capturing the same scene with several depth sensors and merging their data, i.e. by performing 3D data fusion, which requires the registration of point clouds from different sensors. We propose a new interactive, fast and user-friendly method for depth sensor registration. We replace the traditional checkerboard pattern used to extract key points in the scene by a finger detector. This provides a main advantage: the method is easier to use and does not require external objects, while the elapsed time and the registration error are similar to those obtained through the classical method.

We test the proposed approach with an interactive hand tracking application, improved to use more than a single sensor, and we show the increase in detection area by more than 70\%.

}, url = {http://www.3dstereomedia.eu/ic3d}, author = {A. Casanova and A. Pujol-Mir{\'o} and Ruiz-Hidalgo, J. and Casas, J.} } @mastersthesis {xPuch16, title = {Nonlinear analysis toolbox for neurodegenerative diseases and aging}, year = {2016}, abstract = {

Advisor: Ver{\'o}nica Vilaplana

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

Neurodegenerative diseases impose substantial public health burdens on populations
throughout the world. Alzheimer{\textquoteright}s disease is among the major neurodegenerative diseases,
and its causes and treatment are still unknown. Researchers around the world are conducting
large data-driven studies in order to unveil the causes and biological mechanisms of such
diseases, and for that reason automatic tools that allow to uncover statistically signi cant
ndings are needed.
To address this problem we present in this thesis a software toolbox that provides the
tools to analyze the linear and nonlinear dynamics of gray-matter and study the statistical
signi cance of such dynamics at the voxel level. The toolbox features various tting methods
and t evaluation metrics, an automatic hyperparameters look-up algorithm and several
visualization and comparison tools.
All the features provided in this toolbox were tested in two real problems provided by
the Pasqual Maragall Foundation, and it yielded results that were validated by the ndings
in the original studies.

}, author = {Santi Puch}, editor = {Ver{\'o}nica Vilaplana} } @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} } @conference {cPerez-Pellitero, title = {PSyCo: Manifold Span Reduction for Super Resolution}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}, year = {2016}, month = {06/2016}, address = {Las Vegas, Nevada, USA}, abstract = {

The main challenge in Super Resolution (SR) is to discover the mapping between the low- and high-resolution manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear regression with promising results. In this paper we present a novel regression-based SR algorithm that benefits from an extended knowledge of the structure of both manifolds. We propose a transform that collapses the 16 variations induced from the dihedral group of transforms (i.e. rotations, vertical and horizontal reflections) and antipodality (i.e. di- ametrically opposed points in the unitary sphere) into a single primitive. The key idea of our transform is to study the different dihedral elements as a group of symmetries within the high-dimensional manifold. We obtain the respective set of mirror-symmetry axes by means of a frequency analysis of the dihedral elements, and we use them to collapse the redundant variability through a modified symmetry distance. The experimental validation of our algorithm shows the effectiveness of our approach, which obtains competitive quality with a dictionary of as little as 32 atoms (reducing other methods{\textquoteright} dictionaries by at least a factor of 32) and further pushing the state-of-the-art with a 1024 atoms dictionary.

}, url = {http://perezpellitero.github.io/}, author = {E. Perez-Pellitero and Salvador, J. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @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.} } @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} } @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 {cPuch, title = {Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling}, booktitle = {NIPS 2016 Workshop on Machine Learning for Health}, year = {2016}, month = {12/2016}, author = {Santi Puch and Asier Aduriz and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cPerez-Pellitero15, title = {Accelerating Super-Resolution for 4K Upscaling}, booktitle = {IEEE International Conference on Consumer Electronics}, year = {2015}, month = {01/2015}, address = {Las Vegas, NV, USA}, abstract = {

This paper presents a fast Super-Resolution (SR) algorithm based on a selective patch processing. Motivated by the observation that some regions of images are smooth and unfocused and can be properly upscaled with fast interpolation methods, we locally estimate the probability of performing a degradation-free upscaling. Our proposed framework explores the usage of supervised machine learning techniques and tackles the problem using binary boosted tree classifiers. The applied upscaler is chosen based on the obtained probabilities: (1) A fast upscaler (e.g. bicubic interpolation) for those regions which are smooth or (2) a linear regression SR algorithm for those which are ill-posed. The proposed strategy accelerates SR by only processing the regions which benefit from it, thus not compromising quality. Furthermore all the algorithms composing the pipeline are naturally parallelizable and further speed-ups could be obtained.

}, doi = {10.1109/ICCE.2015.7066429}, author = {E. Perez-Pellitero and Salvador, J. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @mastersthesis {xPanizo15, title = {Classification techniques for Alzheimer{\textquoteright}s disease early diagnosis}, year = {2015}, abstract = {

Advisor: Ver{\'o}nica Vilaplana

Studies: Telecommunication Engineering at\ Telecom BCN-ETSETB\ from the Technical University of Catalonia (UPC)

Alzheimer{\textquoteright}s disease currently affects more than 36 million people in the world. A patient{\textquoteright}s brain suffers changes during the earliest stages of the disease and long before showing any clinical symptoms. For that reason, researchers focus their efforts towards defining which changes occur and where do they take place, with the goal of detecting indicators to predict the development of the disease. Specifically, the entity Fundaci{\'o}n Pascual Maragall para la investigaci{\'o}n contra el Alzheimer studies the processes of the brain all along the disease{\textquoteright}s stages using images obtained through different MRI techniques. The huge volume of data generated in this kind of investigation is a big obstacle to carry out analysis and extracting conclusions. The aim of this thesis is making this process easier by using data mining techniques. The goal is to develop a basic classification system to distinguish in which stage of the disease a patient is in, using data extracted from cerebral images. This system must form the basis for a future data mining system that satisfies the necessities of the Fundaci{\'o}n Pascual Maragall researchers. In addition to the classification system, this project focuses on distinguishing which is the most relevant data in the classification and on optimizing the classification in the pre-clinical stage of the disease.

}, author = {Eva Panizo}, editor = {Ver{\'o}nica Vilaplana} } @article {xPan, title = {End-to-end Convolutional Network for Saliency Prediction}, year = {2015}, month = {07/2015}, institution = {arXiv}, address = {Boston, MA (USA)}, abstract = {

The prediction of saliency 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 network. 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 a not very deep architecture which is both fast and accurate. The convolutional network in this paper, named JuntingNet, won the LSUN 2015 challenge on saliency prediction with a superior performance in all considered metrics.



}, url = {http://arxiv.org/abs/1507.01422}, author = {Pan, Junting and Xavier Gir{\'o}-i-Nieto} } @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 {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.} } @conference {cLin15, title = {Time consistent estimation of End-effectors from RGB-D data}, booktitle = {Image and Video Technology: 7th Pacific-Rim Symposium, PSIVT 2015, Auckland, New Zealand, November 25-27, 2015, Revised Selected Papers}, number = {9431}, year = {2015}, month = {12/2015}, pages = {529-543}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

End-effectors are usually related to the location of the free end of a kinematic chain. Each of them contains rich structure information about the entity. Hence, estimating stable end-effectors of different entities enables robust tracking as well as a generic representation. In this paper, we present a system for end-effector estimation from RGB-D stream data. Instead of relying on a specific pose or configuration for initialization, we exploit time coherence without making any assumption with respect to the prior knowledge. This makes the estimation process more robust in a predict-update framework. Qualitative and quantitative experiments are performed against the reference method with promising results.

}, keywords = {End-effector estimation; Time coherence; Topology representation}, isbn = {978-3-319-29451-3}, doi = {10.1007/978-3-319-29451-3_42}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @mastersthesis {xPana, title = {Visual Saliency Prediction using Deep learning Techniques}, year = {2015}, abstract = {

Advisor: 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 with honors (9.9/10.0)

A saliency map is a model that predicts eye fixations on a visual scene. In other words, it is the prediction of saliency areas in images has been traditionally addressed with hand crafted features inspired on neuroscience principles. This work however addresses the problem with a completely data-driven approach by training a convolutional network. The recent publication of large datasets of saliency prediction has provided enough data to train a not very deep network architecture which is both fast and accurate. In our system, named JuntingNet, 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 convolutional network developed in this work, named JuntingNet, won the CVPR Large-scale Scene UNderstanding (LSUN) 2015 challenge on saliency prediction with a superior performance in all considered metrics.

Saliency prediction using deep learning techniques from Xavier Giro

2015-TFG-JuntingPan-VisualSaliencyPredictionUsingDeepLearningTechniques from Image Processing Group on Vimeo.

See https://imatge.upc.edu/web/resources/end-end-convolutional-networks-saliency-prediction-software.

}, author = {Pan, Junting}, editor = {Xavier Gir{\'o}-i-Nieto} } @article {aPaloua, title = {Depth order estimation for video frames using motion occlusions}, journal = {IET Computer Vision}, volume = {8}, year = {2014}, month = {04/2014}, pages = {152-160}, doi = {10.1049/iet-cvi.2012.0287}, author = {Palou, G. and Salembier, P.} } @conference {cBosch14, title = {An Epipolar-Constrained Prior for Efficient Search in Multi-View Scenarios}, booktitle = {EUSIPCO}, year = {2014}, month = {09/2014}, address = {Lisbon}, abstract = {

In this paper we propose a novel framework for fast exploitation of multi-view cues with applicability in different image processing problems. In order to bring our proposed framework into practice, an epipolar-constrained prior is presented, onto which a random search algorithm is proposed to find good matches among the different views of the same scene. This algorithm includes a generalization of the local coherency in 2D images for multi-view wide-baseline cases. Experimental results show that the geometrical constraint allows a faster initial convergence when finding good matches. We present some applications of the proposed framework on classical image processing problems.

}, keywords = {approximate nearest neighbor, deblurring, epipolar line, Super resolution}, author = {Bosch, I. and Salvador, J. and E. Perez-Pellitero and Ruiz-Hidalgo, J.} } @conference {cPerez-Pellitero14, title = {Fast Super-Resolution via Dense Local Training and Inverse Regressor Search}, booktitle = {Asian Conference in Computer Vision (ACCV)}, year = {2014}, month = {11/2014}, address = {Singapore}, abstract = {

Regression-based Super-Resolution (SR) addresses the up- scaling problem by learning a mapping function (i.e. regressor) from the low-resolution to the high-resolution manifold. Under the locally linear assumption, this complex non-linear mapping can be properly modeled by a set of linear regressors distributed across the manifold. In such methods, most of the testing time is spent searching for the right regressor within this trained set. In this paper we propose a novel inverse-search approach for regression-based SR. Instead of performing a search from the image to the dictionary of regressors, the search is done inversely from the regressors{\textquoteright} dictionary to the image patches. We approximate this framework by applying spherical hashing to both image and regressors, which reduces the inverse search into computing a trained function. Additionally, we propose an improved training scheme for SR linear regressors which improves perceived and objective quality. By merging both contributions we improve speed and quality compared to the state-of-the-art.

}, author = {E. Perez-Pellitero and Salvador, J. and Torres-Xirau, I. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @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.} } @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.} } @phdthesis {dPalou14, title = {Monocular Depth Estimation in Images and Sequences using Occlusion Cues}, volume = {PhD}, year = {2014}, month = {02/2014}, pages = {250}, abstract = {

When humans observe a scene, they are able to perfectly distinguish the different parts composing it. Moreover, humans can easily reconstruct the spatial position of these parts and conceive a consistent structure. The mechanisms involving visual perception have been studied since the beginning of neuroscience but, still today, not all the processes composing it are known. In usual situations, humans can make use of three different methods to estimate the scene structure. The first one is the so called divergence and it makes use of both eyes. \ When objects lie in front of the observed at a distance up to hundred meters, subtle differences in the image formation in each eye can be used to determine depth. When objects are not in the field of view of both eyes, other mechanisms should be used. In these cases, both visual cues and prior learned information can be used to determine depth. Even if these mechanisms are less accurate than divergence, humans can almost always infer the correct depth structure when using them. As an example of visual cues, occlusion, perspective or object size provide a lot of information about the structure of the scene. A priori information depends on each observer, but it is normally used subconsciously by humans to detect commonly known regions such as the sky, the ground or different types of objects.

In the last years, since technology has been able to handle the processing burden of vision systems, there has been lots of efforts devoted to design automated scene interpreting systems. In this thesis we address the problem of depth estimation using only one point of view and using only occlusion depth cues. The thesis objective is to detect occlusions present in the scene \ and combine them with a segmentation system so as to generate a relative depth order depth map for a scene. We explore both static and dynamic situations such as single images, frame inside sequences or full video sequences. In the case where a full image sequence is available, a system exploiting motion information to recover depth structure is also designed. Results are promising and competitive with respect to the state of the art literature, but there is still much room for improvement when compared to human depth perception performance.

}, url = {http://hdl.handle.net/10803/144653}, author = {Palou, G.}, editor = {Salembier, P.} } @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 {cGallego14a, title = {Multiview Foreground Segmentation using 3D Probabilistic Model}, booktitle = {ICIP, IEEE International Conference on Image Processing}, year = {2014}, month = {10/2014}, abstract = {

We propose a complete multi-view foreground segmentation and 3D reconstruction system that defines a 3-dimensional probabilistic model to model the foreground object in the 3 spatial dimensions, thus gathering the information from all the camera views. This 3D model is projected to each one of the views in order to perform the 2D segmentation with the foreground information shared by all the cameras. Then, for each one of the views, a MAP-MRF classification framework is applied between the projected region-based foreground model, the pixel-wise background model and the regionbased shadow model defined for each view. The resultant masks are used to compute the next 3-dimensional reconstruction. This system achieves correct results by reducing the false positive and false negative errors in sequences where some camera sensors can present camouflage situations between foreground and background. Moreover, the use of the 3D model opens possibilities to use it for objects recognition or human activity understanding.

}, author = {Gallego, J. and M. Pard{\`a}s} } @conference {cPalou14, title = {Precision-Recall-Classification Evaluation Framework: Application to Depth Estimation on Single Images}, booktitle = {European Conference on Computer Vision (ECCV)}, year = {2014}, month = {09/2014}, address = {Zurich}, abstract = {

Many computer vision applications involve algorithms that can be decomposed in two main steps. In a first step, \ events or objects are detected and, in a subsequent stage, detections are assigned to various classes. Examples of such {\textquoteleft}{\textquoteleft}detection plus classification{\textquoteright}{\textquoteright} problems can be found in human pose classification, object recognition or action classification among others. In this paper, we focus on a special case: depth ordering on single images. In this \ problem, the detection step consists of the image segmentation, and the classification stage assigns a depth gradient to each contour or a depth order to each region.

In this paper, we discuss the limitations of the classical Precision-Recall evaluation framework for these kind of problems and define an extended framework called {\textquoteleft}{\textquoteleft}Precision-Recall-Classfication{\textquoteright}{\textquoteright} (PRC). Then, we apply this framework to depth ordering problems and design two specific PRC

measures to evaluate both the local and the global depth consistencies. We use these measures to evaluate precisely state of the art depth ordering systems for monocular images. Based on this evaluation, we also propose an extension to the method of [Cal13] applying an optimal graph cut on a hierarchical segmentation structure such as ultrametric contour maps. The resulting system is proven to provide better results than state of the art algorithms.

}, author = {Palou, G. and Salembier, P.} } @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 {cGallego14, title = {Robust 3D SFS reconstruction based on reliability maps}, booktitle = {ICIP, IEEE International Conference on Image Processing}, year = {2014}, month = {10/2014}, abstract = {

This paper deals with Shape from Silhouette (SfS) volumetric reconstruction in the context of multi-view smart room scenarios. The method that we propose first computes a 2D foreground object segmentation in each one of the views, by using region-based models to model the foreground, and shadow classes, and a pixel-wise model to model the background class. Next, we calculate the reliability maps between foreground and background/shadow classes in each view, by computing the hellinger distance among models. These 2D reliability maps are taken into account finally, in the 3D SfS reconstruction algorithm, to obtain an enhanced final volumetric reconstruction. The advantages of our system rely on the possibility to obtain a volumetric representation which automatically defines the optimal tolerance to errors for each one of the voxels of the volume, with a low rate of false positive and false negative errors. The results obtained by using our proposal improve the traditional SfS reconstruction computed with a fixed tolerance for the overall volume.

}, author = {Gallego, J. and M. Pard{\`a}s} } @phdthesis {dAlcoverro14, title = {Stochastic optimization and interactive machine learning for human motion analysis}, year = {2014}, month = {12/2014}, school = {UPC}, address = {Barcelona}, abstract = {

The analysis of human motion from visual data is a central issue in the computer vision research community as it enables a wide range of applications and it still remains a challenging problem when dealing with unconstrained scenarios and general conditions. Human motion analysis is used in the entertainment industry for movies or videogame production, in medical applications for rehabilitation or biomechanical studies. It is also used for human computer interaction in any kind of environment, and moreover, it is used for big data analysis from social networks such as Youtube or Flickr, to mention some of its use cases. In this thesis we have studied human motion analysis techniques with a focus on its application for smart room environments. That is, we have studied methods that will support the analysis of people behavior in the room, allowing interaction with computers in a natural manner and in general, methods that introduce computers in human activity environments to enable new kind of services but in an unobstrusive mode. The thesis is structured in two parts, where we study the problem of 3D pose estimation from multiple views and the recognition of gestures using range sensors. First, we propose a generic framework for hierarchically layered particle filtering (HPF) specially suited for motion capture tasks. Human motion capture problem generally involve tracking or optimization of high-dimensional state vectors where also one have to deal with multi-modal pdfs. HPF allow to overcome the problem by means of multiple passes through substate space variables. Then, based on the HPF framework, we propose a method to estimate the anthropometry of the subject, which at the end allows to obtain a human body model adjusted to the subject. Moreover, we introduce a new weighting function strategy for approximate partitioning of observations and a method that employs body part detections to improve particle propagation and weight evaluation, both integrated within the HPF framework. The second part of this thesis is centered in the detection of gestures, and we have focused the problem of reducing annotation and training efforts required to train a specific gesture. In order to reduce the efforts required to train a gesture detector, we propose a solution based on online random forests that allows training in real-time, while receiving new data in sequence. The main aspect that makes the solution effective is the method we propose to collect the hard negatives examples while training the forests. The method uses the detector trained up to the current frame to test on that frame, and then collects samples based on the response of the detector such that they will be more relevant for training. In this manner, training is more effective in terms of the number of annotated frames required.

}, url = {http://hdl.handle.net/10803/285337}, author = {Alcoverro, M.}, editor = {M. Pard{\`a}s and Casas, J.} } @conference {cPerez-Pellitero13, title = {Bayesian region selection for adaptive dictionary-based Super-Resolution}, booktitle = {British Machine Vision Conference}, year = {2013}, month = {09/2013}, abstract = {

The performance of dictionary-based super-resolution (SR) strongly depends on the contents of the training dataset. Nevertheless, many dictionary-based SR methods randomly select patches from of a larger set of training images to build their dictionaries,\ thus relying on patches being diverse enough. This paper describes an external-dictionary SR algorithm based on adaptively selecting an optimal subset of patches out of the training images. Each training image is divided into sub-image entities, named regions, of such size that texture consistency is preserved. For each input patch to super-resolve, the best-fitting region (with enough high-frequency energy) is found through a Bayesian selection. In order to handle the high number of regions in the train- ing dataset, a local Naive Bayes Nearest Neighbor (NBNN) approach is used. Trained with this adapted subset of patches, sparse coding SR is applied to recover the high-resolution image. Experimental results demonstrate that using our adaptive algorithm produces an improvement in SR performance with respect to non-adaptive training.\ 

}, author = {E. Perez-Pellitero and Salvador, J. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @conference {cPalou13, title = {Hierarchical Video Representation with Trajectory Binary Partition Tree}, booktitle = {Computer Vision and Pattern Recognition (CVPR)}, year = {2013}, month = {06/2013}, address = {Portland, Oregon}, abstract = {

As early stage of video processing, we introduce an iterative trajectory merging algorithm that produces a region-based and hierarchical representation of the video sequence, called the Trajectory Binary Partition Tree (BPT). From this representation, many analysis and graph cut techniques can be used to extract partitions or objects that are useful in the context of specific applications.\ 

In order to define trajectories and to create a precise merging algorithm, color and motion cues have to be used. Both types of informations are very useful to characterize objects but present strong differences of behavior in the spatial and the temporal dimensions. On the one hand, scenes and objects are rich in their spatial color distributions, but these distributions are rather stable over time. Object motion, on the other hand, presents simple structures and low spatial variability but may change from frame to frame. The proposed algorithm takes into account this key difference and relies on different models and associated metrics to deal with color and motion information. We show that the proposed algorithm outperforms existing hierarchical video segmentation algorithms and provides more stable and precise regions.

}, author = {Palou, G. and Salembier, P.} } @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 {aPalou13, title = {Monocular Depth Ordering Using T-junctions and Convexity Occlusion Cues}, journal = {IEEE Transactions on Image Processing}, volume = {22}, year = {2013}, month = {2013}, pages = { 1926 - 1939 }, abstract = {

This paper proposes a system that relates objects in an image using occlusion cues and arranges them according to depth. The system does not rely on a priori knowledge of the scene structure and focuses on detecting special points, such as T-junctions and highly convex contours, to infer the depth relationships between objects in the scene. The system makes extensive use of the binary partition tree as hierarchical region-based image representation jointly with a new approach for candidate T-junction estimation. Since some regions may not involve T-junctions, occlusion is also detected by examining convex shapes on region boundaries. Combining T-junctions and convexity leads to a system which only relies on low level depth cues and does not rely on semantic information. However, it shows a similar or better performance with the state-of-the-art while not assuming any type of scene.

}, doi = {10.1109/TIP.2013.2240002}, author = {Palou, G. and Salembier, P.} } @phdthesis {dGallego13, title = {Parametric Region-Based Foreground Segmentation in Planar and Multi-View Sequences}, year = {2013}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

Foreground segmentation in video sequences is an important area of the image processing that attracts great interest among the scientist community, since it makes possible the detection of the objects that appear in the sequences under analysis, and allows us to achieve a correct performance of high level applications which use foreground segmentation as an initial step. The current Ph.D. thesis entitled Parametric Region-Based Foreground Segmentation in Planar and Multi-View Sequences details, in the following pages, the research work carried out within this field. In this investigation, we propose to use parametric probabilistic models at pixel-wise and region level in order to model the different classes that are involved in the classification process of the different regions of the image: foreground, background and, in some sequences, shadow. The development is presented in the following chapters as a generalization of the techniques proposed for objects segmentation in 2D planar sequences to 3D multi-view environment, where we establish a cooperative relationship between all the sensors that are recording the scene. Hence, different scenarios have been analyzed in this thesis in order to improve the foreground segmentation techniques: In the first part of this research, we present segmentation methods appropriate for 2D planar scenarios. We start dealing with foreground segmentation in static camera sequences, where a system that combines pixel-wise background model with region-based foreground and shadow models is proposed in a Bayesian classification framework. The research continues with the application of this method to moving camera scenarios, where the Bayesian framework is developed between foreground and background classes, both characterized with region-based models, in order to obtain a robust foreground segmentation for this kind of sequences. The second stage of the research is devoted to apply these 2D techniques to multi-view acquisition setups, where several cameras are recording the scene at the same time. At the beginning of this section, we propose a foreground segmentation system for sequences recorded by means of color and depth sensors, which combines different probabilistic models created for the background and foreground classes in each one of the views, by taking into account the reliability that each sensor presents. The investigation goes ahead by proposing foreground segregation methods for multi-view smart room scenarios. In these sections, we design two systems where foreground segmentation and 3D reconstruction are combined in order to improve the results of each process. The proposals end with the presentation of a multi-view segmentation system where a foreground probabilistic model is proposed in the 3D space to gather all the object information that appears in the views. The results presented in each one of the proposals show that the foreground segmentation and also the 3D reconstruction can be improved, in these scenarios, by using parametric probabilistic models for modeling the objects to segment, thus introducing the information of the object in a Bayesian classification framework.

}, url = {http://hdl.handle.net/10803/130813}, author = {Gallego, J.}, editor = {M. Pard{\`a}s} } @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.} } @article {aGallego13, title = {Region Based Foreground Segmentation Combinig Color and Depth Sensors Via Logarithmic Opinion Pool Decision}, journal = {Journal of Visual Communication and Image Representation}, year = {2013}, month = {04/2013}, abstract = {

In this paper we present a novel foreground segmentation system that combines color and depth sensors information to perform a more complete Bayesian segmentation between foreground and background classes. The system shows a combination of spatial-color and spatial-depth region-based models for the foreground as well as color and depth pixel-wise models for the background in a Logarithmic Opinion Pool decision framework used to correctly combine the likelihoods of each model. A posterior enhancement step based on a trimap analysis is also proposed in order to correct the precision errors that the depth sensor introduces. The results presented in this paper show that our system is robust in front of color and depth camouflage problems between the foreground object and the background, and also improves the segmentation in the area of the objects\u2019 contours by reducing the false positive detections that appear due to the lack of precision of the depth sensors.

}, doi = {http://dx.doi.org/10.1016/j.jvcir.2013.03.019}, url = {http://www.sciencedirect.com/science/article/pii/S104732031300059X}, author = {Gallego, J. and M. Pard{\`a}s} } @mastersthesis {xTort13, title = {Video Clustering Using Camera Motion}, year = {2013}, abstract = {

This document contains the work done in INP Grenoble during the second semester of the academic year 2011-2012, completed in Barcelona during the firsts months of the 2012-2013. The work presented consists in a camera motion study in different types of video in order to group fragments that have some similarity in the content.\ 

In the document it is explained how the data extracted by the program Motion 2D, proportionated by the French university, are treated in order to represented them in a more simplified using motion histograms. It is also explained how the different distances between histograms are calculated and how its similarity is computed.\ 

Three different distances are used: Manhattan, Euclidean and Bhattacharyya, although in the project there can be found the explanation of some others a little bit more complicated. Different histogram configurations are used, using more or less bins to represent the motion.\ 

Every possible combination of the number of bins and distances are evaluated using a group of 30 fragments of video and the clustering algorithm K-Means. The clustering results are evaluated using F1-Score, a very popular measurement suitable for clustering algorithms and also classification.

}, url = {http://hdl.handle.net/2099.1/17337}, author = {Tort, Laura}, editor = {Xavier Gir{\'o}-i-Nieto and Rombaut, Mich{\`e}le and Pellerin, Denis} } @conference {cPalou12, title = {2.1 Depth Estimation of Frames in Image Sequences Using Motion Occlusions}, booktitle = {Computer Vision {\textendash} ECCV 2012. Workshops and Demonstrations}, year = {2012}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, isbn = {978-3-642-33884-7}, doi = {10.1007/978-3-642-33885-4_52}, url = {http://dx.doi.org/10.1007/978-3-642-33885-4_52}, author = {Palou, G. and Salembier, P.} } @conference {cPalou12b, title = {Depth Ordering on Image Sequences Using Motion Occlusions}, booktitle = {IEEE Int. Conf. in Image Processing, ICIP 2012}, year = {2012}, month = {09/2012}, address = {Orlando, Florida, USA}, author = {Palou, G. and Salembier, P.} } @article {aGallego12, title = {Enhanced foreground segmentation and tracking combining Bayesian background, shadow and foreground modeling}, journal = {Pattern Recognition Letters}, volume = {33}, year = {2012}, month = {09/2012}, pages = {1558{\textendash}1568}, type = {journal}, chapter = {1558}, abstract = {

In this paper we present a foreground segmentation and tracking system for monocular static camera sequences and indoor scenarios that achieves correct foreground detection also in those complicated scenes where similarity between foreground and background colours appears. The work flow of the system is based on three main steps: An initial foreground detection performs a simple segmentation via Gaussian pixel color modeling and shadows removal. Next, a tracking step uses the foreground segmentation for identifying the objects, and tracks them using a modified mean shift algorithm. At the end, an enhanced foreground segmentation step is formulated into a Bayesian framework. For this aim, foreground and shadow candidates are used to construct probabilistic foreground and shadow models. The Bayesian framework combines a pixel-wise color background model with spatial-color models for the foreground and shadows. The final classification is performed using the graph-cut algorithm. The tracking step allows a correct updating of the probabilistic models, achieving a foreground segmentation that reduces the false negative and false positive detections, and obtaining a robust segmentation and tracking of each object of the scene.

}, keywords = {Foreground segmentation, GMM, Objects tracking, Shadow model, Space-color models}, issn = {0167-8655}, doi = {10.1016/j.patrec.2012.05.004}, url = {http://www.sciencedirect.com/science/article/pii/S016786551200164X}, author = {Gallego, J. and M. Pard{\`a}s and Haro, G.} } @inbook {bGallego12, title = {Foreground objects segmentation for moving camera scenarios based on SCGMM}, booktitle = {Computational Intelligence for Multimedia Understanding}, volume = {7252}, number = {Lecture Notes in Computer Science}, year = {2012}, month = {09/2011}, pages = {195-206}, publisher = {Springer}, organization = {Springer}, address = {Berlin Heidelberg}, abstract = {

In this paper we present a new system for segmenting non-rigid objects in moving camera sequences for indoor and outdoor scenarios that achieves a correct object segmentation via global MAP-MRF framework formulation for the foreground and background classification task. Our proposal, suitable for video indexation applications, receives as an input an initial segmentation of the object to segment and it consists of two region-based parametric probabilistic models to model the spatial (x,y) and color (r,g,b) domains of the foreground and background classes. Both classes rival each other in modeling the regions that appear within a dynamic region of interest that includes the foreground object to segment and also, the background regions that surrounds the object. The results presented in the paper show the correctness of the object segmentation, reducing false positive and false negative detections originated by the new background regions that appear near the region of the object.

}, keywords = {moving camera sequences, Object segmentation, SCGMM, video indexation}, issn = {978-3-642-32435-2}, doi = {10.1007/978-3-642-32436-9_17}, url = {http://www.springerlink.com/content/r6u266562h586476/}, author = {Gallego, J. and M. Pard{\`a}s and Solano, M.} } @conference {cPalou12a, title = {From local occlusion cues to global depth estimation}, booktitle = {IEEE Int. Conf. on Acoustics Speech and Signal Processing, ICASSP 2012}, year = {2012}, month = {03/2012}, address = {Kyoto, Japan}, author = {Palou, G. and Salembier, P.} } @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} } @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} } @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 {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.} } @conference {cLopez-Mendez11, title = {Approximate partitioning of observations in hierarchical particle filter body tracking}, booktitle = {2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}, year = {2011}, pages = {19{\textendash}24}, abstract = {

This paper presents a model-based hierarchical particle filtering algorithm to estimate the pose and anthropometric parameters of humans in multi-view environments. Our method incorporates a novel likelihood measurement approach consisting of an approximate partitioning of observations. Provided that a partitioning of the human body model has been defined and associates body parts to state space variables, the proposed method estimates image regions that are relevant to that body part and thus to the state space variables of interest. The proposed regions are bounding boxes and consequently can be efficiently processed in a GPU. The algorithm is tested in a challenging dataset involving people playing tennis (TennisSense) and also in the well-known HumanEva dataset. The obtained results show the effectiveness of the proposed method.

}, isbn = {978-1-4577-0529-8}, doi = {10.1109/CVPRW.2011.5981712}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5981712}, author = {L{\'o}pez-M{\'e}ndez, A. and Alcoverro, M. and M. Pard{\`a}s and Casas, J.} } @conference {cAlcoverro11a, title = {Connected Operators on 3D data for human body analysis}, booktitle = {2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}, year = {2011}, pages = {9{\textendash}14}, abstract = {

This paper presents a novel method for filtering and extraction of human body features from 3D data, either from multi-view images or range sensors. The proposed algorithm consists in processing the geodesic distances on a 3D surface representing the human body in order to find prominent maxima representing salient points of the human body. We introduce a 3D surface graph representation and filtering strategies to enhance robustness to noise and artifacts present in this kind of data. We conduct several experiments on different datasets involving 2 multi-view setups and 2 range data sensors: Kinect and Mesa SR4000. In all of them, the proposed algorithm shows a promising performance towards human body analysis with 3D data.

}, isbn = {978-1-4577-0529-8}, doi = {10.1109/CVPRW.2011.5981772}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5981772}, author = {Alcoverro, M. and L{\'o}pez-M{\'e}ndez, A. and M. Pard{\`a}s and Casas, J.} } @article {aCanton-Ferrer11, title = {Human motion capture using scalable body models}, journal = {Computer vision and image understanding}, volume = {115}, number = {10}, year = {2011}, pages = {1363{\textendash}1374}, abstract = {

This paper presents a general analysis framework towards exploiting the underlying hierarchical and scalable structure of an articulated object for pose estimation and tracking. Scalable human body models are introduced as an ordered set of articulated models fulfilling an inclusive hierarchy. The concept of annealing is applied to derive a generic particle filtering scheme able to perform a sequential filtering over the set of models contained in the scalable human body model. Two annealing loops are employed, the standard likelihood annealing and the newly introduced structural annealing, leading to a robust, progressive and efficient analysis of the input data. The validity of this scheme is tested by performing markerless human motion capture in a multi-camera environment employing the standard HumanEva annotated datasets. Finally, quantitative results are presented and compared with other existing HMC techniques.

}, issn = {1077-3142}, doi = {10.1016/j.cviu.2011.06.001}, url = {http://hdl.handle.net/2117/13393}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @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} } @conference {cGallego11, title = {Joint multi-view foreground segmentation and 3D reconstruction with tolerance loop}, booktitle = {IEEE International Conference on Image Processing}, year = {2011}, pages = {997{\textendash}1000}, abstract = {

In this paper we present a novel foreground segmentation and 3D reconstruction system for multi-view scenarios. The system achieves correct 3D object reconstruction even when foreground segmentation presents critical misses in some of the views. We introduce the spatial redundancy of the multi-view data into the foreground segmentation process by combining segmentation and the 3D reconstruction in a two steps workflow. First, the segmentation of the objects in each view uses a monocular, region-based foreground segmentation in a MAP-MRF framework for foreground, background and shadow classes. Next, we compute an iterative volume reconstruction in a 3D tolerance loop, obtaining an iteratively enhanced SfS volume. Foreground segmentation is improved by updating the foreground model of each view at each iteration. The results presented in this paper show the improved foreground segmentation and the reduction of errors in the reconstruction of the volume.

}, isbn = {978-1-4577-1304-0}, doi = {10.1109/ICIP.2011.6116731}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6116731}, author = {Gallego, J. and Salvador, J. and Casas, J. and M. Pard{\`a}s} } @article {dPalou11, title = {Monocular Depth Ordering Using Occlusion Cues}, year = {2011}, institution = {Technical University of Catalonia}, type = {masters}, address = {Barcelona}, author = {Palou, G. and Salembier, P.} } @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.} } @conference {cPalou11, title = {Occlusion-based depth ordering on monocular images with binary partition tree}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2011}, year = {2011}, pages = {1093{\textendash}1096}, address = {Prague, Czech Republic}, doi = {0.1109/ICASSP.2011.5946598}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5946598\&tag=1}, author = {Palou, G. and Salembier, P.} } @conference {cAlcoverro11, title = {A real-time body tracking system for smart rooms}, booktitle = {ICME - 2011 IEEE International Conference on Multimedia and Expo}, year = {2011}, pages = {1{\textendash}6}, abstract = {

We present a real-time human body tracking system for a single user in a Smart Room scenario. In this paper we propose a novel system that involves a silhouette-based cost function using variable windows, a hierarchical optimization method, parallel implementations of pixel-based algorithms and efficient usage of a low-cost hardware structure. Results in a Smart Room setup are presented.

}, isbn = {978-1-61284-349-0}, doi = {10.1109/ICME.2011.6011847}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6011847}, author = {Alcoverro, M. and L{\'o}pez-M{\'e}ndez, A. and Casas, J. and M. Pard{\`a}s} } @conference {cLopez-Mendez11a, title = {Real-time upper body tracking with online initialization using a range sensor}, booktitle = {2011 IEEE International Conference on Computer VIsion Workshops (ICCV Workshops)}, year = {2011}, pages = {391{\textendash}398}, abstract = {

We present a novel method for upper body pose estimation with online initialization of pose and the anthropometric profile. Our method is based on a Hierarchical Particle Filter that defines its likelihood function with a single view depth map provided by a range sensor. We use Connected Operators on range data to detect hand and head candidates that are used to enrich the Particle Filter{\textquoteright}s proposal distribution, but also to perform an automated initialization of the pose and the anthropometric profile estimation. A GPU based implementation of the likelihood evaluation yields real-time performance. Experimental validation of the proposed algorithm and the real-time implementation are provided, as well as a comparison with the recently released OpenNI tracker for the Kinect sensor.

}, isbn = {978-1-4673-0063-6/11}, doi = {10.1109/ICCVW.2011.6130268}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6130268}, author = {L{\'o}pez-M{\'e}ndez, A. and Alcoverro, M. and M. Pard{\`a}s and Casas, J.} } @conference {cPardas11, title = {Work in progress - Cooperative and competitive projects for engaging students in advanced ICT subjects}, booktitle = {41st Annual Frontiers in Education Conference}, year = {2011}, pages = {1{\textendash}3}, isbn = {3-540-76258-2}, doi = {10.1109/FIE.2011.6143032}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6143032\&tag=1}, author = {M. Pard{\`a}s and Bonafonte, A.} } @conference {cHaro10, title = {3D shape from multi-camera views by error projection minimization}, booktitle = {10th Workshop on Image Analysis for Multimedia Interactive Services}, year = {2010}, pages = {250{\textendash}253}, isbn = {978-1-4244-3609-5}, doi = {10.1109/WIAMIS.2009.5031480}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5031480}, author = {Haro, G. and M. Pard{\`a}s} } @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.} } @conference {cGallego10, title = {Enhanced bayesian foreground segmentation using brightness and color distortion region-based model for shadow removal}, booktitle = {2010 IEEE International Conference on Image Processing}, year = {2010}, pages = {3449{\textendash}3452}, isbn = {1522-4880}, doi = {10.1109/ICIP.2010.5653897}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5653897\&tag=1}, author = {Gallego, J. and M. Pard{\`a}s} } @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} } @article {aGudmundsson10, title = {Improved 3D reconstruction in smart-room environments using ToF imaging}, journal = {Computer vision and image understanding}, volume = {114}, number = {12}, year = {2010}, month = {12/2010}, pages = {1376{\textendash}1384}, abstract = {

This paper presents a general analysis framework towards exploiting the underlying hierarchical and scalable structure of an articulated object for pose estimation and tracking. Scalable human body models are introduced as an ordered set of articulated models fulfilling an inclusive hierarchy. The concept of annealing is applied to derive a generic particle filtering scheme able to perform a sequential filtering over the set of models contained in the scalable human body model. Two annealing loops are employed, the standard likelihood annealing and the newly introduced structural annealing, leading to a robust, progressive and efficient analysis of the input data. The validity of this scheme is tested by performing markerless human motion capture in a multi-camera environment employing the standard HumanEva annotated datasets. Finally, quantitative results are presented and compared with other existing HMC techniques.

}, issn = {1077-3142}, doi = {10.1016/j.cviu.2010.07.011}, url = {http://www.sciencedirect.com/science/article/pii/S1077314210001736}, author = {Gudmundsson, S. and M. Pard{\`a}s and Casas, J. and Sveinsson, J. and Aanaes, H. and Larsen, R.} } @article {aCanton-Ferrer10, title = {Marker-based human motion capture in multi-view sequences}, journal = {Eurasip journal on advances in signal processing}, volume = {2010}, number = {Article ID 105476}, year = {2010}, pages = {1{\textendash}11}, abstract = {

This paper presents a low-cost real-time alternative to available commercial human motion capture systems. First, a set of distinguishable markers are placed on several human body landmarks, and the scene is captured by a number of calibrated and synchronized cameras. In order to establish a physical relation among markers, a human body model is defined. Markers are detected on all camera views and delivered as the input of an annealed particle filter scheme where every particle encodes an instance of the pose of the body model to be estimated. Likelihood between particles and input data is performed through the robust generalized symmetric epipolar distance and kinematic constrains are enforced in the propagation step towards avoiding impossible poses. Tests over the HumanEva annotated data set yield quantitative results showing the effectiveness of the proposed algorithm. Results over sequences involving fast and complex motions are also presented.

}, keywords = {CHIL, PROVEC}, issn = {1687-6172}, doi = {10.1155/2010/105476}, url = {http://www.hindawi.com/journals/asp/2010/105476.html}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @article {cSayrol10, title = {Mixed integration of CDIO skills into telecommunication engineering curricula}, journal = {Elektronika ir elektrotechnika}, volume = {102}, number = {102}, year = {2010}, pages = {127{\textendash}130}, issn = {1392-1215}, url = {http://www.ktu.lt/lt/mokslas/zurnalai/elektros_z/z102/32__ISSN_1392-1215_Mixed\%20Integration\%20of\%20CDIO\%20skills\%20into\%20Telecommunication\%20Engineering\%20Curricula.pdf}, author = {Elisa Sayrol and Bragos, R. and Alarc{\'o}n, E. and Cabrera, M. and Calveras, A. and Comellas, J. and O{\textquoteright}Callaghan, J. and Pegueroles, J. and Pla, E. and Prat, L. and Saez, G. and Sarda, J. and Tallon, C.} } @article {aGudmundsson10a, title = {Model-based hand gesture tracking in ToF image sequences}, journal = {Lecture notes in computer science}, volume = {6169/2010}, year = {2010}, pages = {118{\textendash}127}, issn = {0302-9743}, doi = {10.1007/978-3-642-14061-7_12}, url = {http://www.springerlink.com/content/l1117675tv565twq/}, author = {Gudmundsson, S. and Sveinsson, J. and M. Pard{\`a}s and Aanaes, H. and Larsen, R.} } @conference {cBragos10, title = {Proceso de inversi{\'o}n de competencias gen{\'e}ricas en los nuevos planes de estudios de grado de la ETSETB de acuerdo con el modelo CDIO}, booktitle = {IX Congreso de Tecnolog{\'\i}as Aplicadas a la Ense{\~n}anza de la Electr{\'o}nica}, year = {2010}, pages = {1{\textendash}9}, isbn = {978-84-96737-68-6}, url = {http://upcommons.upc.edu/e-prints/urlFiles?idDrac=4451103}, author = {Bragos, R. and Alarc{\'o}n, E. and Cabrera, M. and Calveras, A. and Comellas, J. and O{\textquoteright}Callaghan, J. and Pegueroles, J. and Prat, L. and Saez, G. and Sarda, J. and Elisa Sayrol} } @conference {cCanton-Ferrer10a, title = {Real-time 3D multi-person tracking using Monte Carlo surface sampling}, booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}, year = {2010}, pages = {40{\textendash}46}, isbn = {978-1-4244-7030-3/10}, doi = {10.1109/CVPRW.2010.5543734}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5543734}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @article {aHaro10, title = {Shape from incomplete silhouettes based on the reprojection error}, journal = {Image and vision computing}, volume = {28}, number = {9}, year = {2010}, pages = {1354{\textendash}1368}, issn = {0262-8856}, doi = {10.1016/j.imavis.2010.01.016}, url = {linkinghub.elsevier.com/retrieve/pii/S0262885610000326}, author = {Haro, G. and M. Pard{\`a}s} } @conference {cAlcoverro10, title = {Skeleton and shape adjustment and tracking in multicamera environments}, booktitle = {6th International Conference AMDO 2010}, year = {2010}, pages = {88{\textendash}97}, isbn = {978-3-642-14060-0}, doi = {10.1007/978-3-642-14061-7_9}, author = {Alcoverro, M. and Casas, J. and M. Pard{\`a}s} } @inbook {bAlcoverro10, title = {Skeleton and shape adjustment and tracking in multicamera environments}, booktitle = {Lecture notes in computer science}, volume = {6169/2010}, year = {2010}, pages = {88{\textendash}97}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

In this paper we present a method for automatic body model adjustment and motion tracking in multicamera environments. We introduce a set of shape deformation parameters based on linear blend skinning, that allow a deformation related to the scaling of the distinct bones of the body model skeleton, and a deformation in the radial direction of a bone. The adjustment of a generic body model to a specific subject is achieved by the estimation of those shape deformation parameters. This estimation combines a local optimization method and hierarchical particle filtering, and uses an efficient cost function based on foreground silhouettes using GPU. This estimation takes into account anthropometric constraints by using a rejection sampling method of propagation of particles. We propose a hierarchical particle filtering method for motion tracking using the adjusted model. We show accurate model adjustment and tracking for distinct subjects in a 5 cameras set up

}, isbn = {978-3-642-14060-0}, issn = {0302-9743}, doi = {10.1007/978-3-642-14061-7_9}, url = {http://www.springerlink.com/content/9662h2hwq81041g2/fulltext.pdf}, author = {Alcoverro, M. and Casas, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer10, title = {Spatio-temporal alignment and hyperspherical radon transform for 3D gait recognition in multi-view environments}, booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}, year = {2010}, pages = {116{\textendash}121}, isbn = {978-1-4244-7030-3/10}, doi = {10.1109/CVPRW.2010.5544615}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5544615}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @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.} } @inbook {bNickel09, title = {Activity Classification}, booktitle = {Computers in the Human Interaction Loop}, year = {2009}, pages = {107{\textendash}119}, publisher = {Springer}, organization = {Springer}, address = {London}, abstract = {

When a person enters a room, he or she immediately develops a mental concept about {\textquotedblleft}what is going on{\textquotedblright} in the room; for example, people may be working in the room, people may be engaged in a conversation, or the room may be empty. The CHIL services depend on just the same kind of semantic description, which is termed activity in the following. The {\textquotedblleft}Connector{\textquotedblright} or the {\textquotedblleft}Memory Jog{\textquotedblright}, for example, could provide support that is appropriate for the given context if it knew about the current activity at the user{\textquoteright}s place. This kind of higher-level understanding of human interaction processes could then be used, e.g., for rating the user{\textquoteright}s current availability in a certain situation.

}, isbn = {978-1-84882-053-1}, doi = {10.1007/978-1-84882-054-8}, url = {http://www.springerlink.com/content/tj2x827107563616/}, author = {Nickel, K. and M. Pard{\`a}s and Stiefelhagen, R. and Cristian Canton-Ferrer and Landabaso, J. and Casas, J.} } @conference {cGallego09, title = {Bayesian foreground segmentation and tracking using pixel-wise background model and region-based foreground model}, booktitle = {16th IEEE International Conference on Image Processing}, year = {2009}, pages = {3205{\textendash}3208}, doi = {10.1109/ICIP.2009.5414380}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5414380}, author = {Gallego, J. and M. Pard{\`a}s and Haro, G.} } @phdthesis {dCanton-Ferrer09, title = {Human Motion Capture with Scalable Body Models}, year = {2009}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

Capturing and tracking human motion is becoming a hot research topic due to the number of applications that can be addressed using this information, ranging from action recognition, human-computer interfaces and biometrics. This PhD thesis addresses the problem of extracting the pose parameters of a human body in a multi-camera environment relying on Monte Carlo techniques.

Extracing the describing parameters (pose) of an articulated model of the human body from information provided by multiple cameras can be efficiently tackled using the standard Bayesian prediction and update formulation. However, due to the high dimensionality of the pose space, standard techniques based on linear and Gaussian assumptions are not suitable. Instead, Monte Carlo methods based on a sampled representation of the involved likelihood functions yield to a promising research direction. In this thesis, we present a number of contributions to this topic based on a coarse-to-fine analysis scheme. The input data to all presented algorithms will be a 3D reconstruction of the scene, described by colored voxels, thus combining the information provided by all camera views into a unified data representation.

In a first stage, subjects are coarsely approximated by an ellipsoid and their centroids are estimated and tracked. A novel approach achieving real-time performance is presented based a surface sampling of the objects in the scene: the Sparse Sampling algorithm. In this filtering scheme, an independent tracker is assigned to every target and an exclusion mechanism is defined to avoid interference among targets. Finally, the obtained centroid positions are employed afterwards to initialize a specific pose estimation algorithm.

Two pose estimation algorithms are presented based on the seminal principle of the annealead particle filter technique. The first one is a low cost approach to marker-based human motion capture and, the second, is a markerless technique relying on likelihood functions computed directly on the 3D voxel representation. In both approaches, kinematic constrains are employed to avoid unfeasible poses. Although these algorithms provide satisfactory results when dealing with accurate input data, they tend to loose track when processing noisy measurements and occluded body parts.\ 

Scalability of the structure of the human body is exploited to define two robust alternatives to analyze faulty data. In the first case, the Scalable Human Body Model-Annealed Particle Filter, is presented as filtering approach adding an extra annealing level to the classical annealed particle filter approach: the body hierarchy annealing loop. In this way, a progressive fitting is performed in a coarse-to-fine manner thus yielding to both more efficient and accurate results. Another alternative is presented employing a human body model hierarchy where different limbs are added progressively to the model. This allows detecting those parts that are occluded (for instance, by furniture) and disregard them into the likelihood evaluation step of the filtering scheme.

Finally, in order to evaluate all the systems proposed in this thesis, a new methodology is presented. Existing methods based on computing the mean and variance of the committed estimation error tend to produce biased figures when a subset of the human body is not tracked properly. We proposed two alternative metrics that avoids this situations and therefore allow a fairer comparison among algorithms.

}, author = {Cristian Canton-Ferrer}, editor = {Casas, J. and M. Pard{\`a}s} } @inbook {bCanton-Ferrer09, title = {Image and video processing tools for HCI}, booktitle = {Multimodal signal processing: theory and applications for human-computer interaction}, year = {2009}, pages = {93{\textendash}118}, isbn = {9780123748256}, author = {Cristian Canton-Ferrer and M. Pard{\`a}s and Ver{\'o}nica Vilaplana} } @inbook {bDimakis09, title = {The Memory Jog Service}, booktitle = {Computers in the Human Interaction Loop}, year = {2009}, pages = {207{\textendash}234}, publisher = {Springer}, organization = {Springer}, address = {London}, abstract = {

The CHIL Memory Jog service focuses on facilitating the collaboration of participants in meetings, lectures, presentations, and other human interactive events, occurring in indoor CHIL spaces. It exploits the whole set of the perceptual components that have been developed by the CHIL Consortium partners (e.g., person tracking, face identification, audio source localization, etc) along with a wide range of actuating devices such as projectors, displays, targeted audio devices, speakers, etc.

}, isbn = {978-1-84882-053-1}, doi = {10.1007/978-1-84882-054-8}, url = {http://www.springerlink.com/content/tg220751v5m1h356/}, author = {Dimakis, N. and Soldatos, J. and Polymenakos, L. and Sturm, J. and Neumann, J. and Casas, J.} } @inbook {bBernardin09, title = {Person Tracking}, booktitle = {Computers in the human interaction loop}, year = {2009}, pages = {11{\textendash}22}, publisher = {Springer}, organization = {Springer}, address = {London}, abstract = {

One of the most basic building blocks for the understanding of human actions and interactions is the accurate detection and tracking of persons in a scene. In constrained scenarios involving at most one subject, or in situations where persons can be confined to a controlled monitoring space or required to wear markers, sensors, or microphones, these tasks can be solved with relative ease. However, when accurate localization and tracking have to be performed in an unobtrusive or discreet fashion, using only distantly placed microphones and cameras, in a variety of natural and uncontrolled scenarios, the challenges posed are much greater. The problems faced by video analysis are those of poor or uneven illumination, low resolution, clutter or occlusion, unclean backgrounds, and multiple moving and uncooperative users that are not always easily distinguishable.

}, isbn = {978-1-84882-053-1}, doi = {10.1007/978-1-84882-054-8}, url = {http://www.springerlink.com/content/tj2x827107563616/}, author = {Bernardin, K. and Stiefelhagen, R. and Pnevmatikakis, A. and Lanz, O. and Brutti, A. and Casas, J. and Potamianos, G.} } @conference {cCanton-Ferrer09a, title = {Towards a low cost multi-camera marker based human motion capture system}, booktitle = {16th International Conference on Image Processing}, year = {2009}, pages = {2581{\textendash}2584}, isbn = {978-1-4244-5655-6}, doi = {10.1109/ICIP.2009.5413915}, url = {http://dx.doi.org/10.1109/ICIP.2009.5413915}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @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.} } @conference {cAlcoverro09, title = {Visual hull reconstruction algorithms comparison: towards robustness to silhouette errors}, booktitle = {International Conference on Computer Vision Theory and Applications 2009}, year = {2009}, pages = {464{\textendash}469}, isbn = {978-989-8111-69-2}, url = {http://upcommons.upc.edu/e-prints/urlFiles?idDrac=2642455}, author = {Alcoverro, M. and M. Pard{\`a}s} } @conference {cCanton-Ferrer09b, title = {Voxel based annealed particle filtering for markerless 3D articulated motion capture}, booktitle = {3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video, 2009}, year = {2009}, pages = {1{\textendash}4}, doi = {10.1109/3DTV.2009.5069645}, url = {http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5069609\&isYear=2009}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @article {aCanton-Ferrer08, title = {Audiovisual head orientation estimation with particle filtering in multisensor scenarios}, journal = {EURASIP Journal on Advances in Signal Processing}, volume = {2008}, year = {2008}, month = {06/2007}, pages = {1{\textendash}13}, chapter = {276846}, abstract = {

This article presents a multimodal approach to head pose estimation of individuals in environments equipped with multiple cameras and microphones, such as SmartRooms or automatic video conferencing. Determining the individuals head orientation is the basis for many forms of more sophisticated interactions between humans and technical devices and can also be used for automatic sensor selection (camera, microphone) in communications or video surveillance systems. The use of particle filters as a unified framework for the estimation of the head orientation for both monomodal and multimodal cases is proposed. In video, we estimate head orientation from color information by exploiting spatial redundancy among cameras. Audio information is processed to estimate the direction of the voice produced by a speaker making use of the directivity characteristics of the head radiation pattern. Furthermore, two different particle filter multimodal information fusion schemes for combining the audio and video streams are analyzed in terms of accuracy and robustness. In the first one, fusion is performed at a decision level by combining each monomodal head pose estimation, while the second one uses a joint estimation system combining information at data level. Experimental results conducted over the CLEAR 2006 evaluation database are reported and the comparison of the proposed multimodal head pose estimation algorithms with the reference monomodal approaches proves the effectiveness of the proposed approach.

}, issn = {1687-6172}, doi = {10.1155/2008/276846}, url = {http://asp.eurasipjournals.com/content/2008/1/276846}, author = {Cristian Canton-Ferrer and Segura, C. and Casas, J. and M. Pard{\`a}s and Hernando, J.} } @article {aTurkan08, title = {Edge Projections for Eye Localization}, journal = {Optical engineering}, volume = {47}, number = {4}, year = {2008}, pages = {1{\textendash}6}, issn = {0091-3286}, doi = {10.1117/1.2902437}, url = {http://spiedigitallibrary.org/oe/resource/1/opegar/v47/i4/p047007_s1}, author = {Turkan, M. and M. Pard{\`a}s and Cetin, E.} } @conference {cCanton-Ferrer08, title = {Exploiting Structural Hierarchy in Articulated Objects Towards Robust Motion Capture}, booktitle = {V Conference on Articulated Motion and Deformable Objects (AMDO)}, year = {2008}, pages = {82{\textendash}91}, isbn = {0302-9743}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @inbook {bCanton-Ferrer08a, title = {Exploiting Structural Hierarchy in Articulated Objects Towards Robust Motion Capture}, booktitle = {Articulated Motion and Deformable Objects}, volume = {5098}, year = {2008}, pages = {82{\textendash}91}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

This paper presents a general analysis framework towards exploiting the underlying hierarchical and scalable structure of an articulated object for pose estimation and tracking. The Scalable Human Body Model (SHBM) is presented as a set of human body models ordered following a hierarchy criteria. The concept of annealing is applied to derive a generic particle filtering scheme able to perform a sequential filtering over the models contained in the SHBM leading to a structural annealing process. This scheme is applied to perform human motion capture in a multi-camera environment. Finally, the effectiveness of the proposed system is addressed by comparing its performance with the standard and annealed particle filtering approaches over an annotated database.

}, isbn = {978-3-540-70516-1}, doi = {10.1007/978-3-540-70517-8_9}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @inbook {bCanton-Ferrer08, title = {Head Orientation Estimation Using Particle Filtering in Multiview Scenarios}, booktitle = {Multimodal Technologies for Perception of Humans}, volume = {4625}, year = {2008}, pages = {317{\textendash}327}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

This paper presents a novel approach to the problem of determining head pose estimation and face 3D orientation of several people in low resolution sequences from multiple calibrated cameras. Spatial redundancy is exploited and the head in the scene is approximated by an ellipsoid. Skin patches from each detected head are located in each camera view. Data fusion is performed by back-projecting skin patches from single images onto the estimated 3D head model, thus providing a synthetic reconstruction of the head appearance. A particle filter is employed to perform the estimation of the head pan angle of the person under study. A likelihood function based on the face appearance is introduced. Experimental results proving the effectiveness of the proposed algorithm are provided for the SmartRoom scenario of the CLEAR Evaluation 2007 Head Orientation dataset.

}, isbn = {978-3-540-68584-5}, doi = {10.1007/978-3-540-68585-2_30}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @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 {cCanton-Ferrer08b, title = {Multimodal Real-Time Focus of Attention Estimation in SmartRooms}, booktitle = {CVPR 2008 Workshop on Human Communicative Behavior Analysis}, year = {2008}, pages = {1{\textendash}4}, isbn = {978-1-4244-2340}, author = {Cristian Canton-Ferrer and Segura, C. and M. Pard{\`a}s and Casas, J. and Hernando, J.} } @inbook {bCanton-Ferrer08b, title = {Multi-Person Tracking Strategies Based on Voxel Analysis}, booktitle = {Multimodal Technologies for Perception of Humans}, volume = {4625}, year = {2008}, pages = {91{\textendash}103}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

This paper presents two approaches to the problem of simultaneous tracking of several people in low resolution sequences from multiple calibrated cameras. Spatial redundancy is exploited to generate a discrete 3D binary representation of the foreground objects in the scene. Color information obtained from a zenithal camera view is added to this 3D information. The first tracking approach implements heuristic association rules between blobs labelled according to spatiotemporal connectivity criteria. Association rules are based on a cost function which considers their placement and color histogram. In the second approach, a particle filtering scheme adapted to the incoming 3D discrete data is proposed. A volume likelihood function and a discrete 3D re-sampling procedure are introduced to evaluate and drive particles. Multiple targets are tracked by means of multiple particle filters and interaction among them is modeled through a 3D blocking scheme. Evaluation over the CLEAR 2007 database yields quantitative results assessing the performance of the proposed algorithm for indoor scenarios.

}, isbn = {978-3-540-68584-5}, doi = {10.1007/978-3-540-68585-2_7}, url = {http://www.springerlink.com/content/k08w4000844220gr/}, author = {Cristian Canton-Ferrer and Salvador, J. and Casas, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer08a, title = {Particle Filtering and Sparse Sampling for Multi-Person 3D Tracking}, booktitle = {IEEE International Conference on Image Processing}, year = {2008}, pages = {2644{\textendash}2647}, isbn = {1-4244-1764-3}, doi = {10.1109/ICIP.2008.4712337}, author = {Cristian Canton-Ferrer and Sblendido, R. and Casas, J. and M. Pard{\`a}s} } @conference {cGallego08, title = {Segmentation and Tracking of Static and Moving Objects in Video Surveillance Scenarios}, booktitle = {IEEE International Conference on Image Processing}, year = {2008}, pages = {2716{\textendash}2719}, isbn = {1-4244-1764-3}, author = {Gallego, J. and M. Pard{\`a}s} } @article {aLandabaso08a, title = {Shape from inconsistent silhouette}, journal = {Computer vision and image understanding}, volume = {112}, number = {2}, year = {2008}, month = {11/2008}, pages = {210{\textendash}224}, abstract = {

Shape from silhouette (SfS) is the general term used to refer to the techniques that obtain a volume estimate from a set of binary images. In a first step, a number of images are taken from different positions around the scene of interest. Later, each image is segmented to produce binary masks, also called silhouettes, to delimit the objects of interest. Finally, the volume estimate is obtained as the maximal one which yields the silhouettes. The set of silhouettes is usually considered to be consistent which means that there exists at least one volume which completely explains them. However, silhouettes are normally inconsistent due to inaccurate calibration or erroneous silhouette extraction techniques. In spite of that, SfS techniques reconstruct only that part of the volume which projects consistently in all the silhouettes, leaving the rest unreconstructed. In this paper, we extend the idea of SfS to be used with sets of inconsistent silhouettes. We propose a fast technique for estimating that part of the volume which projects inconsistently and propose a criteria for classifying it by minimizing the probability of miss-classification taking into account the 2D error detection probabilities of the silhouettes. A number of theoretical and empirical results are given, showing that the proposed method reduces the reconstruction error.

}, issn = {1077-3142}, doi = {10.1016/j.cviu.2008.02.006}, author = {Landabaso, J. and M. Pard{\`a}s and Casas, J.} } @conference {cLandabaso08, title = {Shape from Inconsistent Silhouette for Free Viewpoint Video}, booktitle = {IEEE International Conference on Image Processing}, year = {2008}, pages = {213{\textendash}216}, isbn = {1-4244-1764-3}, author = {Landabaso, J. and Lizcano, L. and M. Pard{\`a}s} } @conference {cGudmundsson08, title = {TOF Imaging in Smart Room Environments towards Improved People Tracking}, booktitle = {Computer Vision and Pattern Recognition: Workshop on Time of Flight based Computer Vision (CVPR/TOF-CV)}, year = {2008}, pages = {1{\textendash}6}, author = {Gudmundsson, S. and M. Pard{\`a}s and Casas, J. and Aanaes, H. and Larsen, R.} } @article {aLandabaso08, title = {A Unified Framework for Consistent 2D/3D Foreground Object Detection}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {18}, number = {8}, year = {2008}, pages = {1040{\textendash}1051}, issn = {1051-8215}, author = {Landabaso, J. and M. Pard{\`a}s} } @phdthesis {dLandabaso08, title = {A Unified Framework for Consistent 2D/3D Foreground Object Detection}, year = {2008}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, author = {Landabaso, J.}, editor = {M. Pard{\`a}s} } @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 {cAsteriadis07, title = {Detection of facial characteristics based on edge information}, booktitle = {International Conference on Computer Vision Theory and Applications, VISAPP 2007}, year = {2007}, pages = {247{\textendash}250}, isbn = {978-972-8865-74-0}, author = {Asteriadis, S. and Nikolaidis, N. and Pitas, I. and M. Pard{\`a}s} } @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 {cPetras07, title = {Flexible test-bed for unusual behavior detection}, booktitle = {6th ACM International Conference on Image and Video Retrieval}, year = {2007}, pages = {105{\textendash}108}, isbn = {0 646 34170 7}, author = {Petras, I. and Beleznai, C. and Dedeoglu, Y. and M. Pard{\`a}s and Kov{\'a}cs, L. and Szl{\'a}vik, Z. and Havasi, L. and Szir{\'a}nyi, T. and T{\"o}reyin, B. and G{\"u}d{\"u}kbay, U. and Cetin, E. and Cristian Canton-Ferrer} } @conference {cCanton-Ferrer07a, title = {Head Orientation Estimation using Particle Filtering in Multiview Scenarios}, booktitle = {CLEAR{\textquoteright}07 Second International Evaluation Workshop on Classification of Events, Activities and Relationships}, year = {2007}, pages = {1{\textendash}11}, isbn = {978-3-540-68584-5}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @inbook {bCanton-Ferrer07, title = {Head Pose Detection based on Fusion of Multiple Viewpoint Information}, booktitle = {Multimodal Technologies for Perception of Humans}, volume = {4122}, year = {2007}, pages = {305{\textendash}310}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

This paper presents a novel approach to the problem of determining head pose estimation and face 3D orientation of several people in low resolution sequences from multiple calibrated cameras. Spatial redundancy is exploited and the head in the scene is detected and geometrically approximated by an ellipsoid. Skin patches from each detected head are located in each camera view. Data fusion is performed by back-projecting skin patches from single images onto the estimated 3D head model, thus providing a synthetic reconstruction of the head appearance. Finally, these data are processed in a pattern analysis framework thus giving an estimation of face orientation. Tracking over time is performed by Kalman filtering. Results of the proposed algorithm are provided in the SmartRoom scenario of the CLEAR Evaluation.

}, isbn = {978-3-540-69567-7}, doi = {10.1007/978-3-540-69568-4_28}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @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 {cTurkan07, title = {Human eye localization using edge projections}, booktitle = {International Conference on Computer Vision Theory and Applications, VISAPP 2007}, year = {2007}, pages = {410{\textendash}415}, isbn = {-}, author = {Turkan, M. and M. Pard{\`a}s and Cetin, E.} } @conference {cCanton-Ferrer07, title = {Multi-Person Tracking Strategies Based on Voxel Analysis}, booktitle = {CLEAR{\textquoteright}07 Second International Evaluation Workshop on Classification of Events, Activities and Relationships}, year = {2007}, pages = {1{\textendash}12}, isbn = {978-3-540-68584-5}, author = {Cristian Canton-Ferrer and Salvador, J. and Casas, J. and M. Pard{\`a}s} } @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.} } @conference {cCanton-Ferrer06, title = {3D Human Action Recognition In Multiple View Scenarios}, booktitle = {2ones Jornades UPC de Investigaci{\'o}n en Autom{\'a}tica, Visi{\'o}n y Rob{\'o}tica}, year = {2006}, pages = {1{\textendash}5}, isbn = {84-7653-885-5}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s and Sargin, M. and Tekalp, M.} } @conference {cLandabaso06, title = {Cooperative background modelling using multiple cameras towards human detection in smart-room}, booktitle = {14th European Signal Processing Conference}, year = {2006}, pages = {1{\textendash}5}, author = {Landabaso, J. and M. Pard{\`a}s} } @article {aLandabaso06, title = {Foreground Regions Extraction and Characterization Towards Real-Time Object Tracking}, journal = {Lecture notes in computer science}, volume = {3869}, year = {2006}, pages = {241{\textendash}249}, issn = {0302-9743}, url = {http://www.springerlink.com}, author = {Landabaso, J. and M. Pard{\`a}s} } @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 {cCanton-Ferrer06b, title = {Head Pose Detection based on Fusion of Multiple Viewpoint Information}, booktitle = {CLEAR{\textquoteright}06 Evaluation Campaign and Workshop - Classification of Events, Activities and Relationships}, year = {2006}, pages = {305{\textendash}310}, isbn = {978-3-540-69567-7}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer06a, title = {Human Model and Motion Based 3D Action Recognition in Multiple View Scenarios}, booktitle = {14th European Signal Processing Conference}, year = {2006}, pages = {1{\textendash}1}, abstract = {

This paper presents a novel view-independent approach to the recognition of human gestures of several people in low resolution sequences from multiple calibrated cameras. In contraposition with other multi-ocular gesture recognition systems based on generating a classifi cation on a fusion of features coming from di fferent views, our system performs a data fusion (3D representation of the scene) and then a feature extraction and classifi cation. Motion descriptors introduced by Bobick et al. for 2D data are extended to 3D and a set of features based on 3D invariant statistical moments are computed. A simple ellipsoid body model is fit to incoming 3D data to capture in which body part the gesture occurs thus increasing the recognition ratio of the overall system and generating a more informative classifi cation output. Finally, a Bayesian classifi er is employed to perform recognition over as mall set of actions. Results are provided showing the eff ectiveness of the proposed algorithm in a SmartRoom scenario.

}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @inbook {bCanton-Ferrer06, title = {Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding}, booktitle = {Machine Learning for Multimodal Interaction}, volume = {3869}, year = {2006}, pages = {250{\textendash}261}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

This paper presents a novel approach to the problem of estimating and tracking 3D locations of multiple targets in a scene using measurements gathered from multiple calibrated cameras. Estimation and tracking is jointly achieved by a newly conceived computational process, the Projective Kalman filter (PKF), allowing the problem to be treated in a single, unified framework. The projective nature of observed data and information redundancy among views is exploited by PKF in order to overcome occlusions and spatial ambiguity. To demonstrate the effectiveness of the proposed algorithm, the authors present tracking results of people in a SmartRoom scenario and compare these results with existing methods as well.

}, isbn = {978-3-540-32549-9}, doi = {10.1007/11677482_22}, author = {Cristian Canton-Ferrer and Casas, J. and Tekalp, M. and M. Pard{\`a}s} } @conference {cLandabaso06a, title = {Reconstruction of 3D shapes considering inconsistent 2D silhouettes}, booktitle = {International Conference on Image Processing}, year = {2006}, pages = {1{\textendash}4}, abstract = {

The visual hull is defined as the intersection of the visual cones formed by the back-projection of C 2D silhouettes into the 3D space. The set of 2D silhouettes is consistent if there exists at least one volume which exactly explains them. Shape from silhouette (SfS) is the general term used to refer to the techniques employed to obtain a volume from silhouettes, which are considered to be consistent. In this paper we extend the idea of SfS to be used with sets of inconsistent silhouettes resulting from inaccurate calibration and erroneous 2D silhouette extraction techniques. The method presented detects and corrects errors in the silhouettes based on the consistency principle, implying an unbiased treatment of false alarms and misses in 2D.

}, author = {Landabaso, J. and M. Pard{\`a}s and Casas, J.} } @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.} } @conference {cLandabaso05a, title = {Foreground Regions Extraction and Characterization Towards Real-Time Object Tracking}, booktitle = {2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms}, year = {2005}, pages = {241{\textendash}249}, author = {Landabaso, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer05a, title = {Fusion of multiple viewpoint information towards 3d face robust orientation detection}, booktitle = {IEEE International Conference on Image Processing}, year = {2005}, abstract = {

This paper presents a novel approach to the problem of determining head pose estimation and face 3D orientation of several people in low resolution sequences from multiple calibrated cameras. Spatial redundancy is exploited and the heads of people in the scene are detected and geometrically approximated by an ellipsoid using a voxel reconstruction and a moment analysis method. Skin patches from each detected head are located in each camera view. Data fusion is performed by back-projecting skin patches from single images onto the estimated 3D head model, thus providing a synthetic reconstruction of the head appearance. Finally, these data are processed in a pattern analysis framework thus giving a reliable and robust estimation of face orientation. Tracking over time is performed by Kalman filtering. Results are provided showing the effectiveness of the proposed algorithm in a SmartRoom scenario.

}, isbn = {0-7803-9135-7}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cLandabaso05, title = {Hierarchical Representation of Scenes using Activity Information}, booktitle = {2005 IEEE International Conference on Acoustics, Speech, and Signal Processing}, year = {2005}, pages = {677{\textendash}680}, isbn = {0780388755}, author = {Landabaso, J. and M. Pard{\`a}s} } @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.} } @inbook {bSoriano05, title = {Multimedia Copyright Protection Platform Demonstrator}, booktitle = {Lecture notes in computer science}, volume = {3477}, year = {2005}, pages = {76{\textendash}83}, issn = {0302-9743}, url = {http://www.springerlink.com/(n0yw1g55rs45pg55g5zcinrm)/app/home/contribution.asp?referrer=parent\&backto=issue,32,34;journal,197,2212;linkingpublicationresults,1:105633,1}, author = {Soriano, M. and Fernandez, M. and Elisa Sayrol and Tom{\`a}s, J. and Casanelles, J. and Pegueroles, J. and Juan Hern{\'a}ndez Serrano} } @conference {cSoriano05, title = {Multimedia copyright protection platform demonstrator}, booktitle = {Third International Conference on Trust Management (iTrust{\textquoteright}05)}, year = {2005}, pages = {411{\textendash}414}, doi = {10.1007/11429760_32}, url = {http://dx.doi.org/10.1007/11429760_32}, author = {Soriano, M. and Fernandez, M. and Elisa Sayrol and Buliart, J. and Casanelles, J. and Pegueroles, J. and Juan Hern{\'a}ndez Serrano} } @article {aCasas05, title = {Mutual feedback scheme for face detection and tracking aimed at density estimation in demonstrations}, journal = {IEE Proceedings-Vision, Image and Signal Processing}, volume = {152}, number = {3}, year = {2005}, month = {05/2005}, pages = {334{\textendash}346}, abstract = {

Crowded video sequences, like those of demonstrations, offer an interesting challenge for object detection and tracking owing to their complexity: taken outdoors, often in different illumination conditions; showing faces not in frontal view, with perspective effects, complex background, etc. Tracking of individuals becomes a difficult task due to the high number of occlusions. The paper proposes a mutual feedback spatiotemporal detection strategy to tackle these problems. The system improves its efficiency thanks to a cooperative approach between spatial detection and temporal tracking. Spatial detection is based on skin colour classification and shape analysis by morphological tools. Temporal tracking is based on the analysis of the optical flow. The mutual feedback scheme benefits both spatial detection and temporal tracking. In order to deal with multiple occlusions, a graph-based tracking technique, which takes advantage of neighbourhood consistency, has been introduced.

}, issn = {1350-245X}, doi = {10.1049/ip-vis:20045071}, url = {http://dx.doi.org/10.1049/ip-vis:20045071}, author = {Casas, J. and Puig-Sitjes, A. and Puig-Folch, P.} } @conference {cCanton-Ferrer05b, title = {Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding}, booktitle = {2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms}, year = {2005}, abstract = {

This paper presents a novel approach to the problem of estimating and tracking 3D locations of multiple targets in a scene using measurements gathered from multiple calibrated cameras. Estimation and tracking is jointly achieved by a newly conceived computational process, the Projective Kalman {\textasciimacron}lter (PKF), allowing the problem to be treated in a single, uni{\textasciimacron}ed framework. The projective nature of observed data and information redundancy among views is exploited by PKF in order to overcome occlusions and spatial ambiguity. To demonstrate the e{\textregistered}ectiveness of the proposed algorithm, the authors present tracking results of people in a SmartRoom scenario and compare these results with existing methods as well.

}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and Tekalp, M. and M. Pard{\`a}s} } @conference {cQun05, title = {Shadow removal with blob-based Morphological Reconstruction for Error Corretion}, booktitle = {2005 IEEE International Conference on Acoustics, Speech, and Signal Processing}, year = {2005}, pages = {729{\textendash}732}, author = {Xu, Li-Qun and Landabaso, J. and M. Pard{\`a}s} } @inbook {bCanton-Ferrer05, title = {Towards a Bayesian Approach to Robust Finding Correspondances in Multiple View Geometry Environments}, booktitle = {Computational Science {\textendash} ICCS 2005}, volume = {3515}, year = {2005}, pages = {281{\textendash}289}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

This paper presents a new Bayesian approach to the problem of finding correspondences of moving objects in a multiple calibrated camera environment. Moving objects are detected and segmented in multiple cameras using a background learning technique. A Point Based Feature (PBF) of each foreground region is extracted, in our case, the top. This features will be the support to establish the correspondences. A reliable, efficient and fast computable distance, the symmetric epipolar distance , is proposed to measure the closeness of sets of points belonging to different views. Finally, matching the features from different cameras originating from the same object is achieved by selecting the most likely PBF in each view under a Bayesian framework. Results are provided showing the effectiveness of the proposed algorithm even in case of severe occlusions or with incorrectly segmented foreground regions.

}, isbn = {978-3-540-26043-1}, doi = {10.1007/11428848_35 }, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer05, title = {Towards a bayesian approach to robust finding correspondences in multiple view geometry environments}, booktitle = {Workshop on Computer Graphics and Geometric Modelling. Intrernational Conference on Computational Science}, year = {2005}, pages = {281{\textendash}289}, abstract = {

This paper presents a new Bayesian approach to the problem of finding correspondences of moving objects in a multiple calibrated camera environment. Moving objects are detected and segmented in multiple cameras using a background learning technique. A Point Based Feature (PBF) of each foreground region is extracted, in our case, the top. This features will be the support to establish the correspondences. A reliable, efficient and fast computable distance, the symmetric epipolar distance, is proposed to measure the closeness of sets of points belonging to different views. Finally, matching the features from different cameras originating from the same object is achieved by selecting the most likely PBF in each view under a Bayesian framework. Results are provided showing the effectiveness of the proposed algorithm even in case of severe occlusions or with incorrectly segmented foreground regions.

}, isbn = {3-540-26043-9}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @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 {cGiro-i-Nieto04, title = {The Moonlight project: bringing light to our satellite}, booktitle = {Proceedings of the 4th International Conference on Solar Power from Space SPS{\textquoteright}04 The 4th International Conference on Solar Power SPS{\textquoteright}04 together with the 5th International Conference on Wireless Power transmissions WPT 5}, year = {2004}, pages = {99{\textendash}100}, isbn = {92-9092-878-6}, author = {Xavier Gir{\'o}-i-Nieto and Aragon, M. and Prats, X. and Acero, L.} } @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.} } @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.} } @article {aLandabaso04, title = {Robust Tracking and Object Classification Towards Automated Video Surveillance}, journal = {Lecture notes in computer science}, volume = {3212}, year = {2004}, pages = {463{\textendash}470}, issn = {0302-9743}, author = {Landabaso, J. and Xu, Li-Qun and M. Pard{\`a}s} } @conference {cJose-Luis04, title = {Robust Tracking and Object Classification Towards Automated Video Surveillance}, booktitle = {International Conference on Image Analysis and Recognition}, year = {2004}, pages = {46333{\textendash}470}, isbn = {3540232230}, author = {Landabaso, J. and M. Pard{\`a}s and Xu, Li-Qun} } @conference {cCasas04, title = {Spatial-temporal Video Analysis for Improved Pedestrian Detection: Application to Density Estimation and Tracking in Demonstrations}, booktitle = {Image Analysis for Multimedia Interactive Services}, year = {2004}, pages = {334{\textendash}346}, abstract = {

Crowded video sequences like those of demonstrations offer an interesting challenge for object extraction and tracking due to their complexity: taken outdoors, often in extreme illumination conditions, with faces not in frontal view, perspective, complex background, etc. Tracking of the individuals becomes a difficult task due to the high number of occlusions. In order to deal with these problems a mutual feedback spatial-temporal detection algorithm is proposed. The system improves its efficiency thanks to a cooperative approach between spatial detection and temporal tracking. Spatial detection is based on skin color classification and shape analysis by morphological tools. Temporal tracking is based on the analysis of the optical flow. The mutual feedback approach improves both spatial detection and temporal tracking. In order to deal with multiple occlusions, a graph-based approach taking advantage of the neighborhood consistency has been introduced.

}, isbn = {972-98115-7-1}, author = {Casas, J. and Puig-Sitjes, A. and Puig-Folch, P.} } @conference {cLandabaso03, title = {HMM recognition of expressions in unrestrained video intervals}, booktitle = {International conference on Acoustics, Speech, and Signal Processing}, year = {2003}, pages = {197{\textendash}200}, isbn = {0-7803-7664-1}, author = {Landabaso, J. and M. Pard{\`a}s and Bonafonte, A.} } @conference {c03, title = {The Moon Orbital Mirror}, booktitle = {54th International Astronautical Congress (IAC)}, year = {2003}, month = {10/2003}, address = {Bremen, Germany}, abstract = {

The current state of space exploration pushes that one of the next steps is the establishment of a permanent base on the Moon. This paper describes the problems related to lunar night, explains the benefits that the Moon Orbital Mirror would offer and gives a possible design for the satellite.

}, url = {http://www.zarm.uni-bremen.de/iaf2003/abstracts/data/pdf/IAC-03-IAA.1.1.02.PDF}, author = {Acero, LLu{\'\i}s and {\`A}. Arag{\'o}n and Xavier Gir{\'o}-i-Nieto and Prats, Xavier} } @conference {cPardas02, title = {Emotion recognition based on MPEG-4 facial animation parameters}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, year = {2002}, pages = {3624{\textendash}3627}, author = {M. Pard{\`a}s and Bonafonte, A. and Landabaso, J.} } @article {aPardas02, title = {Facial animation parameters extraction and expression recognition using Hidden Markov Models}, journal = {Signal processing: image communication}, number = {17}, year = {2002}, pages = {675{\textendash}688}, issn = {0923-5965}, author = {M. Pard{\`a}s and Bonafonte, A.} } @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.} } @conference {cSayrol01, title = {Color Inititialization for Lip Tracking}, booktitle = {International Conference on Augmented, Virtual Environments and 3D Imaging}, year = {2001}, pages = {351{\textendash}354}, author = {Elisa Sayrol and Fischi, O. and M. Pard{\`a}s} } @conference {cPineda01, title = {Estudio de campos de golf mediante t{\'e}cnicas de segmentaci{\'o}n}, booktitle = {IX Congreso Nacional de Teledetecci{\'o}n}, year = {2001}, address = {Lleida, Spain}, author = {Pineda, N and Jorge, J and Garrido, L. and Salembier, P.} } @conference {cPardas01, title = {Facial Animation Parameters extraction and Expression detection using HMM}, booktitle = {International Conference on Augmented, Virtual Environments and 3D Imaging}, year = {2001}, pages = {120{\textendash}123}, isbn = {1-4244-1764-3}, author = {M. Pard{\`a}s and Bonafonte, A.} } @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.} } @article {aPardas01, title = {Motion estimation based tracking of active contours}, journal = {Pattern recognition letters}, volume = {22}, year = {2001}, pages = {1447{\textendash}1456}, issn = {0167-8655}, author = {M. Pard{\`a}s and Elisa Sayrol} } @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 {cPardas00, title = {Extraction and tracking of the eyelids}, booktitle = {International Conference on Acoustics, Speech and Signal Processing}, year = {2000}, author = {M. Pard{\`a}s} } @conference {cPardas00a, title = {A new approach to active contours for tracking}, booktitle = {IEEE International Conference on Image Processing}, year = {2000}, isbn = {0-7803-7403-7}, author = {M. Pard{\`a}s and Elisa Sayrol} } @conference {cPardas99, title = {Automatic Face Analysis for Model Calibration}, booktitle = {International Workshop on Synthetic and natural hybrid coding and three dimensional imaging}, year = {1999}, pages = {12{\textendash}15}, isbn = {0-7695-0750-6}, author = {M. Pard{\`a}s} } @conference {cSalembier99a, title = {Hierarchical visual description schemes for still images and video sequences}, booktitle = {1999 IEEE International Conference on Image Processing, ICIP 1999}, year = {1999}, address = {Kobe, Japan}, author = {Salembier, P. and O{\textquoteright}Connor, N. and Correia, P. and Pereira, F.} } @conference {cSalembier98a, title = {Connected operators for sprite creation and layered representation of image sequences}, booktitle = {9th European Signal Processing Conference, EUSIPCO 1998}, year = {1998}, pages = {2105{\textendash}2108}, address = {Rhodes, Greece}, isbn = {978-1-4503-0159-6}, author = {Salembier, P. and Pujol, O. and Garrido, L.} } @conference {cPardas98, title = {Video Object Segmentation introducing depth and motion information}, booktitle = {IEEE International Conference on Image Processing}, year = {1998}, isbn = {-}, author = {M. Pard{\`a}s} } @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 {aPardas97, title = {Object-base image coding}, journal = {Vistas in astronomy}, volume = {41}, number = {3}, year = {1997}, pages = {455{\textendash}461}, issn = {0083-6656}, author = {M. Pard{\`a}s} } @conference {cPardas97, title = {Relative depth estimation and segmentation in monocular sequences}, booktitle = {1997 PICTURE CODING SYMPOSIUM}, year = {1997}, pages = {367{\textendash}372}, isbn = {0 7923 7862 8}, author = {M. Pard{\`a}s} } @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 {aPardas97a, title = {Segmentation of video sequences for partition tree generation}, journal = {Annales des t{\'e}lecommunications. Annals of telecommunications}, volume = {52}, number = {7-8}, year = {1997}, pages = {389{\textendash}396}, issn = {0003-4347}, author = {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 {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.} } @article {aSalembier96, title = {Morphological operators for image and video compression}, journal = {IEEE transactions on image processing}, volume = {5}, number = {6}, year = {1996}, month = {06/1996}, pages = {881{\textendash}898}, abstract = {

This paper deals with the use of some morphological tools for image and video coding. Mathematical morphology can be considered as a shape-oriented approach to signal processing, and some of its features make it very useful for compression. Rather than describing a coding algorithm, the purpose of this paper is to describe some morphological tools that have proved attractive for compression. Four sets of morphological transformations are presented: connected operators, the region-growing version of the watershed, the geodesic skeleton, and a morphological interpolation technique. The authors discuss their implementation, and show how they can be used for image and video segmentation, contour coding, and texture coding.

}, issn = {1057-7149}, doi = {10.1109/83.503906}, author = {Salembier, P. and Brigger, P. and Casas, J. and M. Pard{\`a}s} } @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 {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.} } @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.} } @phdthesis {dPardas95, title = {Segmentaci{\'o}n Morfol{\'o}gica de Secuencias de Im{\'a}genes: Aplicaci{\'o}n a la Codificaci{\'o}n}, year = {1995}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, author = {M. Pard{\`a}s}, editor = {Salembier, P.} } @article {aPardas94, title = {3d morphological segmentation and motion estimation for image sequences}, journal = {Signal processing}, volume = {38}, number = {2}, year = {1994}, pages = {31{\textendash}43}, issn = {0165-1684}, author = {M. Pard{\`a}s and Salembier, P.} } @article {aSalembier94, title = {Hierarchical morphological segmentation for image sequence coding}, journal = {IEEE transactions on image processing}, volume = {3}, number = {5}, year = {1994}, pages = {639{\textendash}651}, issn = {1057-7149}, author = {Salembier, P. and M. Pard{\`a}s} } @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 {cPardas94b, title = {Joint region and motion estimation with morphological tools}, booktitle = {International Symposium on Mathematical Morphology, ISMM 1994}, year = {1994}, address = {Fontainebleau, France}, author = {M. Pard{\`a}s and Salembier, P.} } @conference {cPardas94a, title = {Motion region overlapping for segmentation-based video coding}, booktitle = {International Conference on Image Processing, ICIP{\textquoteright}94}, year = {1994}, pages = {428{\textendash}431}, address = {Austin, Texas}, isbn = {0-7803-5470-2}, author = {M. Pard{\`a}s and Salembier, P. and Gonzalez, B.} } @conference {cPardas94, title = {Time-recursive segmentation of image sequences}, booktitle = {European Signal Processing Conference, EUSIPCO-94}, year = {1994}, pages = {18{\textendash}21}, address = {Edinburgh, UK}, author = {M. Pard{\`a}s and Salembier, P.} } @conference { cSalembier94, title = {Very low bit rate video coding using morphological segmentation and contour/texture motion compensation}, booktitle = {12th International Conference on Pattern Recognition, ICPR 1994}, year = {1994}, address = {Jerusalem, Israel}, author = {Salembier, P. and Gu, C. and M. Pard{\`a}s and Kunt, M} } @conference {cPardas93a, title = {3D morphological segmentation and motion estimation for image sequences}, booktitle = {International Symposium on Mathematical Morphology and its applications to image and signal processing, ISMM 1993}, year = {1993}, pages = {58{\textendash}63}, address = {Barcelona, Spain}, isbn = {0-8186-8821-1}, author = {M. Pard{\`a}s and Salembier, P.} } @conference {cPardas93, title = {3D morphological segmentation or image sequence processing}, booktitle = {IEEE Winter Workshop on Nonlinear Signal Processing}, year = {1993}, pages = {31{\textendash}36}, address = {Tampere, Finland}, isbn = {960-7620-06-4}, author = {M. Pard{\`a}s and Salembier, P. and Torres, L.} } @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 {cPardas92, title = {Connectivity filters for image sequences}, booktitle = {IMAGE ALGEBRA AND MORPHOLOGICAL IMAGE PROCESSING. SPIE}, year = {1992}, pages = {318{\textendash}329}, isbn = {0 7923 5133 9}, author = {M. Pard{\`a}s and Torres, L.} } @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 {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.} }