@conference {cCumplido-Mayoral23a, title = {Brain-age mediates the association between modifiable risk factors and cognitive decline early in the AD continuum}, booktitle = {Alzheimer{\textquoteright}s Association International Conference (AAIC)}, year = {2023}, month = {07/2023}, address = {Amsterdam, Netherlands}, author = {Irene Cumplido-Mayoral and Anna Brugulat-Serrat and Gonzalo S{\'a}nchez-Benavides and Armand Gonz{\'a}lez-Escalante and Federica Anastasi and Marta Mila-Aloma and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Carolina Minguillon and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @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 {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 {pTerradas22, title = {A method, system and computer programs to automatically transform an image}, journal = {European Patent Office}, volume = {21382176}, year = {2022}, abstract = {

The present invention is directed, in general, to a method and a system to automatically transform an image using neural networks. More specifically, the invention relates to a controllable image generation through an image representation and several conditions using a conditional Neural Network.

The method comprises receiving, by a processing unit, at least one image and processing the received image to obtain an image representation thereof (i.e. an intermediate representation of the initial image that captures high level features and low level properties of the image and that is structured in an understandable way for a conditional Neural Network such as a deep generative Neural Network). The method also includes receiving, by an encoding unit, one or more references (e.g. other images, text, labels, combinations thereof, or even other data describing how the received image should be transformed) and encoding the received one or more references into one or more features, the latter being further provided to a conditional Neural Network as a condition(s). In addition, the method further applies the conditional Neural Network to transform the obtained image representation into a resulting conditioned image based on said condition(s).

}, issn = {21382176.2}, url = {https://patentscope.wipo.int/search/es/detail.jsf?docId=EP373278976}, author = {Terradas, R. and Pau Domingo and Grau, M. and Alarc{\'o}n, E. and Ruiz-Hidalgo, J.} } @article {aa, title = {QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation--Analysis of Ranking Metrics and Benchmarking Results}, journal = {Journal of Machine Learning for Biomedical Imaging}, year = {2022}, month = {08/2022}, abstract = {

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

}, url = {https://www.melba-journal.org/papers/2022:026.html}, author = {Raghav Metha and Angelos Filos and Ujjwal Baid and Laura Mora and Ver{\'o}nica Vilaplana and Christos Davatzikos and Bjoern Menze and Spyridon Bakas and Yarin Gal and Tar Arbel} } @conference {cDuartec, title = {Sign Language Video Retrieval with Free-Form Textual Queries}, booktitle = {CVPR 2022 - CVF/IEEE Conference on Computer Vision and Pattern Recognition}, year = {2022}, abstract = {

Systems that can efficiently search collections of sign language videos have been highlighted as a useful application of sign language technology. However, the problem of searching videos beyond individual keywords has received limited attention in the literature. To address this gap, in this work we introduce the task of sign language retrieval with free-form textual queries: given a written query (e.g., a sentence) and a large collection of sign language videos, the objective is to find the signing video in the collection that best matches the written query. We propose to tackle this task by learning cross-modal embeddings on the recently introduced large-scale How2Sign dataset of American Sign Language (ASL). We identify that a key bottleneck in the performance of the system is the quality of the sign video embedding which suffers from a scarcity of labeled training data. We, therefore, propose SPOT-ALIGN, a framework for interleaving iterative rounds of sign spotting and feature alignment to expand the scope and scale of available training data. We validate the effectiveness of SPOT-ALIGN for learning a robust sign video embedding through improvements in both sign recognition and the proposed video retrieval task.

}, author = {Amanda Duarte and Samuel Albanie and Xavier Gir{\'o}-i-Nieto and G{\"u}l Varol} } @article {xAguilar21, title = {2D-to-3D Lifting of Sign Language Body Poses with Recurrent Neural Networks}, year = {2021}, month = {01/2021}, institution = {UPC ETSETB TelecomBCN}, address = {Barcelona}, abstract = {

This paper aims at improving the quality of a dataset\ that contains multiple sequences of 3D poses extracted from\ American Sign Language videos. Each pose consists of 147\ points with three coordinates each. We propose an algorithm able to correct missing points as well as to add some\ constraints such as the length of the bones. To prove the\ quality of the algorithm{\textquoteright}s outcome, we evaluate the task of\ lifting 2D to 3D poses with a deep learning model trained\ on raw data, and another one trained with the preprocessed\ data.

}, author = {Aguilar, Jordi}, editor = {Xavier Gir{\'o}-i-Nieto and Amanda Duarte} } @article {aAbadal, title = {A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 imagery}, journal = {Remote Sensing}, volume = {13}, year = {2021}, month = {2021}, pages = {4547}, abstract = {

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

}, author = {Sa{\"u}c Abadal and Luis Salgueiro and Javier Marcello and Ver{\'o}nica Vilaplana} } @conference {cMayoral21, title = {Machine learning on combined neuroimaging and plasma biomarkers for triaging participants of secondary prevention trials in Alzheimer{\textquoteright}s Disease}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Gemma Salvad{\'o} and Mahnaz Shekari and Carles Falcon and Marta Mil{\`a} Alom{\`a} and Aida Ni{\~n}erola Baiz{\'a}n and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @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} } @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} } @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 {cGorrizc, title = {Assessing Knee OA Severity with CNN attention-based end-to-end architectures}, booktitle = {International Conference on Medical Imaging with Deep Learning (MIDL) 2019}, year = {2019}, month = {02/2019}, publisher = {JMLR}, organization = {JMLR}, address = {London, United Kingdom}, abstract = {

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

}, url = {http://proceedings.mlr.press/v102/gorriz19a.html}, author = {G{\'o}rriz, Marc and Antony, Joseph and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @conference {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 {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 {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} } @mastersthesis {xArtigot18, title = {Automatic fruit classification using deep learning}, year = {2018}, abstract = {

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

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

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

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

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

}, author = {P. Petrone and Adri{\`a} Casamitjana and C. Falcon and M. Artigues and G. Operto and S. Skouras and R. Cacciaglia and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @article {aPetrone18, title = {Characteristic Brain Volumetric Changes in the AD Preclinical Signature.}, journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association}, volume = {14}, year = {2018}, month = {07/2018}, pages = {P1235}, doi = {10.1016/j.jalz.2018.06.1737}, author = {P. Petrone and Adri{\`a} Casamitjana and C. Falcon and M. Artigues and G. Operto and S. Skouras and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @conference {cGene-Mola18, title = {Fruit Detection Using Mobile Terrestrial Laser Scanning}, booktitle = {AgEng 2018,}, year = {2018}, month = {07/2018}, address = {Wageningen (Netherlands)}, abstract = {

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

}, author = {Gen{\'e}-Mola, Jordi and Gregorio, Eduard and Guevara, Javier and Auat, Fernando and Escol{\`a}, Alexandre and Morros, J.R. and Rosell-Polo, Joan R.} } @conference {cBakas19, title = {Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge}, booktitle = {MICCAI - Multimodal Brain Tumor Segmentation Challenge}, year = {2018}, month = {12/2018}, abstract = {

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

}, url = {https://arxiv.org/pdf/1811.02629.pdf}, author = {Spyridon Bakas and Mauricio Reyes and Andras Jakab and Stefan Bauer and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and et. Al.} } @mastersthesis {xAlsina, title = {An interactive Lifelog Search Engine for LSC2018}, year = {2018}, abstract = {

This thesis consists on developing an interactive lifelog search engine for the LSC 2018 search\ challenge at ACM ICMR 2018. This search engine is created in order to browse for images from a given lifelog dataset and display them along with some written information related to them and four other images providing contextualization about the searched one.

First of all, the work makes an introduction to the relevance of this project. It introduces the\ reader to the main social problems affronted and the aim of our project to deal with them. Thus, go ahead with the scope of the project introducing to the main objectives fixed. Also, the work is gone by the actual state of the same kind of prototypes that already exist to let the reader see the differences that our project presents.

After the project approach is done, it begins a travel trough the methodology and creation\ process, going deep in the main aspects and the explanation of every election and decision, also remarking the limits of the current prototype.

Additionally, the project concludes with a result section where the system is tested with six users. They are asked to find three specific images using the search engine. This test is divided in two sections: first, a qualitative section where the user is asked to test the system and fill out a survey to see how comfortable it is for him. And a second section, more quantitative, where they value the speed of our system.

Finally, the project concludes going through the actual and future ethics of lifelogging in\ general and with a final conclusion further investigation and future improvement.

}, author = {Alsina, Adri{\`a}}, editor = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @conference {cAlsina, title = {An Interactive Lifelog Search Engine for LSC2018}, booktitle = {Lifelog Search Challenge workshop at ICMR2018}, year = {2018}, month = {06/2018}, publisher = {ACM}, organization = {ACM}, address = {Yokohama, Japan}, abstract = {

In this work, we describe an interactive lifelog search engine developed for the LSC 2018 search challenge at ACM ICMR 2018. The paper introduces the four-step process required to support lifelog search engines and describes the source data for the search engine as well as the approach to ranking chosen for the iterative search engine. Finally the interface used is introduced before we highlight the limits of the current prototype and suggest opportunities for future work.

}, doi = {10.1145/3210539.3210546}, url = {https://dl.acm.org/citation.cfm?id=3210546}, author = {Alsina, Adri{\`a} and Xavier Gir{\'o}-i-Nieto and Gurrin, Cathal} } @conference {cGorrizb, title = {Leishmaniasis Parasite Segmentation and Classification Using Deep Learning}, booktitle = {International Conference on Articulated Motion and Deformable Objects}, year = {2018}, address = {Palma, Spain}, abstract = {

Leishmaniasis is considered a neglected disease that causes thousands of deaths annually in some tropical and subtropical countries. There are various techniques to diagnose leishmaniasis of which manual microscopy is considered to be the gold standard. There is a need for the development of automatic techniques that are able to detect parasites in a robust and unsupervised manner. In this paper we present a procedure for automatizing the detection process based on a deep learning approach. We train a U-net model that successfully segments leismania parasites and classifies them into promastigotes, amastigotes and adhered parasites.

}, author = {G{\'o}rriz, Marc and Albert Aparicio and Berta Ravent{\'o}s and Daniel L{\'o}pez-Codina and Ver{\'o}nica Vilaplana and Elisa Sayrol} } @inbook {bGorriz18, title = {Leishmaniasis Parasite Segmentation and Classification Using Deep Learning}, booktitle = { Articulated Motion and Deformable Objects}, volume = {10945}, number = {Lecture Notes in Computer Science}, year = {2018}, pages = {53-62}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

Leishmaniasis is considered a neglected disease that causes thousands of deaths annually in some tropical and subtropical countries. There are various techniques to diagnose leishmaniasis of which manual microscopy is considered to be the gold standard. There is a need for the development of automatic techniques that are able to detect parasites in a robust and unsupervised manner. In this paper we present a procedure for automatizing the detection process based on a deep learning approach. We train a U-net model that successfully segments leismania parasites and classifies them into promastigotes, amastigotes and adhered parasites.

}, issn = {978-3-319-94544-6}, doi = {10.1007/978-3-319-94544-6}, author = {G{\'o}rriz, Marc and Albert Aparicio and Berta Ravent{\'o}s and Ver{\'o}nica Vilaplana and Elisa Sayrol and Daniel L{\'o}pez-Codina} } @conference {cAssensa, title = {PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks}, booktitle = {ECCV 2018 Workshop on Egocentric Perception, Interaction and Compution (EPIC)}, year = {2018}, month = {07/2018}, publisher = {Springer}, organization = {Springer}, address = {Munich, Germany}, abstract = {

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

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

}, doi = {10.1007/978-3-030-11021-5_25}, url = {https://doi.org/10.1007/978-3-030-11021-5_25}, author = {Assens, Marc and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @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} } @article {aAssens, title = {Scanpath and Saliency Prediction on 360 Degree Images}, journal = {Elsevier Signal Processing: Image Communication}, year = {2018}, abstract = {

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

}, url = {https://www.sciencedirect.com/science/article/pii/S0923596518306209}, author = {Assens, Marc and McGuinness, Kevin and O{\textquoteright}Connor, N. and Xavier Gir{\'o}-i-Nieto} } @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} } @mastersthesis {xJimenez, title = {Class Weighted Convolutional Features for Image Retrieval}, year = {2017}, abstract = {

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

Grade: A with honours (10.0/10.0)

Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or ne-tuning a model every time new images are added to the database is neither ecient nor scalable. Convolutional Neural Networks trained for image classi cation over large datasets have been proven e ective feature extractors when transferred to the task of image retrieval. The most successful approaches are based in encoding the activations of convolutional layers as they convey the image spatial information. Our proposal goes beyond and aims at a local-aware encoding of these features depending on the predicted image semantics, with the advantage of using only of the knowledge contained inside the network. In particular, we employ Class Activation Maps (CAMs) to obtain the most discriminative regions of the image from a semantic perspective. Additionally, CAMs are also used to generate object proposals during an unsupervised re-ranking stage after a rst fast search. Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate that our system is competitive and even outperforms the current state-of-the-art when using o -the-shelf models trained on the object classes of ImageNet.

[Project page]

Class Weighted Convolutional Features for Image Retrieval from Xavier Giro-i-Nieto
}, keywords = {Convolutional Neural Networks, deep learning, image retrieval, Transfer Learning, Visual Instance Search}, author = {Jim{\'e}nez, Albert}, editor = {Xavier Gir{\'o}-i-Nieto and {\'A}lvarez, Jose M} } @conference {cJimenez, title = {Class Weighted Convolutional Features for Visual Instance Search}, booktitle = {28th British Machine Vision Conference (BMVC)}, year = {2017}, month = {09/2017}, address = {London, UK}, abstract = {

Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images.\ In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable.\ Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors when transferred to the task of image retrieval. The most successful approaches are based on encoding the activations of convolutional layers, as they convey the image spatial information. \ Our proposal goes beyond and aims at a local-aware encoding of these features depending on the predicted image semantics, with the advantage of using only of the knowledge contained inside the network.\ In particular, we employ Class Activation Maps (CAMs) to obtain the most discriminative regions from a semantic perspective. Additionally, CAMs are also used to generate object proposals during an unsupervised re-ranking stage after a first fast search.\ Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate that our system is competitive and even outperforms the current state-of-the-art when using off-the-shelf models trained on the object classes of ImageNet.

Class Weighted Convolutional Features for Image Retrieval from Xavier Giro-i-Nieto
}, author = {Jim{\'e}nez, Albert and {\'A}lvarez, Jose M and Xavier Gir{\'o}-i-Nieto} } @article {aMartinez17, title = {A closed-loop approach for tracking a humanoid robot using particle filtering and depth data}, journal = {Intelligent Service Robotics}, volume = {10}, year = {2017}, month = {10/2017}, pages = {297{\textendash}312}, abstract = {

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

}, keywords = {Humanoid robot, ~Particle Filter, ~RGB-D sensor, ~ROS, ~Tracking}, issn = {1861-2776}, doi = {10.1007/s11370-017-0230-0}, url = {http://rdcu.be/s1RE}, author = {Mart{\'\i}nez, P. A. and X. Lin and Castel{\'a}n, M and Casas, J. and Arechavaleta, G.} } @mastersthesis {xArazo, title = {The impact of visual saliency prediction in image classification}, year = {2017}, abstract = {

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

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

Grade: A (9.7/10.0)

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

The impact of visual saliency prediction in image classification from Xavier Giro
}, author = {Arazo, Eric}, editor = {McGuinness, Kevin and Mohedano, Eva and Xavier Gir{\'o}-i-Nieto} } @conference {cSalvadorc, title = {Learning Cross-modal Embeddings for Cooking Recipes and Food Images}, booktitle = {CVPR}, year = {2017}, month = {03/2017}, publisher = {CVF / IEEE}, organization = {CVF / IEEE}, address = {Honolulu, Hawaii, USA}, abstract = {

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

[Project page]

In the news:

}, doi = {10.1109/CVPR.2017.327}, url = {http://openaccess.thecvf.com/content_cvpr_2017/html/Salvador_Learning_Cross-Modal_Embeddings_CVPR_2017_paper.html}, author = {Amaia Salvador and Hynes, Nicholas and Aytar, Yusuf and Marin, Javier and Ofli, Ferda and Weber, Ingmar and Torralba, Antonio} } @article {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 {cAssens, title = {SaltiNet: Scan-path Prediction on 360 Degree Images using Saliency Volumes}, booktitle = {ICCV Workshop on Egocentric Perception, Interaction and Computing}, year = {2017}, month = {07/2017}, publisher = {IEEE}, organization = {IEEE}, address = {Venice, Italy}, abstract = {

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

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

SaltiNet: The Temporal Dimension of Visual Attention Models from Xavier Giro-i-Nieto
}, isbn = {978-1-5386-1034-3}, doi = {10.1109/ICCVW.2017.275}, url = {http://ieeexplore.ieee.org/document/8265485/}, author = {Assens, Marc and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N.} } @mastersthesis {xAssens, title = {The Temporal Dimension of Visual Attention Models}, year = {2017}, abstract = {

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

Grade: A with honours (10.0/10.0)

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

The Temporal Dimension of Visual Attention Models from Xavier Giro-i-Nieto
}, author = {Assens, Marc}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and Noel E. O{\textquoteright}Connor} } @conference {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} } @mastersthesis {xAduriz16, title = {Analysis of the dynamics of gray matter reduction in Alzheimer{\textquoteright}s Disease}, 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)

This project attempts to study the cerebral atrophy patterns in gray matter across the
di erent stages of the Alzheimer{\textquoteright}s Disease (AD), or more speci cally, along the entire AD
continuum, in a voxelwise approach. To this end, we propose and implement an extensible
toolbox that allows to t di erent models to the data, hence de ning a curve for each voxel
that shows the evolution of the gray matter volume in the respective region as compared
to the progression of the disease. The toolbox also includes several evaluation methods
to estimate how closely the proposed model ts the data for each particular voxel. The
resulting values, namely tting-scores, serve as a base to achieve two di erent goals: a)
to identify the regions within the brain that are (most) likely to follow the curve-shape
speci ed in a given model, and b) to depict the model that best describes the behavior of
the gray matter volume in each voxel from a xed set of models.

}, author = {Asier Aduriz}, editor = {Ver{\'o}nica Vilaplana} } @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 {cMarsden, title = {Dublin City University and Partners{\textquoteright} Participation in the INS and VTT Tracks at TRECVid 2016}, booktitle = {TRECVID Workshop 2016}, year = {2016}, month = {11/2016}, address = {Gaithersburg, MD, USA}, abstract = {

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

}, url = {http://doras.dcu.ie/21484/}, author = {Marsden, Mark and Mohedano, Eva and McGuinness, Kevin and Calafell, Andrea and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Zhou, Jiang and Azevedo, Lucas and Daubert, Tobias and Davis, Brian and H{\"u}rlimann, Manuela and Afli, Haithem and Du, Jinhua and Ganguly, Debasis and Li, Wei and Way, Andy and Smeaton, Alan F.} } @mastersthesis {xNespereira, title = {Siamese Convolutional Neural Network for Learning Object Similarities in RGB-D Images}, year = {2016}, abstract = {

Student: Alejandro Nespereira

Advisors: Farzad Husain (Catchoom), Tomasz Adamek (Catchoom) and Xavier Gir{\'o}-i-Nieto (UPC)

Program:\ Master in Computer Vision\ (Class of 2016)

This report explores the suitability of using a Siamese Convolutional Neural Network (CNN) for the task of false positive rejection. We present a Siamese CNN model trained with an in-house dataset of weakly textured objects. Our model is able to successfully assert the classification of an object detection pipeline with unseen new objects. Additionally, we also compare it with a hand-crafted method in order to compare its performance. We demonstrate the usage of our model by learning to discriminate between inter and intra object classes for a challenging dataset.

}, author = {Nespereira, Alejandro}, editor = {Husain, Farzad and Adamek, Tomasz 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} } @mastersthesis {xAlonso15, title = {Detecci{\'o} de Text utilitzant Xarxes Neuronals Convolucionals }, year = {2015}, abstract = {

Text detection in images is a challenging problem that has received a significant amount of attention. The inclusion of machine learning allows building accurate classifiers. The goal of this work is to detect text in images. In order to achieve this goal we analyze the Convolutional Neural Network (CNN). We use an implemented CNN to detect areas where there may be text. Then we analyze and filter this areas so we can obtain the detected text.\ 

}, author = {Antea Herrera Alonso}, editor = {Ver{\'o}nica Vilaplana} } @conference {cRoldan-Carlosa, title = {Event Video Retrieval using Global and Local Descriptors in Visual Domain}, booktitle = {IEEE/ACM International Workshop on Content-Based Multimedia Indexing - CBMI 2015 }, year = {2015}, month = {06/2015}, abstract = {

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

}, url = {https://upcommons.upc.edu/handle/2117/76553}, author = {Roldan-Carlos, Jennifer and Lux, Mathias and Xavier Gir{\'o}-i-Nieto and Pia Mu{\~n}oz-Trallero and Anagnostopoulos, Nektarios} } @mastersthesis {xAlfaro15, title = {Inclusion of depth information on a temporal hierarchical co-clustering technique}, year = {2015}, abstract = {

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

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

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

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

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

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

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

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

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

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

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

}, url = {http://arxiv.org/abs/1510.04842}, author = {David Varas and Alfaro, M. and Marqu{\'e}s, F.} } @article {aTochon15, title = {On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images}, journal = {Remote Sensing of Environment}, volume = {159}, year = {2015}, month = {03/2015}, pages = {318-331}, abstract = {

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

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

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

[Paper on arXiv]

[CBMI 2015 Conference website]

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

}, url = {http://arxiv.org/abs/1504.07874}, author = {Roldan-Carlos, Jennifer and Lux, Mathias and Xavier Gir{\'o}-i-Nieto and Pia Mu{\~n}oz-Trallero and Anagnostopoulos, Nektarios} } @conference {cMartinez14, title = {3D Shape Reconstruction from a Humanoid Generated Video Sequence}, booktitle = {IEEE International Conference on Humanoid Robots}, year = {2014}, address = {Madrid}, abstract = {

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

}, author = {Mart{\'\i}nez, P. A. and David Varas and Castel{\'a}n, M and Camacho, M and Marqu{\'e}s, F. and Arechavaleta, G.} } @conference {cMcGuinness, title = {Insight Centre for Data Analytics (DCU) at TRECVid 2014: Instance Search and Semantic Indexing Tasks}, booktitle = {2014 TRECVID Workshop}, year = {2014}, month = {11/2014}, publisher = {National Institute of Standards and Technology (NIST)}, organization = {National Institute of Standards and Technology (NIST)}, address = {Orlando, Florida (USA)}, abstract = {

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

[2014 TREC Video Retrieval Evaluation Notebook Papers and Slides]

Poster

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

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

Poster


}, author = {Imedio-Pereira, Sergi}, editor = {Mohedano, Eva and Xavier Gir{\'o}-i-Nieto and Albatal, Rami and O{\textquoteright}Connor, N.} } @mastersthesis {xFerrarons-Betrian, title = {Mobile Visual Search at Catchoom}, year = {2014}, abstract = {

Author: Miquel Ferrarons-Betrian

Advisors:\ Xavier Gir{\'o}-i-Nieto\ (UPC) and Tomasz Adamek\ (Catchoom)

Degree: Master in Computer Vision (1 year)

}, keywords = {feature selection, large-scale visual search, Mobile visual search, synthetic views matching, visual word}, author = {Ferrarons-Betrian, Miquel}, editor = {Adamek, Tomasz and Xavier Gir{\'o}-i-Nieto} } @conference {cAlonso-Gonzalez14, title = {Multidimensional SAR Data Analysis Based on Binary Partition Trees and the Covariance Matrix Geometry}, booktitle = {International Radar Conference 2014}, year = {2014}, month = {10/2014}, publisher = {SEE}, organization = {SEE}, address = {Lille, France}, abstract = {

In this paper, we propose the use of the Binary Partition Tree (BPT) as a region-based and multi-scale image representation to process multidimensional SAR data, with special emphasis on polarimetric SAR data. We also show that this approach could be extended to other types of remote sensing imaging technologies, such as hyperspatial imagery. The Binary Partition Tree contains a lot of information about the image structure at different detail levels. At the same time, this structure represents a convenient vehicle to exploit both the statistical properties, as well as the geometric properties of the multidimensional SAR data given by the covariance matrix. The BPT construction process and its exploitation for PolSAR and temporal data information estimation is analyzed in this work. In particular, this work focuses on the speckle noise filtering problem and the temporal characterization of the image dynamics. Results with real data are presented to illustrate the capabilities of the BPT processing approach, specially to maintain the spatial resolution and the small details of the image.

}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and 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} } @article {aAlonso-Gonzalez, title = {PolSAR Time Series Processing with Binary Partition Trees}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {52}, year = {2014}, month = {06/2014}, pages = {3553 {\textendash} 3567}, abstract = {

This paper deals with the processing of polarimetric synthetic aperture radar (SAR) time series. Different approaches to deal with the temporal dimension of the data are considered, which are derived from different target characterizations in this dimension. These approaches are the basis for defining two different binary partition tree (BPT) structures that are employed for SAR polarimetry (PolSAR) data processing. Once constructed, the BPT is processed by a tree pruning, producing a set of spatiotemporal homogeneous regions, and estimating the polarimetric response within them. It is demonstrated that the proposed technique preserves the PolSAR information in the spatial and the temporal domains without introducing bias nor distortion. Additionally, the evolution of the data in the temporal dimension is also analyzed, and techniques to obtain BPT-based scene change maps are defined. Finally, the proposed techniques are employed to process two real RADARSAT-2 data sets.

}, issn = {0196-2892}, doi = {10.1109/TGRS.2013.2273664}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @article {Suau2014, title = {Real-time Fingertip Localization Conditioned on Hand Gesture Classification}, journal = {Image and Vision Computing}, volume = {32}, year = {2014}, month = {05/2014}, pages = {522 - 532}, abstract = {

A method to obtain accurate hand gesture classification and fingertip localization from depth images is proposed. The Oriented Radial Distribution feature is utilized, exploiting its ability to globally describe hand poses, but also to locally detect fingertip positions. Hence, hand gesture and fingertip locations are characterized with a single feature calculation. We propose to divide the difficult problem of locating fingertips into two more tractable problems, by taking advantage of hand gesture as an auxiliary variable. Along with the method we present the ColorTip dataset, a dataset for hand gesture recognition and fingertip classification using depth data. ColorTip contains sequences where actors wear a glove with with colored fingertips, allowing automatic annotation. The proposed method is evaluated against recent works in several datasets, achieving promising results in both gesture classification and fingertip localization.

}, keywords = {dataset, fingertip classification, hand gesture recognition, interactivity, range camera}, issn = {0262-8856}, doi = {10.1016/j.imavis.2014.04.015}, url = {http://www.sciencedirect.com/science/article/pii/S0262885614000845}, author = {Suau, X. and Alcoverro, M. and L{\'o}pez-M{\'e}ndez, A. and Ruiz-Hidalgo, J. and Casas, J.} } @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.} } @mastersthesis {xAlmendros-Gutierrez14, title = {Visual instance mining of news videos using a graph-based approach}, year = {2014}, abstract = {

Advisors:\ Xavier Gir{\'o}-i-Nieto\ (UPC) and Horst Eidenberger (TU Wien)

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

The aim of this thesis is to design a tool that performs visual instance\ search mining for news video summarization. This means to extract the\ relevant content of the video in order to be able to recognize the storyline\ of the news.

Initially, a sampling of the video is required to get the frames with a desired rate. Then, different relevant contents are detected from each frame, focusing on faces, text and several objects that the user can select. Next, we use a graph-based clustering method in order to recognize them with a high accuracy and select the most representative ones to show them in the visual summary. Furthermore, a graphical user interface in Wt was developed to create an online demo to test the application.

During the development of the application we have been testing the tool with the CCMA dataset. We prepared a web-based survey based on four results from this dataset to check the opinion of the users. We also validate our visual instance mining results comparing them with the results obtained applying an algorithm developed at Columbia University for video summarization. We have run the algorithm on a dataset of a few videos on two events: {\textquoteright}Boston bombings{\textquoteright} and the {\textquoteright}search of the Malaysian airlines flight{\textquoteright}. We carried out another web-based survey in which users could compare our approach with this related work. With these surveys we analyze if our tool fulfill the requirements we set up.

We can conclude that our system extract visual instances that show the most relevant content of news videos and can be used to summarize these videos effectively.

Final grade: B (7/10)

}, url = {http://hdl.handle.net/2099.1/22362}, author = {Almendros-Guti{\'e}rrez, David}, editor = {Xavier Gir{\'o}-i-Nieto and Eidenberger, Horst} } @article {aAlonso-Gonzalez13a, title = {Bilateral Distance Based Filtering for Polarimetric SAR Data}, journal = {Remote Sensing}, volume = {5}, year = {2013}, month = {10/2013}, pages = {5620-5641}, abstract = {

This paper introduces a non-linear Polarimetric SAR data filtering approach able to preserve the edges and small details of the data. It is based on exploiting the data locality in both, the spatial and the polarimetric domains, in order to avoid mixing heterogeneous samples of the data. A weighted average is performed over a given window favoring pixel values that are close on both domains. The filtering technique is based on a modified bilateral filtering, which is defined in terms of spatial and polarimetric distances. These distances encapsulate all the knowledge in both domains for an adaptation to the data structure. Finally, the proposed technique is employed to process a real RADARSAT-2 dataset.

}, issn = {2072-4292}, doi = {10.3390/rs5115620}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P. and X. Deng} } @mastersthesis {xAntoja-Sabin13, title = {El tel{\`e}fon m{\`o}bil com a eina d{\textquoteright}aprenentatge informal}, year = {2013}, abstract = {

Director: Xavier Gir{\'o}-i-Nieto

Els tel{\`e}fons intel{\textperiodcentered}ligents estan cada dia m{\'e}s presents a la nostra societat. L{\textquoteright}educaci{\'o} secund{\`a}ria obligat{\`o}ria no n{\textquoteright}est{\`a} al marge. M{\'e}s aviat al contrari, {\'e}s un dels temes a nivell de conducta que genera m{\'e}s pol{\`e}mica actualment amb l{\textquoteright}alumnat adolescent. Per altra banda, {\'e}s una tecnologia molt potent per accedir a informaci{\'o} i relacionar-se, en qualsevol moment i lloc.

La proposta {\'e}s que l{\textquoteright}alumne hagi de captar amb el tel{\`e}fon m{\`o}bil algun element que tingui a veure amb all{\`o} que s{\textquoteright}ha treballat a classe i li sigui proper. Es tracta de vincular la teoria amb la pr{\`a}ctica, els conceptes de classe amb la seva quotidianitat.

El docent proposar{\`a} uns deures relacionats amb els continguts de classe. L{\textquoteright}alumne utilitzar{\`a} el tel{\`e}fon m{\`o}bil intel{\textperiodcentered}ligent com a vehicle per realitzar la tasca.

Per tal de deixar const{\`a}ncia de la feina feta, s{\textquoteright}utilitzar{\`a} la metodologia de la carpeta d{\textquoteright}aprenentatge electr{\`o}nica (electronic portfolio). Aquesta segona eina permetr{\`a} deixar const{\`a}ncia de tot el que l{\textquoteright}alumne va fent, avaluar-lo i fer una coavaluaci{\'o}.

A la mem{\`o}ria del treball s{\textquoteright}explica quins fonaments pedag{\`o}gics t{\'e} la proposta, com encaixa en el context normatiu de l{\textquoteright}educaci{\'o} secund{\`a}ria obligat{\`o}ria, exemples pr{\`a}ctics d{\textquoteright}exercicis amb els que s{\textquoteright}aplica la metodologia, com aix{\`o} es pot dur a terme a nivell t{\`e}cnic i els resultats de l{\textquoteright}experiment fet amb un grup d{\textquoteright}alumnes utilitzant la metodologia proposada.\ 

}, keywords = {educaci{\'o}, internet, ortfolio, secund{\`a}ria, significatiu. bloc, smartphone, tecnologia. aprenentatge, wordpress}, author = {Antoja-Sabin, Joan}, editor = {Xavier Gir{\'o}-i-Nieto} } @article {aAlcoverro13, title = {Gesture Control Interface for immersive panoramic displays}, journal = {Multimedia Tools and Applications}, year = {2013}, month = {07/2013}, pages = {1-27}, abstract = {

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

}, issn = {1380-7501}, doi = {10.1007/s11042-013-1605-7}, author = {Alcoverro, M. and Suau, X. and Morros, J.R. and L{\'o}pez-M{\'e}ndez, A. and A. Gil-Moreno and Ruiz-Hidalgo, J. and Casas, J.} } @conference {cAlonso-Gonzalez13, title = {PolSAR time series processing and analysis based on Binary Partition Trees}, booktitle = {PoLinSAR 2013 Workshop}, year = {2013}, address = {Frascati (Rome), Italy}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @article {aAlonso-Gonzalez13, title = {Processing Multidimensional SAR and Hyperspectral Images With Binary Partition Tree}, journal = {Proceedings of the IEEE}, volume = {101}, year = {2013}, month = {March, 2013}, pages = {723 - 747}, chapter = {723 - 747}, abstract = {

The current increase of spatial as well as spectral resolutions of modern remote sensing sensors represents a real opportunity for many practical applications but also generates important challenges in terms of image processing. In particular, the spatial correlation between pixels and/or the spectral correlation between spectral bands of a given pixel cannot be ignored. The traditional pixel-based representation of images does not facilitate the handling of these correlations.

In this paper, we discuss the interest of a particular hierarchical region-based representation of images based on binary partition tree (BPT). This representation approach is very flexible as it can be applied to any type of image. Here both optical and radar images will be discussed. Moreover, once the image representation is computed, it can be used for many different applications. Filtering, segmentation, and classification will be detailed in this paper. In all cases, the interest of the BPT representation over the classical pixel-based representation will be highlighted.

}, doi = {10.1109/JPROC.2012.2205209}, author = {Alonso-Gonz{\'a}lez, A. and Valero, S. and Chanussot, J. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @article {aTochon13, title = {Segmentation hyperspectrales de forets tropicales par arbres de partition binaires}, journal = {Revue fran{\c c}aise de photogramm{\'e}trie et de t{\'e}l{\'e}d{\'e}tection}, volume = {202}, year = {2013}, month = {May 2013}, pages = {55-65}, author = {G. Tochon and J.B. Feret and Valero, S. and R.E. Martin and R. Tupayachi and Chanussot, J. and Salembier, P. and G. Asner} } @article {aAlonso-Gonzalez12, title = {Filtering and Segmentation of Polarimetric SAR Data Based on Binary Partition Trees}, journal = {IEEE transactions on geoscience and remote sensing}, volume = {50}, year = {2012}, pages = {593{\textendash}605}, abstract = {

In this paper,we propose the use of binary partition trees (BPT) to introduce a novel region-based and multi-scale polarimetric SAR (PolSAR) data representation. The BPT structure represents homogeneous regions in the data at different detail levels. The construction process of the BPT is based, firstly, on a region model able to represent the homogeneous areas, and, secondly, on a dissimilarity measure in order to identify similar areas and define the merging sequence. Depending on the final application, a BPT pruning strategy needs to be introduced. In this paper, we focus on the application of BPT PolSAR data representation for speckle noise filtering and data segmentation on the basis of the Gaussian hypothesis, where the average covariance or coherency matrices are considered as a region model. We introduce and quantitatively analyze different dissimilarity measures. In this case, and with the objective to be sensitive to the complete polarimetric information under the Gaussian hypothesis, dissimilarity measures considering the complete covariance or coherency matrices are employed. When confronted to PolSAR speckle filtering, two pruning strategies are detailed and evaluated. As presented, the BPT PolSAR speckle filter defined filters data according to the complete polarimetric information. As shown, this novel filtering approach is able to achieve very strong filtering while preserving the spatial resolution and the polarimetric information. Finally, the BPT representation structure is employed for high spatial resolution image segmentation applied to coastline detection. The analyses detailed in this work are based on simulated, as well as on real PolSAR data acquired by the ESAR system of DLR and the RADARSAT-2 system.

}, issn = {0196-2892}, doi = {10.1109/TGRS.2011.2160647}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @inbook {bSuau12, title = {INTAIRACT: Joint Hand Gesture and Fingertip Classification for Touchless Interaction}, booktitle = {Computer Vision {\textendash} ECCV 2012}, volume = {7585}, year = {2012}, pages = {602-606}, publisher = {Springer}, organization = {Springer}, chapter = {3}, address = {Heidelberg}, abstract = {

In this demo we present INTAIRACT, an online hand-based touchless interaction system. Interactions are based on easy-to-learn hand gestures, that combined with translations and rotations render a user friendly and highly configurable system. The main advantage with respect to existing approaches is that we are able to robustly locate and identify fingertips. Hence, we are able to employ a simple but powerful alphabet of gestures not only by determining the number of visible fingers in a gesture, but also which fingers are being observed. To achieve such a system we propose a novel method that jointly infers hand gestures and fingertip locations using a single depth image from a consumer depth camera. Our approach is based on a novel descriptor for depth data, the Oriented Radial Distribution (ORD) [1]. On the one hand, we exploit the ORD for robust classification of hand gestures by means of efficient k-NN retrieval. On the other hand, maxima of the ORD are used to perform structured inference of fingertip locations. The proposed method outperforms other state-of-the-art approaches both in gesture recognition and fingertip localization. An implementation of the ORD extraction on a GPU yields a real-time demo running at approximately 17fps on a single laptop.

1. Suau, X., Ruiz-Hidalgo, J., Casas, J.R.: Oriented Radial Distribution on Depth\ Data: Application to the Detection of End-Effectors. In: ICASSP. (2012)

}, isbn = {978-3-642-33885-4}, issn = {978-3-642-33884-7}, doi = {10.1007/978-3-642-33885-4_62}, author = {Suau, X. and Alcoverro, M. and L{\'o}pez-M{\'e}ndez, A. and Ruiz-Hidalgo, J. and Casas, J.} } @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 {cNavarro12 , title = {Multi-view Body Tracking with a Detector-Driven Hierarchical Particle Filter}, booktitle = {7th International Conference AMDO 2012}, year = {2012}, month = {07/2012}, publisher = {Springer}, organization = {Springer}, address = {Port d{\textquoteright}Andratx, Mallorca}, 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.

}, 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.} } @article {aRuiz-Hidalgo12, title = {Multiview depth coding based on combined color/depth segmentation}, journal = {Journal of visual communication and image representation}, volume = {23}, number = {1}, year = {2012}, pages = {42{\textendash}52}, abstract = {

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

}, keywords = {3DTV, Depth map, multiview video coding, virtual view}, issn = {1047-3203}, doi = {10.1016/j.jvcir.2011.08.001}, url = {http://www.sciencedirect.com/science/article/pii/S1047320311001040}, author = {Ruiz-Hidalgo, J. and Morros, J.R. and Aflaki, P. and Calderero, F. and Marqu{\'e}s, F.} } @conference {cAlonso-Gonzalez12a, title = {Temporal polsar image series exploitation with binary partition trees}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012}, year = {2012}, address = {Munich, Germany}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @conference {cAlonso-Gonzalez12, title = {Variable local weight filtering for polsar data speckle noise reduction}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS{\textquoteright}2012}, year = {2012}, address = {Munich, Germany}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @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 {cAlonso-Gonzalez11a, title = {Binary partition tree as a polarimetric SAR data representation in the space-time domain}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011}, year = {2011}, pages = {3819{\textendash}3822}, address = {Vanouver, Canada}, isbn = {978-1-4577-1005-6}, doi = {10.1109/IGARSS.2011.6050063}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6050063}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @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 {aTenorio11, title = {Correlation between a semiautomated method based on ultrasound texture analysis and standard ultrasound diagnosis using white matter damage in preterm neonates as a model}, journal = {Journal of ultrasound in medicine}, volume = {30}, number = {10}, year = {2011}, pages = {1365{\textendash}1377}, issn = {0278-4297}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21968487}, author = {Tenorio, V. and Bonet, E. and Botet, F. and Marqu{\'e}s, F. and Amat, I. and Gratacos, E.} } @conference {cGiro-i-Nieto11, title = {Diversity ranking for video retrieval from a broadcaster archive}, booktitle = {1st ACM International Conference on Multimedia Retrieval (ICMR {\textquoteright}11)}, year = {2011}, pages = {1{\textendash}8}, abstract = {

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

}, isbn = {978-1-4503-0336-1}, doi = {10.1145/1991996.1992052}, url = {http://dx.doi.org/10.1145/1991996.1992052}, author = {Xavier Gir{\'o}-i-Nieto and Alfaro, M. and Marqu{\'e}s, F.} } @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 {cAlarcon11, title = {Learning to conceive, design, implement and operate circuits and systems}, booktitle = {2011 IEEE International Symposium on Circuits and Systems}, year = {2011}, pages = {1183{\textendash}1186}, isbn = {978-1-4244-9472-9}, doi = {10.1109/ISCAS.2011.5937780}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5937780\&tag=1}, author = {Alarc{\'o}n, E. and Bragos, R. and Elisa Sayrol} } @conference {cAlonso-Gonzalez11, title = {PolSAR speckle filtering and segmentation based on binary partition tree representation}, booktitle = {5th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry, PolInSAR 2011}, year = {2011}, pages = {1{\textendash}19}, address = {Frascati (Rome), Italy}, url = {http://cataleg.upc.edu/record=b1233548~S1*cat}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. 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.} } @mastersthesis {xAlfaro11 , title = {Reordenaci{\'o} i agrupament d{\textquoteright}imatges d{\textquoteright}una cerca de v{\'\i}deo}, year = {2011}, month = {01/2011}, abstract = {

La recuperaci{\'o} de v{\'\i}deo a trav{\'e}s de consultes textuals es una practica molt com{\'u} en els arxius de radiodifusi{\'o}. Les paraules clau de les consultes son comparades amb les metadades que s{\textquoteright}anoten manualment als assets de v{\'\i}deo pels documentalistes. A m{\'e}s, les cerques textuals b{\`a}siques generen llistes de resultats planes, on tots els resultats tenen la mateixa import{\`a}ncia, ja que, es limita a avaluar bin{\`a}riament si la paraula de cerca apareix o no entre les metadades associades als continguts. A m{\'e}s, acostumen a mostrar continguts molt similars, donant al usuari una llista ordenada de resultats de poca diversitat visual. La redund{\`a}ncia en els resultats provoca un malbaratament d{\textquoteright}espai a la interf{\'\i}cie gr{\`a}fica d{\textquoteright}usuari (GUI) que sovint obliga a l{\textquoteright}usuari a interactuar fortament amb la interf{\'\i}cie gr{\`a}fica fins localitzar els resultats rellevants per a la seva cerca. La aportaci{\'o} del present projecte consisteix en la presentaci{\'o} d{\textquoteright}una estrat{\`e}gia de reordenaci{\'o} i agrupaci{\'o} per obtenir keyframes de major rellev{\`a}ncia entre els primers resultats, per{\`o} al mateix temps mantenir una diversitat d{\textquoteright}assets. D{\textquoteright}aquesta forma, aquestes t{\`e}cniques permetran millorar els sistemes de visualitzaci{\'o} d{\textquoteright}imatges resultants d{\textquoteright}una cerca de v{\'\i}deo. L{\textquoteright}eina global es dissenya per ser integrada en l{\textquoteright}entorn del Digition, el gestor de continguts audiovisuals de la Corporaci{\'o} Catalana de Mitjans Audiovisuals.




}, url = {http://hdl.handle.net/2099.1/11106}, author = {Alfaro, M.}, editor = {Vives, X. and Xavier Gir{\'o}-i-Nieto} } @conference {cAlonso-Gonzalez10, title = {Filtering and segmentation of polarimetric SAR images with binary partition trees}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010}, year = {2010}, pages = {4043{\textendash}4046}, address = {Honolulu, USA}, isbn = {978-1-4244-9564-1}, doi = {10.1109/IGARSS.2010.5653466}, url = {http://cataleg.upc.edu/record=b1167223~S1*cat}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @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 {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} } @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 {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} } @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 {cRolon08, title = {Image compression with generalized lifting and partial knowledge of the signal pdf}, booktitle = {IEEE International Conference on Image Processing, ICIP 2008}, year = {2008}, pages = {250{\textendash}254}, address = {San Diego, USA}, isbn = {88-86179-83-9}, author = {Rol{\'o}n, J. and Salembier, P. and Alameda, X.} } @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 {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.} } @conference {cLuque06a, title = {Audio, Video and Multimodal Person Identification in a Smart Room}, booktitle = {CLEAR{\textquoteright}06 Evaluation Campaign and Workshop - Classification of Events, Activities and Relationships}, year = {2007}, pages = {258{\textendash}269}, isbn = {978-3-540-69567-7}, author = {Luque, J. and Morros, J.R. and Garde, A. and Anguita, J. and Farr{\'u}s, M. and Macho, D. and Marqu{\'e}s, F. and Mart{\'\i}nez, C. and Ver{\'o}nica Vilaplana and Hernando, J.} } @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 Dios, V. 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 {cSegura07, title = {Multimodal Head Orientation towards Attention Tracking in Smart Rooms}, booktitle = {2007 International Conference on Acoustics, Speech and Signal Processing}, year = {2007}, author = {Segura, C. and Abad, A. and Casas, J. and Hernando, J.} } @conference {cCabrera07, title = {Proyecto de Innovaci{\'o}n Docente COM@WEB (COMunicaciones en la WEB)}, booktitle = {VIII Simposio Nacional de Tecnolog{\'\i}as de la Informaci{\'o}n y las Comunicaciones en la Educaci{\'o}n. SINTICE{\textquoteright}07}, year = {2007}, month = {09/2007}, pages = {113{\textendash}120}, abstract = {

COMalaWEB significa Comunicaciones en la Web y es una plataforma de e-learning que ofrece diferentes recursos docentes on-line a los estudiantes universitarios con el objetivo de facilitar el estudio preferentemente en las tem{\'a}ticas de procesado de se{\~n}al y comunicaciones.

La plataforma es interactiva y se ha dise{\~n}ado para promover la experimentaci{\'o}n e-learning y adquisici{\'o}n de h{\'a}bitos de estudio basados en el uso de nuevas tecnolog{\'\i}as.

El sistema en un futuro a corto plazo podr{\'a} generar itinerarios autom{\'a}ticos de estudio al estudiante en funci{\'o}n del rendimiento obtenido en las pruebas de autoevaluaci{\'o}n y a partir de informaci{\'o}n empaquetada en metadatos e integrada en una base de datos de objetos de aprendizaje.

El objetivo del proyecto COMalaWEB a medio y largo plazo es constituir un punto de encuentro en la www para estudiantes, profesores y profesionales relacionados con el {\'a}rea de las telecomunicaciones y la docencia a nivel universitario.

Uno de los cursos integrados en COMalaWEB es el laboratorio virtual de comunicaciones anal{\'o}gicas y digitales LaViCAD, de libre difusi{\'o}n y acceso y basado en la simulaci{\'o}n de diferentes sistemas de comunicaciones que pueden ser utilizados tanto en docencia presencial como en educaci{\'o}n a distancia.\ 

Actualmente se puede consultar el contenido del proyecto en la plataforma: http://comweb.upc.edu/

}, isbn = {978-84-9732-597-4}, author = {Cabrera, M. and Closas, P. and Alfredo, L. and Xavier Gir{\'o}-i-Nieto and Rey, F.} } @inbook {bAbad07, title = {UPC Audio, Video and Multimodal Person Tracking Systems in the CLEAR Evaluation Campaign}, booktitle = {Lecture notes in computer science}, volume = {4122}, year = {2007}, pages = {93{\textendash}104}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-540-69567-7}, doi = {10.1007/978-3-540-69568-4_6}, author = {Abad, A. and Cristian Canton-Ferrer and Segura, C. and Landabaso, J. and Macho, D. and Casas, J. and Hernando, J. and M. Pard{\`a}s and Nadeu, C.} } @inbook {bLuque06, title = {Audio, Video and Multimodal Person Identification in a Smart Room}, booktitle = {Lecture notes in computer science - Multimodal Technologies for Perception of Humans}, volume = {4122}, year = {2006}, pages = {258{\textendash}269}, issn = {0302-9743}, doi = {10.1007/978-3-540-69568-4_23}, author = {Luque, J. and Morros, J.R. and Garde, A. and Anguita, J. and Farr{\'u}s, M. and Macho, D. and Marqu{\'e}s, F. and Mart{\'\i}nez, C. and Ver{\'o}nica Vilaplana and Hernando, J.} } @conference {cLuque06, title = {Multimodal Person Identification in a Smart Room}, booktitle = {IV Jornadas en Tecnolog{\'\i}a del Habla}, year = {2006}, pages = {327{\textendash}331}, isbn = {84-96214-82-6}, author = {Luque, J. and Morros, J.R. and Anguita, J. and Farr{\'u}s, M. and Macho, D. and Marqu{\'e}s, F. and Mart{\'\i}nez, C. and Ver{\'o}nica Vilaplana and Hernando, J.} } @conference {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 {cAnton05, title = {Collaborative Network Space: Infrastructure and Learning Application}, booktitle = {IEEE Region 8 EUROCON 2005 Conference: Computer as a tool.}, year = {2005}, pages = {803{\textendash}806}, isbn = {1-4244-0050-3}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01630054}, author = {Anton, M. and Vall-Llosera, M. and Jordi Torres and Romeu, J. and Jofre, L. and Sole, F. and Marqu{\'e}s, F. and Sabate, F. and Berenguer, J.} } @conference {cGiro-i-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 {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 {cO{\textquoteright}Connor03, title = {Region and object segmentation algorithms in the QIMERA segmentation platform}, booktitle = {Third International Workshop on Content-Based Multimedia Indexing}, year = {2003}, pages = {95{\textendash}103}, isbn = {978-84-612-2373-2}, author = {O{\textquoteright}Connor, N. and Sav, S. and Adamek, T. and Mezaris, V. and Kompatsiaris, I. and Lui, T. and Izquierdo, E. and Ferran, C. and Casas, J.} } @conference {cAvrithis03, title = {Unified access to heterogeneous audiovisual archives}, booktitle = {International Conference on Knowledge Management}, year = {2003}, pages = {1{\textendash}2}, address = {Graz, Austria}, author = {Avrithis, Y. and Stamou, G. and Wallace, M. and Marqu{\'e}s, F. and Salembier, P. and Xavier Gir{\'o}-i-Nieto and Haas, W. and Vallant, H. and Zufferey, M.} } @article {aAvrithis03, title = {Unified Access to Heterogeneous Audiovisual Archives}, journal = {Journal of universal computer science}, volume = {9}, number = {6}, year = {2003}, pages = {510{\textendash}519}, abstract = {

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

}, issn = {0948-6968}, doi = {10.3217/jucs-009-06-0510}, url = {http://www.jucs.org/jucs_9_6}, author = {Avrithis, Y. and Stamou, G. and Wallace, M. and Marqu{\'e}s, F. and Salembier, P. and Xavier Gir{\'o}-i-Nieto and Haas, W. and Vallant, H. and Zufferey, M.} } @inbook {bSalembier02, title = {Systems Architecture}, booktitle = {Introduction to the mpeg-7: multimedia content description interface}, year = {2002}, pages = {33{\textendash}42}, publisher = {Wiley}, organization = {Wiley}, edition = {B. S. Manjunath, P. Salembier, T. Sikora (Eds.)}, chapter = {3}, isbn = {0471486787}, author = {Avaro, O. and Salembier, P.} } @article {aAvaro01, title = {MPEG-7 Systems: overview}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {11}, number = {6}, year = {2001}, pages = {760{\textendash}764}, issn = {1051-8215}, author = {Avaro, O. and Salembier, P.} } @article {aTorres97, title = {Stochastic vector quantization of images}, journal = {Signal Processing}, volume = {62}, number = {3}, year = {1997}, month = {11/1997}, pages = {291{\textendash}301}, abstract = {

One of the most important steps in the vector quantization of images is the design of the codebook. The codebook is generally designed using the LBG algorithm, that is in essence a clustering algorithm which uses a large training set of empirical data that is statistically representative of the image to be quantized. The LBG algorithm, although quite effective for practical applications, is computationally very expensive and the resulting codebook has to be recalculated each time the type of image to be encoded changes. One alternative to the generation of the codebook, called stochastic vector quantization, is presented in this paper. Stochastic vector quantization (SVQ) is based on the generation of the codebook according to some previous model defined for the image to be encoded. The well-known AR model has been used to model the image in the current implementations of the technique, and has shown good performance in the overall scheme. To show the merit of the technique in different contexts, stochastic vector quantization is discussed and applied to both pixel-based and segmentation-based image coding schemes.

}, issn = {0165-1684}, doi = {10.1016/S0165-1684(97)00130-8}, author = {Torres, L. and Casas, J. and Arias, E.} } @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 {cSalembier96a, title = {Very low rate video coding using active triangular mesh}, booktitle = {IEEE International Conference on Acoustics, Speech \& Signal Processing, ICASSP 1996}, year = {1996}, pages = {97{\textendash}110}, address = {Atlanta (GA), USA}, isbn = {84-699-1358-1}, author = {Salembier, P. and Ayuso, X.} } @conference {cGasull89a, title = {Automated left ventricular contour extraction and volume calculation form echocardiographic images}, booktitle = {Ultrasonics International 89}, year = {1989}, pages = {1{\textendash}4}, isbn = {SN.}, author = {Gasull, A. and Alquezar, R. and Torres, L. and Sallent, S. and Marqu{\'e}s, F. and Vidal, J. and Sangr{\'a}, E.} }