@article {aWang25, title = {Adaptive Fusion of LiDAR Features for 3D Object Detection in Autonomous Driving}, journal = {Sensors}, volume = {25}, year = {2025}, month = {06/2025}, abstract = {

In the field of autonomous driving, cooperative perception through vehicle-to-vehicle communication significantly enhances environmental understanding by leveraging multi-sensor data, including LiDAR, cameras, and radar. However, traditional early or late fusion methods face challenges such as high bandwidth and computational resources, which make it difficult to balance data transmission efficiency with the accuracy of perception of the surrounding environment, especially for the detection of smaller objects such as pedestrians. To address these challenges, this paper proposes a novel cooperative perception framework based on two-stage intermediate-level sensor feature fusion specifically designed for complex traffic scenarios where pedestrians and vehicles coexist. In such scenarios, the model demonstrates superior performance in detecting small objects like pedestrians compared to mainstream perception methods while also improving the cooperative perception accuracy for medium and large objects such as vehicles. Furthermore, to thoroughly validate the reliability of the proposed model, we conducted both qualitative and quantitative experiments on mainstream simulated and real-world datasets. The experimental results demonstrate that our approach outperforms state-of-the-art perception models in terms of mAP, achieving up to a 4.1\% improvement in vehicle detection accuracy and a remarkable 29.2\% enhancement in pedestrian detection accuracy.

}, doi = {https://doi.org/10.3390/s25133865}, author = {Wang, M. and Li, D. and Casas, J. and Ruiz-Hidalgo, J.} } @conference {cLo Anguera25, title = {Leveraging Quantum Machine Learning for Intrusion Detection in Software-Defined Networks}, booktitle = {IEEE International Conference on Machine Learning for Communication and Networking}, year = {2025}, month = {05/2025}, publisher = {IEEE}, organization = {IEEE}, address = {Barcelona}, abstract = {

Quantum machine learning (QML) algorithms\ for intrusion detection systems in software-defined networks are investigated, and their effectiveness is compared with their classical machine learning methods. The University of Nevada - Reno intrusion detection dataset (UNR-IDD) is used to evaluate different QML models, including quantum k-nearest neighbors (QKNNs), quantum support vector machines (QSVMs), quantum neural networks (QNNs), and hybrid quantum neural networks (HQNNs). These models were tested with quantum simulators to evaluate their potential advantages in processing complex datasets. The results show that HQNN and QSVM have higher accuracy than their classical SVM and NN counterparts. This study shows the potential of leveraging QML to enhance precision. References to other works that dive into efficiency and complexity are included.

}, keywords = {Cybersecurity, Intrusion Detection, Quantum machine learning (QML), Software-Defined Networks (SDN)}, url = {https://icmlcn2025.ieee-icmlcn.org/program/special-sessions}, author = {Joan Lo Anguera and Ruiz-Hidalgo, J. and Sol{\'e}, A. and Casas, J. and Jos{\'e} Antonio L{\'a}zaro and Samuel Sarmiento and Adolfo Ler{\'\i}n} } @conference {cAnguera25, title = {Quantum Machine Learning for Intrusion Detection in Software-Defined Networks}, booktitle = {1st Intl. Workshop on Quantum Computing and Artificial Intelligence (QC+AI 2025) }, year = {2025}, month = {03/2025}, publisher = {Association for the Advancement of Artificial Intelligence (AAAI)}, organization = {Association for the Advancement of Artificial Intelligence (AAAI)}, address = {Philadelphia}, abstract = {

Quantum machine learning (QML) algorithms for intrusion detection systems in software-defined networks are investigated, and their effectiveness is compared with their
classical machine learning methods. The University of Nevada - Reno intrusion detection dataset (UNR-IDD) is used to evaluate different QML models, including quantum k-nearest neighbors (QKNNs), quantum support vector machines (QSVMs), quantum neural networks (QNNs), and hybrid quantum neural networks (HQNNs). These models were tested with quantum simulators to evaluate their potential advantages in processing complex datasets. The results show that HQNN and QSVM have higher accuracy than their classical SVM and NN counterparts.\  This study shows the potential of leveraging QML to enhance precision. References to other works that dive into efficiency and complexity are included.

}, url = {https://sites.google.com/view/qcai2025}, author = {Joan Lo Anguera and Jos{\'e} Antonio L{\'a}zaro and Ruiz-Hidalgo, J. and Sol{\'e}, A. and Casas, J. and Samuel Sarmiento and Adolfo Ler{\'\i}n} } @conference {cLems25a, title = {Towards a multicentric open DigitAL PatHology assIstant beNchmark: Initial Results from the DALPHIN Study}, booktitle = {United States \& Canadian Academy of Pathology 114th Annual Meeting (USCAP 2025)}, year = {2025}, month = {03/2025}, publisher = {Laboratory Investigation, Volume 105, Issue 3, Supplement, 2025, 103609, ISSN 0023-6837}, organization = {Laboratory Investigation, Volume 105, Issue 3, Supplement, 2025, 103609, ISSN 0023-6837}, address = {Boston, USA}, doi = {https://doi.org/10.1016/j.labinv.2024.103609.}, url = {https://www.sciencedirect.com/science/article/pii/S0023683724032872}, author = {Carlijn M Lems and Nat{\'a}lie Klub{\'\i}{\v c}kov{\'a} and Biagio Brattoli and Taebum Lee} } @conference {cLems25, title = {Towards a multicentric open DigitAL PatHology assIstant beNchmark: Initial Results from the DALPHIN Study}, booktitle = {United States \& Canadian Academy of Pathology 114th Annual Meeting (USCAP 2025)}, year = {2025}, month = {03/2025}, publisher = {Laboratory Investigation, Volume 105, Issue 3, Supplement, 2025, 103609, ISSN 0023-6837}, organization = {Laboratory Investigation, Volume 105, Issue 3, Supplement, 2025, 103609, ISSN 0023-6837}, address = {Boston, USA}, doi = {https://doi.org/10.1016/j.labinv.2024.103609.}, url = {https://www.sciencedirect.com/science/article/pii/S0023683724032872}, author = {Carlijn M Lems and Nat{\'a}lie Klub{\'\i}{\v c}kov{\'a} and Biagio Brattoli, and Taebum Lee and Ver{\'o}nica Vilaplana and Pedro Luis Fern{\'a}ndez and Laura Pons and Milda Poceviciute and Nadieh Khalili and Francesco Ciompi} } @article {aManjabacas25, title = {Twenty years of AFORO: New developments and connections enhancing otolith research}, journal = {Fisheries Research}, volume = {281}, year = {2025}, month = {01/2025}, abstract = {

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

}, keywords = {artificial intelligence, computer Vision, deep learning, neural networks, Otoliths}, issn = {0165-7836}, doi = {https://doi.org/10.1016/j.fishres.2024.107242}, author = {A. Manjabacas and O. Chic and E. Garc{\'\i}a-Ladona and V.M. Tuset and Morros, J.R. and Elisa Sayrol and P. Mart{\'\i}-Puig and J. Forest and J. Vasconcelos and A. Lombarte} } @conference {cLazaro24, title = {6G-EWOC: Crowdsourced SLAM Data Fusion for Safe and Efficient ADAS Driving}, booktitle = {IEEE Future Networks World Forum: Symposium on Future Networks for Connected and Automated Mobility }, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Dubai}, abstract = {

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

}, doi = {10.5281/zenodo.14283743}, url = {https://publications.ait.ac.at/en/publications/6g-ewoc-crowdsourced-slam-data-fusion-for-safe-and-efficient-adas}, author = {Jos{\'e} Antonio L{\'a}zaro and Casas, J. and Ruiz-Hidalgo, J. and Cortada-Garcia, M. and Mart{\'\i}n-Pey, G. and Salavedra-Pujol, J. and Wang, M. and Theodoropoulou, E. and Lyberopoulos, G. and Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Jordi Riu and Marcus, C. and Eriksson, O. and Schrenk, B. and F{\`a}brega, J.M.} } @conference {cSchrenk24, title = {6G-EWOC: Optical Wireless Communication in Support of Autonomous Driving}, booktitle = {IEEE Future Networks World Forum: Symposium on Future Networks for Connected and Automated Mobility }, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Dubai}, abstract = {

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

}, doi = {10.5281/zenodo.13904356}, url = {https://publications.ait.ac.at/en/publications/6g-ewoc-optical-wireless-communication-in-support-of-autonomous-d}, author = {Schrenk, B. and Theodoropoulou, E. and Lyberopoulos, G. and Marcus, C. and Eriksson, O. and Jos{\'e} Antonio L{\'a}zaro and Casas, J. and Federico Dios and Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Jordi Riu and F{\`a}brega, J.M.} } @article {aDios24, title = {Experimental evaluation of a MIMO radar performance for ADAS application}, journal = {Telecom}, volume = {5}, year = {2024}, month = {06/2024}, pages = {508-521}, chapter = {508}, abstract = {

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

}, keywords = {advanced driver assistance systems, autonomous vehicles, MIMO radar, radar}, doi = {10.3390/telecom5030026}, author = {Federico Dios and Sergio Torres-Benito and Jos{\'e} Antonio L{\'a}zaro and Casas, J. and Jorge Pinazo and Adolfo Ler{\'\i}n} } @conference {cTheodoropoulou24, title = {Towards AI-Enhanced Fibre-Wireless Optical 6G Network in Support of Connected Mobility}, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Athens}, abstract = {

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

}, doi = {10.5281/zenodo.14246039}, author = {Theodoropoulou, E. and Lyberopoulos, G. and Setaki, Foteini and Pablo Garc{\'\i}a-G{\'o}mez and Santiago Royo and Marcus, C. and Eriksson, O. and F{\`a}brega, J.M. and Ricardo Mart{\'\i}nez and Ramon Casellas and Schrenk, B. and Casas, J. and Jos{\'e} Antonio L{\'a}zaro} } @article {aCumplido-Mayoral22, title = {Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer{\textquoteright}s Disease and Neurodegeneration stratified by sex}, journal = {eLife}, volume = {12}, year = {2023}, month = {04/2023}, abstract = {

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

}, issn = {2050-084X}, doi = {https://doi.org/10.7554/eLife.81067}, author = {Irene Cumplido-Mayoral and Marina Garc{\'\i}a-Prat and Gregory Operto and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Marta Mila-Aloma and Luigi Lorenzini and Carolina Minguillon and Jose Luis Molinuevo and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan Domingo Gispert} } @conference {cLozano23, title = {Optical Phased Array Antenna Apodization for Lidar in Autonomous Vehicles}, booktitle = {XIII Reuni{\'o}n OptoElectr{\'o}nica}, year = {2023}, month = {06/2023}, publisher = {OPTOEL}, organization = {OPTOEL}, address = {Sevilla, Spain}, abstract = {

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

}, url = {https://www.optoel2023.es/94299/section/44205/optoel-2023.html}, author = {Jos{\'e} Lozano and Humberto Jim{\'e}nez and Sergio Torres and Pau Biosca and Bernat Fontanet and Jorge Pinazo and Adolfo Ler{\'\i}n and Federico Dios and Casas, J. and Jos{\'e} Antonio L{\'a}zaro} } @conference {cCumplido-Mayoral22, title = {Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer{\textquoteright}s disease and neurodegeneration stratified by sex}, booktitle = {15th Clinical Trials on Alzheimer{\textquoteright}s Disease Conference (CTAD)}, year = {2022}, month = {11/2022}, address = {San Francisco, USA}, author = {Irene Cumplido-Mayoral and Marta Mila-Aloma and Luigi Lorenzini and Alle Meije Wink and H. Mutsaerts and Sven Haller and Ga{\"e}l Chetelat and Frederik Barkhof and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @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} } @conference {cPinazo22, title = {Perception in the era of Autonomous Vehicles}, booktitle = {Photonics 4 Smart Cities, SCEWC 2022}, year = {2022}, month = {11/2022}, publisher = {Photonics21}, organization = {Photonics21}, address = {Barcelona}, url = {https://www.fotonica21.org/photonics-4-smart-cities}, author = {Jorge Pinazo and Adolfo Ler{\'\i}n and Francesc Xavier de Gibert and {\'A}lvaro Moliner and Daniel Sevilla and Antonio Jurado and Iv{\'a}n R{\'\i}os and Rodrigo Jerez and Jaime Santiago and {\'A}lvaro Linuesa and Antonio Cano and Federico Dios and Adolfo Comer{\'o}n and Casas, J. and Jos{\'e} Antonio L{\'a}zaro} } @conference {cBartusiak, title = {Predicting Dog Phenotypes from Genotypes}, booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}22)}, year = {2022}, abstract = {

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

}, doi = {https://doi.org/10.1101/2022.04.13.488108 }, author = {Bartusiak, Emily and Barrab{\'e}s, M{\'\i}riam and Aigerim Rymbe and J{\'u}lia Gimbernat and Cayetana L{\'o}pez and Lorenzo Barberis and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cMayoral21b, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using structural MRI}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Silvia Ingala and Luigi Lorenzini and Alle Meije Wink and Sven Haller and Jose Luis Molinuevo and Robin Wolz and Alessandro Palombit and Adam J Schwarz and Ga{\"e}l Chetelat and Pierre Payoux and Pablo Martinez-Lage and Giovanni Frisoni and Nick C Fox and Craig W Ritchie and Joanna M Wardlaw and Adam Waldman and Frederik Barkhof and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cManas, title = {Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data}, booktitle = {International Conference in Computer Vision (ICCV)}, year = {2021}, publisher = {IEEE/CVF}, organization = {IEEE/CVF}, address = {Virtual}, abstract = {

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

}, url = {https://arxiv.org/abs/2103.16607}, author = {Ma{\~n}as,Oscar and Lacoste, Alexandre and Xavier Gir{\'o}-i-Nieto and Vazquez, David and Rodr{\'\i}guez, Pau} } @article {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 {cBernal, title = {Transcription-Enriched Joint Embeddings or Spoken Descriptions of Images and Videos}, booktitle = {CVPR 2020 Workshop on Egocentric Perception, Interaction and Computing}, year = {2020}, month = {06/2020}, publisher = {arXiv}, organization = {arXiv}, address = {Seattle, WA, USA}, abstract = {

In this work, we propose an effective approach for training unique embedding representations by combining three simultaneous modalities: image and spoken and textual narratives. The proposed methodology departs from a baseline system that spawns a embedding space trained with only spoken narratives and image cues. Our experiments on the EPIC-Kitchen and Places Audio Caption datasets show that introducing the human-generated textual transcriptions of the spoken narratives helps to the training procedure yielding to get better embedding representations. The triad speech, image and words allows for a better estimate of the point embedding and show an improving of the performance within tasks like image and speech retrieval,\ even when text third modality, text, is not present in the task.

Transcription-Enriched Joint Embeddings for Spoken Descriptions of Images and Videos from Universitat Polit{\`e}cnica de Catalunya
}, author = {Oriol, Benet and Luque, J. and Diego, Ferran and Xavier Gir{\'o}-i-Nieto} } @conference {cLopez-Palmab, title = {Audience measurement using a top-view camera and oriented trajectories}, booktitle = {IEEE IECON 2019}, year = {2019}, month = {10/2019}, address = {Lisbon, Portugal}, abstract = {

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

}, keywords = {attention time, depth sensor, top-view camera, tracking}, doi = {https://doi.org/10.1109/IECON.2019.8927222}, author = {Manuel L{\'o}pez-Palma and Morros, J.R. and Montserrat Corbal{\'a}n and Javier Gago} } @article {aWang19, title = {Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge}, journal = {IEEE Transactions on Medical Imaging}, year = {2019}, month = {2019/2/27}, abstract = {

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

}, doi = {10.1109/TMI.2019.2901712}, author = {Li Wang and Dong Nie and Guannan Li and Elodie Puybareau and Jose Dolz and Qian Zhang and Fan Wang and Jing Xia and Zhengwang Wu and Jiawei Chen and Kim-HanThung and Toan Duc Bui and Jitae Shin and Guodong Zeng and Guoyan Zheng and Vladimir S. Fonov and Andrew Doyle and Yongchao Xu and Pim Moeskops and Josien Pluim and Christian Desrosiers and Ismail Ben Ayed and Gerard Sanroma and Oualid Benkarim and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Weili Lin and Gang Li and Dinggang Shen} } @article {aLin19, title = {Depth Estimation and Semantic Segmentation from a Single RGB Image Using a Hybrid Convolutional Neural Network}, journal = {Sensors}, volume = {19}, year = {2019}, month = {04/2019}, chapter = {1795}, abstract = {

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

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

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

}, keywords = {Convolutional Neural Network, Instance Segmentation, One Shot Learning}, isbn = {978-989-758-354-4}, doi = {10.5220/0007259902330239}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @article {xLLados19, title = {The pillars of the Computer Vision Catalan Alliance}, year = {2019}, abstract = {

Panel at the 6th Annual Catalan Meeting on Computer Vision.

$\#$ACM2019 @JosepLlados now introducing CVCatalliance with @xantallavina and experts from different sectors: @Vilynx @Satellogic @osalap @TheCollider_MWC @DocXavi pic.twitter.com/o5tVWW0gMQ

{\textemdash} CVC_UAB (@CVC_UAB) September 17, 2019
}, author = {LLad{\'o}s, Josep and Bou, Elisenda and Bressan, Marco and Sala, Oscar and Xavier Gir{\'o}-i-Nieto} } @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 {aSalembier18, title = {Ship Detection in SAR Images Based on Maxtree Representation and Graph Signal Processing}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {57}, year = {2019}, month = {05/2019}, pages = {2709 - 2724}, author = {Salembier, P. and Liesegang, S. and L{\'o}pez-Mart{\'\i}nez, C.} } @conference {cLinardosa, title = {Simple vs complex temporal recurrences for video saliency prediction}, booktitle = {British Machine Vision Conference (BMVC)}, year = {2019}, month = {09/2019}, publisher = {British Machine Vision Association}, organization = {British Machine Vision Association}, address = {Cardiff, Wales / UK.}, abstract = {

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

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

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

}, keywords = {brain, Evaluation and performance, Magnetic resonance imaging (MRI), segmentation}, issn = {0278-0062}, doi = {10.1109/TMI.2019.2905770}, author = {Hugo Kuijf and Matthijs Biesbroek and Jeroen de Bresser and Rutger Heinen and Simon Andermatt and Mariana Bento and Matt Berseth and Mikhail Belyaev and Jorge Cardoso and Adri{\`a} Casamitjana and Louis Collins and Mahsa Dadar and Achileas Georgiou and Mohsen Ghafoorian and Dakai Jin and April Khademi and Jesse Knight and Hongwei Li and Xavier Llado and Miguel Luna and Qaiser Mahmood and Richard McKinley and Alireza Mehrtash and Sebastien Ourselin and Bo-yong Park and Hyunkin Park and Sang Hyun Park and Simon Pezold and Elodie Puybareau and Leticia Rittner and Carole Sudre and Sergi Valverde and Ver{\'o}nica Vilaplana and Rolan Wiest and Yongchao Xu and Ziyue Xu and Guodong Zeng and Jianguo Zhang and Guoyan Zheng and Christoper Chen and Wiesje van der Flier and Frederik Barkhof and Max Viergever and Geert Jan Biessels} } @conference {cSanchez-Escobedo, title = {HybridNet for Depth Estimation and Semantic Segmentation}, booktitle = {ICASSP 2018}, year = {2018}, month = {04/2018}, publisher = {IEEE}, organization = {IEEE}, address = {Calgary, Alberta, Canada}, abstract = {

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

}, doi = {10.1109/ICASSP.2018.8462433}, url = {https://ieeexplore.ieee.org/abstract/document/8462433}, author = {D. Sanchez-Escobedo and X. Lin and Casas, J. and M. Pard{\`a}s} } @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 {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} } @conference {cLopez-Palma, title = {Oriented trajectories as a method for audience measurement}, booktitle = {27th International Symposium on Industrial Electronics (ISIE)}, year = {2018}, month = {06/2018}, publisher = {IEEE}, organization = {IEEE}, address = {Cairns, Australia}, abstract = {

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

}, doi = {10.1109/ISIE.2018.8433615}, author = {Manuel L{\'o}pez-Palma and Morros, J.R. and Javier Gago and Montserrat Corbal{\'a}n} } @phdthesis {dLin18, title = {Semantic and Generic Object Segmentation for Scene Analysis Using RGB-D Data}, year = {2018}, month = {07/2018}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {PhD}, abstract = {

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

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

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

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

}, url = {http://hdl.handle.net/10803/620762}, author = {X. Lin}, editor = {Casas, J. and M. Pard{\`a}s} } @conference {cLinardos, title = {Temporal Saliency Adaptation in Egocentric Videos}, booktitle = {ECCV 2018 Workshop on Egocentric Perception, Interaction and Computing}, year = {2018}, month = {07/2018}, publisher = {Extended abstract}, organization = {Extended abstract}, address = {Munich, Germany}, abstract = {

}, author = {Linardos, Panagiotis and Mohedano, Eva and Chert{\'o}, M{\`o}nica and Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @article {aLin, title = {Temporally Coherent 3D Point Cloud Video Segmentation in Generic Scenes}, journal = {IEEE Transactions on Image Processing}, volume = {27}, year = {2018}, month = {06/2018}, pages = {3087 - 3099}, abstract = {

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

}, issn = {1057-7149}, doi = {10.1109/TIP.2018.2811541}, url = {https://ieeexplore.ieee.org/document/8306148/}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @conference {cLopez-Palmaa, title = {Who watches the watchers? Quality control of the human inspection in production lines using Visual Intensity of Attention}, booktitle = {SAAEI 2018}, year = {2018}, month = {07/2018}, address = {Barcelona}, abstract = {

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

}, author = {Manuel L{\'o}pez-Palma and Morros, J.R. and Javier Gago and Montserrat Corbal{\'a}n} } @conference {cLinb, title = {3D Point Cloud Segmentation Using a Fully Connected Conditional Random Field}, booktitle = {The 25th European Signal Processing Conference (EUSIPCO 2017)}, year = {2017}, month = {08/2017}, publisher = {Eurasip/IEEE}, organization = {Eurasip/IEEE}, address = {Kos island, Greece}, abstract = {

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

}, keywords = {Image color analysis, Image segmentation, Labeling, Object segmentation, Three-dimensional displays, Two dimensional displays}, doi = {10.23919/EUSIPCO.2017.8081170}, url = {https://www.eusipco2017.org/}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @article {aMartinez17, title = {A closed-loop approach for tracking a humanoid robot using particle filtering and depth data}, journal = {Intelligent Service Robotics}, volume = {10}, year = {2017}, month = {10/2017}, pages = {297{\textendash}312}, abstract = {

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

}, keywords = {Humanoid robot, ~Particle Filter, ~RGB-D sensor, ~ROS, ~Tracking}, issn = {1861-2776}, doi = {10.1007/s11370-017-0230-0}, url = {http://rdcu.be/s1RE}, author = {Mart{\'\i}nez, P. A. and X. Lin and Castel{\'a}n, M and Casas, J. and Arechavaleta, G.} } @conference {cLina, title = {Disentangling Motion, Foreground and Background Features in Videos}, booktitle = {CVPR 2017 Workshop Brave New Motion Representations}, year = {2017}, month = {05/2017}, abstract = {

This paper instroduces an unsupervised framework to extract semantically rich features for video representation. Inspired by how the human visual system groups objects based on motion cues, we propose a deep convolutional neural network that disentangles motion, foreground and background information. The proposed architecture consists of a 3D convolutional feature encoder for blocks of 16 frames, which is trained for reconstruction tasks over the first and last frames of the sequence. The model is trained with a fraction of videos from the UCF-101 dataset taking as ground truth the bounding boxes around the activity regions. Qualitative results indicate that the network can successfully update the foreground appearance based on pure-motion features. The benefits of these learned features are shown in a discriminative classification task when compared with a random initialization of the network weights, providing a gain of accuracy above the 10\%.

Disentangle motion, Foreground and Background Features in Videos from Xavier Giro-i-Nieto
}, author = {Lin, Xunyu and V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto and Jordi Torres and Cristian Canton-Ferrer} } @inbook {bBellot17, title = {Efficient Combination of Pairwise Feature Networks}, booktitle = {Neural Connectomics Challenge}, year = {2017}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, chapter = {7}, issn = {978-3-319-53069-7}, doi = {10.1007/978-3-319-53070-3}, author = {P Bellot and Patrick Meyer}, editor = {Battaglia, D and Guyon, I. and Lemaire, V. and Orlandi, J. and Ray, B. and Soriano, J.} } @conference {xLidona, title = {Semantic Summarization of Egocentric Photo Stream Events}, booktitle = {ACM Multimedia 2017 Workshop on Lifelogging Tools and Applications}, year = {2017}, month = {10/2017}, publisher = {ACM}, organization = {ACM}, address = {Mountain View, CA, USA}, abstract = {

With the rapid increase of users of wearable cameras in recent years and of the amount of data they produce, there is a strong need for automatic retrieval and summarization techniques. This work addresses the problem of automatically summarizing egocentric photo streams captured through a wearable camera by taking an image retrieval perspective. After removing non-informative images by a new CNN-based filter, \ images are ranked by relevance to ensure semantic diversity and \ finally re-ranked by a novelty criterion to reduce redundancy. \ To assess the results, a new evaluation metric is proposed which takes into account the non-uniqueness of the solution. Experimental results applied on a database of 7,110 images from 6 different subjects and evaluated by experts gave 95.74\% of experts satisfaction and a Mean Opinion Score of 4.57 out of 5.0.

}, doi = {10.1145/3133202.3133204}, url = {https://dl.acm.org/citation.cfm?id=3133204}, author = {Lidon, Aniol and Bola{\~n}os, Marc and Dimiccoli, M. and Radeva, Petia and Garolera, Maite and Xavier Gir{\'o}-i-Nieto} } @conference {cLuque17, title = {Spatio-Temporal Road Detection from Aerial Imagery using CNNs}, booktitle = {International Conference on Computer Vision Theory and Applications}, year = {2017}, month = {2/2017}, address = {Porto, Portugal}, abstract = {

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

}, doi = {10.5220/0006128904930500}, author = {Luque, B. and Morros, J.R. and Ruiz-Hidalgo, J.} } @conference {cLe, title = {Towards large scale multimedia indexing: A case study on person discovery in broadcast news}, booktitle = {International Workshop on Content-Based Multimedia Indexing - CBMI 2017}, year = {2017}, month = {06/2017}, address = {Firenze, Italy}, abstract = {

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

}, doi = {https://doi.org/10.1145/3095713.3095732}, author = {Nam Le and Herv{\'e} Bredin and Gabriel Sargent and Miquel India and Paula Lopez-Otero and Claude Barras and Camille Guinaudeau and Guillaume Gravier and Gabriel Barbosa da Fonseca and Izabela Lyon Freire and Zenilton Patroc{\'\i}nio Jr. and Silvio Jamil F. Guimaraes and Gerard Mart{\'\i} and Morros, J.R. and Javier Hernando and Laura Docio-Fernandez and Carmen Garcia-Mateo and Sylvain Meignier and Jean-Marc Odobez} } @conference {cLin16a, title = {3D Point Cloud Segmentation Oriented to The Analysis of Interactions}, booktitle = {The 24th European Signal Processing Conference (EUSIPCO 2016)}, year = {2016}, month = {08/2016}, publisher = {Eurasip}, organization = {Eurasip}, address = {Budapest, Hungary}, abstract = {

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

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

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

}, issn = {978-3-319-49409-8}, doi = {10.1007/978-3-319-49409-8}, url = {http://eecs.oregonstate.edu/IWVS_workshop/}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @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.} } @conference {cLin16, title = {Graph based Dynamic Segmentation of Generic Objects in 3D}, booktitle = {CVPR SUNw: Scene Understanding Workshop}, year = {2016}, month = {06/2016}, address = {Las Vegas, US}, abstract = {

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

}, url = {http://sunw.csail.mit.edu/posters.html}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @conference {cdeOliveira-Barraa, title = {Large Scale Content-Based Video Retrieval with LIvRE}, booktitle = {14th International Workshop on Content-based Multimedia Indexing (CBMI)}, year = {2016}, month = {06/2016}, publisher = {IEEE}, organization = {IEEE}, address = {Bucharest, Romania}, abstract = {

The fast growth of video data requires robust, efficient, and scalable systems to allow for indexing and retrieval. These systems must be accessible from lightweight, portable and usable interfaces to help users in management and search of video content. This demo paper presents LIvRE, an extension of an existing open source tool for image retrieval to support video indexing. LIvRE consists of three main system components (pre-processing, indexing and retrieval), as well as a scalable and responsive HTML5 user interface accessible from a web browser. LIvRE supports image-based queries, which are efficiently matched with the extracted frames of the indexed videos.

}, author = {Gabriel de Oliveira-Barra and Lux, Mathias and Xavier Gir{\'o}-i-Nieto} } @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 {xBarra, title = {LIvRE: A Video Extension to the LIRE Content-Based Image Retrieval System}, year = {2015}, abstract = {

Advisors:\ Mathias Lux\ (Klagenfurt University) and\ Xavier Gir{\'o}-i-Nieto\ (UPC)

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

Grade: A (9.0/10.0)

This project explores the expansion of Lucene Image Retrieval Engine (LIRE), an open-source Content-Based Image Retrieval (CBIR) system, for video retrieval on large scale video datasets. The fast growth of the need to store huge amounts of video in servers requires efficient, scalable search and indexing engines capable to assist users in their management and retrieval. In our tool, queries are formulated by visual examples allowing users to find the videos and the moment of time when the query image is matched with. The video dataset used on this scenario comprise over 1,000 hours of different news broadcast channels. This thesis presents an extension and adaptation of Lire and its plugin for Solr, an open-source enterprise search platform from the Apache Lucene project, for video retrieval based on visual features, as well as a web-interface for users from different devices.

LIvRE: A Video Extension to the LIRE Content-Based Image Retrieval System from Xavier Giro
}, url = {http://upcommons.upc.edu/handle/2117/79052}, author = {Gabriel de Oliveira-Barra}, editor = {Xavier Gir{\'o}-i-Nieto and Lux, Mathias} } @article {aBonet-Carne14a, title = {Quantitative Ultrasound Texture Analysis of Fetal Lungs To Predict Neonatal Respiratory Morbidity}, journal = {Ultrasound in Obstetrics and Gynecology, Wiley}, volume = {45}, year = {2015}, pages = {427{\textendash}433}, author = {E. Bonet-Carne and M. Palacio and T. Cobo and A. Perez-Moreno and M. Lopez and J. P. Piraquive and J. C. Ramirez and F. Marques and E. Gratacos} } @mastersthesis {xLopez15, title = {Reconstrucci{\'o} de la forma del rostre a partir de contorns}, year = {2015}, abstract = {

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

}, author = {Laia del Pino L{\'o}pez}, editor = {Ver{\'o}nica Vilaplana and Josep Ramon Morros} } @mastersthesis {xLidon, title = {Semantic and Diverse Summarization of Egocentric Photo Events}, year = {2015}, abstract = {

Student: Aniol Lidon

Advisors: Petia Radeva (UB) and Xavier Gir{\'o}-i-Nieto (UPC)

Program: Master in Computer Vision

Grade: A (9.8/10.0)

This project generates visual summaries of events depicted from egocentric photos taken with a wearable camera. These summaries are addressed to mild-dementia patients in order to exercise their memory in a daily base. The main contribution is an iterative approach that guarantees the semantic diversity of the summary and a novel soft metric to assess subjective results. Medical experts validated the proposed solution with a Mean Opinion Score of 4.6 out of of 5.0. The flexibility and quality of the solution was also tested in the 2015 Retrieving Diverse Social Images Task from the scientific international benchmark, MediaEval.\ 

Semantic and Diverse Summarization of Egocentric Photo Events from Xavier Giro
Aniol Lidon Master Thesis
}, author = {Lidon, Aniol}, editor = {Radeva, Petia and Xavier Gir{\'o}-i-Nieto} } @conference {cLin15, title = {Time consistent estimation of End-effectors from RGB-D data}, booktitle = {Image and Video Technology: 7th Pacific-Rim Symposium, PSIVT 2015, Auckland, New Zealand, November 25-27, 2015, Revised Selected Papers}, number = {9431}, year = {2015}, month = {12/2015}, pages = {529-543}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

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

}, keywords = {End-effector estimation; Time coherence; Topology representation}, isbn = {978-3-319-29451-3}, doi = {10.1007/978-3-319-29451-3_42}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @conference {cLidon, title = {UPC-UB-STP @ MediaEval 2015 Diversity Task: Iterative Reranking of Relevant Images}, booktitle = {MediaEval 2015 Workshop}, year = {2015}, month = {09/2015}, abstract = {

This paper presents the results of the UPC-UB-STP team in the 2015 MediaEval Retrieving Diverse Images Task.The goal of the challenge is to provide a ranked list of Flickr photos for a predefined set of queries. Our approach firstly generates a ranking of images based on a query-independent estimation of its relevance. Only top results are kept and iteratively re-ranked based on their intra-similarity to introduce diversity.

UPC-UB-STP @ MediaEval 2015 diversity task iterative reranking of relevant images from Xavier Giro
}, url = {http://ceur-ws.org/Vol-1436/}, author = {Lidon, Aniol and Bola{\~n}os, Marc and Seidl, Markus and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Zeppelzauer, Matthias} } @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} } @mastersthesis {xRoldan-Carlos, title = {Visual Search for Musical Performances and Endoscopic Videos}, year = {2015}, abstract = {

Advisors: Mathias Lux (Klagenfurt University) and\ Xavier Gir{\'o}-i-Nieto\ (UPC)

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

Grade: A (9.5/10)

This project explores the potential of LIRE, an en existing Content-Based Image Retrieval (CBIR) system, when used to retrieve medical videos. These videos are recording of the live streams used by surgeons during the endoscopic procedures, captured from inside of the subject. The growth of such video content stored in servers requires search engines capable to assist surgeons in their management and retrieval. In our tool, queries are formulated by visual examples and those allow surgeons to re-find shots taken during the procedure. This thesis presents an extension and adaptation of Lire for video retrieval based on visual features and late fusion. The results are assessed from two perspectives: a quantitative and qualitative one. While the quantitative one follows the standard practices and metrics for video retrieval, the qualitative assessment has been based on an empirical social study using a semi-interactive web-interface. In particular, a thinking aloud test was applied to analyze if the user expectations and requirements were fulfilled. Due to the scarcity of surgeons available for the qualitative tests, a second domain was also addressed: videos captured at musical performances. These type of videos has also experienced an exponential growth with the advent of affordable multimedia smart phones, available to a large audience. Analogously to the endoscopic videos, searching in a large data set of such videos is a challenging topic.

}, url = {http://hdl.handle.net/2099.1/26032}, author = {Roldan-Carlos, Jennifer}, editor = {Xavier Gir{\'o}-i-Nieto and Lux, Mathias} } @conference {cSalembier14, title = {Low-level processing of PolSAR images with binary partition trees}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2014}, year = {2014}, month = {07/2014}, publisher = {IEEE}, organization = {IEEE}, address = {Quebec, Canada}, abstract = {

This paper discusses the interest of Binary Partition Trees (BPTs) and the usefulness of graph cuts for low-level processing of PolSAR images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for many applications including filtering, segmentation, classification and object detection. Many processing strategies consist in populating the tree with a specific feature and in applying a graph-cut called pruning. Different graph-cuts are discussed and analyzed in the context of PolSAR images for speckle filtering and segmentation.

}, author = {Salembier, P. and S. Foucher and L{\'o}pez-Mart{\'\i}nez, C.} } @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.} } @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 {aBonet-Carne14, title = {Quantitative Ultrasound Texture Analysis of Fetal Lungs To Predict Neonatal Respiratory Morbidity}, journal = {Ultrasound in Obstetrics and Gynecology, Wiley}, volume = {44}, year = {2014}, author = {E. Bonet-Carne and M. Palacio and T. Cobo and A. Perez-Moreno and M. Lopez and J. P. Piraquive and J. C. Ramirez and F. Marques and E. Gratacos} } @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.} } @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} } @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.} } @inbook {bLeon13, title = {Region-based caption text extraction}, booktitle = {Lecture Notes in Electrical Engineering (Analysis, Retrieval and Delivery of Multimedia Content)}, volume = {158}, year = {2013}, month = {07/2012}, pages = {21-36}, publisher = {Springer}, organization = {Springer}, address = {New York}, abstract = {

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

}, keywords = {Text detection}, isbn = {978-1-4614-3830-4}, doi = {10.1007/978-1-4614-3831-1_2}, author = {Le{\'o}n, M. and Ver{\'o}nica Vilaplana and Gasull, A. and Marqu{\'e}s, F.} } @mastersthesis {xTort13, title = {Video Clustering Using Camera Motion}, year = {2013}, abstract = {

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

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

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

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

}, url = {http://hdl.handle.net/2099.1/17337}, author = {Tort, Laura}, editor = {Xavier Gir{\'o}-i-Nieto and Rombaut, Mich{\`e}le and Pellerin, Denis} } @phdthesis {dLopez-Mendez12, title = {Articulated Models for Human Motion Analysis}, year = {2012}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

Human motion analysis is as a broad area of computer vision that has strongly attracted the interest of researchers in the last decades. Motion analysis covers topics such as human motion tracking and estimation, action and behavior recognition or segmentation of human motion. All these fields are challenging due to different reasons, but mostly because of viewing perspectives, clutter and the imprecise semantics of actions and human motion. The computer vision community has addressed human motion analysis from several perspectives. Earlier approaches often relied on articulated human body models represented in the three-dimensional world. However, due to the traditionally high difficulty and cost of estimating such an articulated structure from video, research has focus on the development of human motion analysis approaches relying on low-level features. Although obtaining impressive results in several tasks, low-level features are typically conditioned by appearance and viewpoint, thus making difficult their application on different scenarios. Nonetheless, the increase in computational power, the massive availability of data and the irruption of consumer-depth cameras is changing the scenario, and with that change human motion analysis through articulated models can be reconsidered. Analyzing and understanding of human motion through 3-dimensional information is still a crucial issue in order to obtain richer models of dynamics and behavior. In that sense, articulated models of the human body offer a compact and view-invariant representation of motion that can be used to leverage motion analysis. In this dissertation, we present several approaches for motion analysis. In particular, we address the problem of pose inference, action recognition and temporal clustering of human motion. Articulated models are the leitmotiv in all the presented approaches. Firstly, we address pose inference by formulating a layered analysis-by-synthesis framework where models are used to generate hypothesis that are matched against video. Based on the same articulated representation upon which models are built, we propose an action recognition framework. Actions are seen as time-series observed through the articulated model and generated by underlying dynamical systems that we hypothesize that are generating the time-series. Such an hypothesis is used in order to develop recognition methods based on time-delay embeddings, which are analysis tools that do not make assumptions on the form of the form of the underlying dynamical system. Finally, we propose a method to cluster human motion sequences into distinct behaviors, without a priori knowledge of the number of actions in the sequence. Our approach relies on the articulated model representation in order to learn a distance metric from pose data. This metric aims at capturing semantics from labeled data in order to cluster unseen motion sequences into meaningful behaviors. The proposed approaches are evaluated using publicly available datasets in order to objectively measure our contributions.

}, url = {http://hdl.handle.net/10803/112124}, author = {L{\'o}pez-M{\'e}ndez, A.}, editor = {Casas, J.} } @conference {cLopez-Mendez12, title = {Can our TV robustly understand human gestures? Real-Time Gesture Localization in Range Data}, booktitle = {Conference on Visual Media Production}, year = {2012}, month = {12/2012}, publisher = {ACM}, organization = {ACM}, address = {London, UK}, abstract = {

The {\textquoteright}old{\textquoteright} remote falls short of requirements when confronted with digital convergence for living room displays. Enriched options to watch, manage and interact with content on large displays demand improved means of interaction. Concurrently, gesture recognition is increasingly present in human-computer interaction for gaming applications. In this paper we propose a gesture localization framework for interactive display of audio-visual content. The proposed framework works with range data captured from a single consumer depth camera. We focus on still gestures because they are generally user friendly (users do not have to make complex and tiring movements) and allow formulating the problem in terms of object localization. Our method is based on random forests, which have shown an excellent performance on classification and regression tasks. In this work, however, we aim at a specific class of localization problems involving highly unbalanced data: positive examples appear during a small fraction of space and time. We study the impact of this natural unbalance on the random forest learning and we propose a framework to robustly detect gestures on range images in real applications. Our experiments with offline data show the effectiveness of our approach. We also present a real-time application where users can control the TV display with a reduced set of still gestures.

}, isbn = {978-1-4503-1311-7/12/11}, doi = {10.1145/2414688.2414691}, url = {http://www.cvmp-conference.org/2012-Papers}, author = {L{\'o}pez-M{\'e}ndez, A. and Casas, J.} } @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.} } @article {bSuau12, title = {INTAIRACT: Joint Hand Gesture and Fingertip Classification for Touchless Interaction}, journal = {Lecture notes in computer science}, volume = {7585}, year = {2012}, month = {10/2012}, pages = {602-606}, publisher = {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.} } @conference {cLopez-Mendez12, title = {Metric Learning from Poses for Temporal Clustering of Human Motion}, booktitle = {British Machine Vision Conference 2012}, year = {2012}, month = {09/2012}, address = {Guildford, UK}, abstract = {

Temporal clustering of human motion into semantically meaningful behaviors is a challenging task. While unsupervised methods do well to some extent, the obtained clusters often lack a semantic interpretation. In this paper, we propose to learn what makes a sequence of human poses different from others such that it should be annotated as an action. To this end, we formulate the problem as weakly supervised temporal clustering for an unknown number of clusters. Weak supervision is attained by learning a metric from the implicit semantic distances derived from already annotated databases. Such a metric contains some low-level semantic information that can be used to effectively segment a human motion sequence into distinct actions or behaviors. The main advantage of our approach is that metrics can be successfully used across datasets, making our method a compelling alternative to unsupervised methods. Experiments on publicly available mocap datasets show the effectiveness of our approach.

}, doi = {10.5244/C.26.49}, author = {L{\'o}pez-M{\'e}ndez, A. and Gall, J. and Casas, J. and van Gool, L.} } @article {aLopez-Mendez12, title = {Model-Based Recognition of Human Actions by Trajectory Matching in Phase Spaces}, journal = {Image and Vision Computing}, year = {2012}, abstract = {

This paper presents a human action recognition framework based on the theory of nonlinear dynamical systems. The ultimate aim of our method is to recognize actions from multi-view video. We estimate and represent human motion by means of a virtual skeleton model providing the basis for a view-invariant representation of human actions. Actions are modeled as a set of weighted dynamical systems associated to different model variables. We use time-delay embeddings on the time series resulting of the evolution of model variables along time to reconstruct phase portraits of appropriate dimensions. These phase portraits characterize the underlying dynamical systems. We propose a distance to compare trajectories within the reconstructed phase portraits. These distances are used to train SVM models for action recognition. Additionally, we propose an efficient method to learn a set of weights reflecting the discriminative power of a given model variable in a given action class. Our approach presents a good behavior on noisy data, even in cases where action sequences last just for a few frames. Experiments with marker-based and markerless motion capture data show the effectiveness of the proposed method. To the best of our knowledge, this contribution is the first to apply time-delay embeddings on data obtained from multi-view video.

}, keywords = {action recognition}, issn = {0262-8856}, doi = {10.1016/j.imavis.2012.06.007}, url = {http://www.sciencedirect.com/science/article/pii/S0262885612000959?v=s5}, author = {L{\'o}pez-M{\'e}ndez, A. and Casas, J.} } @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.} } @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 {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 {aTello11, title = {Edge enhancement algorithm based on the wavelet transform for automatic edge detection in SAR images}, journal = {IEEE transactions on geoscience and remote sensing}, volume = {49}, number = {1}, year = {2011}, pages = {222{\textendash}235}, issn = {0196-2892}, doi = {10.1109/TGRS.2010.2052814}, url = {http://hdl.handle.net/2117/11057}, author = {Tello, M. and L{\'o}pez-Mart{\'\i}nez, C. and Mallorqui, J.J. and Salembier, P.} } @conference {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.} } @inbook {bWaibel10, title = {Computers in the Human Interaction Loop}, booktitle = {Handbook on Ambient Intelligence and Smart Environments (AISE)}, year = {2010}, pages = {1071{\textendash}1116}, publisher = {Springer}, organization = {Springer}, address = {Boston, MA}, abstract = {

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

}, isbn = {978-0-387-93807-3}, doi = {10.1007/978-0-387-93808-0_40}, author = {Waibel, A. and Stiefelhagen, R. and Carlson, R. and Casas, J. and Kleindienst, J. and Lamel, L. and Lanz, O. and Mostefa, D. and Omologo, M. and Pianesi, F. and Polymenakos, L. and Potamianos, G. and Soldatos, J. and Sutschet, G. and Terken, J.} } @conference {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 {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 {cVilaplana10, title = {Object detection and segmentation on a hierarchical region-based image representation}, booktitle = {2010 IEEE International Conference on Image Processing}, year = {2010}, pages = {3393{\textendash}3396}, isbn = {0-8194-2103-0}, url = {http://hdl.handle.net/2117/11435}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F. and Le{\'o}n, M. and Gasull, A.} } @conference {cLeon10, title = {Region-based caption text extraction}, booktitle = {11th. International Workshop on Image Analysis for Multimedia Application Services}, year = {2010}, pages = {1{\textendash}4}, url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5608542}, author = {Le{\'o}n, M. and Ver{\'o}nica Vilaplana and Gasull, A. and Marqu{\'e}s, F.} } @conference {cLopez-Mendez10, title = {Virtual view appearance representation for human motion analysis in multi-view environments}, booktitle = {18th European Signal Processing Conference}, year = {2010}, pages = {959{\textendash}963}, isbn = {2076-1465}, url = {http://hdl.handle.net/2117/8747}, author = {L{\'o}pez-M{\'e}ndez, A. and Cristian Canton-Ferrer and Casas, J.} } @inbook {bNickel09, title = {Activity Classification}, booktitle = {Computers in the Human Interaction Loop}, year = {2009}, pages = {107{\textendash}119}, publisher = {Springer}, organization = {Springer}, address = {London}, abstract = {

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

}, isbn = {978-1-84882-053-1}, doi = {10.1007/978-1-84882-054-8}, url = {http://www.springerlink.com/content/tj2x827107563616/}, author = {Nickel, K. and M. Pard{\`a}s and Stiefelhagen, R. and Cristian Canton-Ferrer and Landabaso, J. and Casas, J.} } @conference {cLeon09, title = {Caption text extraction for indexing purposes using a hierarchical region-based image model}, booktitle = {16th International Conference on Image Processing}, year = {2009}, pages = {1869{\textendash}1872}, isbn = {978-1-4244-5655-0}, doi = {http://dx.doi.org/10.1109/ICIP.2009.5413607}, url = {http://hdl.handle.net/2117/7940}, author = {Le{\'o}n, M. and Ver{\'o}nica Vilaplana and Gasull, A. and Marqu{\'e}s, F.} } @conference {cLopez-Mendez09, title = {Feature-based annealing particle filter for robust body pose estimation}, booktitle = {Fourth International Conference on Computer Vision Theory and Applications}, year = {2009}, pages = {438{\textendash}443}, abstract = {

This paper presents a new annealing method for particle filtering in the context of body pose estimation. The feature-based annealing is inferred from the weighting functions obtained with common image features used for the likelihood approximation. We introduce a complementary weighting function based on the foreground extraction and we balance the different measures through the annealing layers in order to improve the posterior estimate. This technique is applied to estimate the upper body pose of a subject in a realistic multi-view environment. Comparative results between the proposed method and the common annealing strategy are presented to assess the robustness of the algorithm.

}, doi = {10.5220/0001783404380443}, url = {http://visapp.visigrapp.org/Abstracts/2009/VISAPP_2009_Abstracts.htm}, author = {L{\'o}pez-M{\'e}ndez, A. and Casas, J.} } @inbook {bBernardin09, title = {Person Tracking}, booktitle = {Computers in the human interaction loop}, year = {2009}, pages = {11{\textendash}22}, publisher = {Springer}, organization = {Springer}, address = {London}, abstract = {

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

}, isbn = {978-1-84882-053-1}, doi = {10.1007/978-1-84882-054-8}, url = {http://www.springerlink.com/content/tj2x827107563616/}, author = {Bernardin, K. and Stiefelhagen, R. and Pnevmatikakis, A. and Lanz, O. and Brutti, A. and Casas, J. and Potamianos, G.} } @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 {cHaro08, title = {On the non-uniform complexity of brain connectivity}, booktitle = {5th IEEE International Symposium on Biomedical Imaging (ISBI 2008)}, year = {2008}, month = {05/2008}, publisher = {IEEE}, organization = {IEEE}, address = {Paris}, abstract = {

A stratification and manifold learning approach for analyzing High Angular Resolution Diffusion Imaging (HARDI) data is introduced in this paper. HARDI data provides high- dimensional signals measuring the complex microstructure of biological tissues, such as the cerebral white matter. We show that these high-dimensional spaces may be understood as unions of manifolds of varying dimensions/complexity and densities. With such analysis, we use clustering to characterize the structural complexity of the white matter. We briefly present the underlying framework and numerical experiments illustrating this original and promising approach.

}, keywords = {biodiffusion, biological tissues, biology computing, brain, brain connectivity, cellular biophysics, cerebral white matter, Clustering methods, complex microstructure, density, Density measurement, Diffusion tensor imaging, Geometry, high-angular resolution diffusion imaging, high-dimensional spaces, High-resolution imaging, Image analysis, Image resolution, Magnetic resonance imaging, manifold learning approach, Microstructure, molecular biophysics, nonuniform complexity, Point processes, Poisson processes, Signal resolution, stratification learning, Switches, Tensile stress, Unsupervised learning}, doi = {10.1109/ISBI.2008.4541139}, author = {Haro, G. and Lenglet, C. and Sapiro, Guillermo and Thompson, P.} } @article {aLandabaso08a, title = {Shape from inconsistent silhouette}, journal = {Computer vision and image understanding}, volume = {112}, number = {2}, year = {2008}, month = {11/2008}, pages = {210{\textendash}224}, abstract = {

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

}, issn = {1077-3142}, doi = {10.1016/j.cviu.2008.02.006}, author = {Landabaso, J. and M. Pard{\`a}s and Casas, J.} } @conference {cLandabaso08, title = {Shape from Inconsistent Silhouette for Free Viewpoint Video}, booktitle = {IEEE International Conference on Image Processing}, year = {2008}, pages = {213{\textendash}216}, isbn = {1-4244-1764-3}, author = {Landabaso, J. and Lizcano, L. and M. Pard{\`a}s} } @conference {cGudmundsson08, title = {TOF Imaging in Smart Room Environments towards Improved People Tracking}, booktitle = {Computer Vision and Pattern Recognition: Workshop on Time of Flight based Computer Vision (CVPR/TOF-CV)}, year = {2008}, pages = {1{\textendash}6}, author = {Gudmundsson, S. and M. Pard{\`a}s and Casas, J. and Aanaes, H. and Larsen, R.} } @phdthesis {dLandabaso08, title = {A Unified Framework for Consistent 2D/3D Foreground Object Detection}, year = {2008}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, author = {Landabaso, J.}, editor = {M. Pard{\`a}s} } @article {aLandabaso08, title = {A Unified Framework for Consistent 2D/3D Foreground Object Detection}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {18}, number = {8}, year = {2008}, pages = {1040{\textendash}1051}, issn = {1051-8215}, author = {Landabaso, J. and M. Pard{\`a}s} } @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.} } @book {eAguilar07, title = {Diccionari de Telecomunicacions}, year = {2007}, isbn = {978-84-412-1459-0}, author = {Aguilar, M. and Alcober, J. and Altes, J. and Aragones, X. and Artigas, D. and Bardes, D. and Barlabe, A. and Bragos, R. and Calderer, J. and Cardama, A. and Casademont, J. and Casals, L. and Comer{\'o}n, A. and Cotrina, J. and Cruz, L. and Federico Dios and Duxans, H. and Esparza, O. and Esquerra, I. and Garcia, D. and Garcias, P. and Gomez, C. and Gorricho, J. and Guinjoan, F. and Hesselbach, X. and Liria, A. and Lopez, J. and Madrenas, J. and Madue{\~n}o, M. and Mestre, F. and Monte, E. and Morros, J.R. and Mu{\~n}oz, J. and Pallar{\'e}s, E. and Pons, J. and Recolons, J. and Rincon, D. and Riu, P. and Pradell, L. and Pascual-Iserte, A. and Prat, L. and Rey, F. and Villares, J.} } @conference {cLopez-Mendez07, title = {Multi-person 3D Tracking with Particle Filters on Voxels}, booktitle = {2007 International Conference on Acoustics, Speech and Signal Processing}, year = {2007}, abstract = {

This paper presents a new approach to the problem of simultaneous tracking of several people in low resolution sequences from multiple calibrated cameras. Spatial redundancy is exploited to generate a discrete 3D binary representation of the scene. A particle filtering scheme adapted to the incoming 3D discrete data is proposed. A volume likelihood function and a discrete 3D re-sampling procedure are introduced to evaluate and drive particles. Multiple targets are tracked by means of multiple particle filters and interaction among them is modeled through a 3D blocking scheme. Test over annotated databases yield quantitative results showing the effectiveness of the proposed algorithm in the indoor scenarios.

}, author = {L{\'o}pez-M{\'e}ndez, A. and Cristian Canton-Ferrer and Casas, J.} } @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 {cLandabaso06, title = {Cooperative background modelling using multiple cameras towards human detection in smart-room}, booktitle = {14th European Signal Processing Conference}, year = {2006}, pages = {1{\textendash}5}, author = {Landabaso, J. and M. Pard{\`a}s} } @article {aLandabaso06, title = {Foreground Regions Extraction and Characterization Towards Real-Time Object Tracking}, journal = {Lecture notes in computer science}, volume = {3869}, year = {2006}, pages = {241{\textendash}249}, issn = {0302-9743}, url = {http://www.springerlink.com}, author = {Landabaso, J. and M. Pard{\`a}s} } @conference {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 {cLandabaso06a, title = {Reconstruction of 3D shapes considering inconsistent 2D silhouettes}, booktitle = {International Conference on Image Processing}, year = {2006}, pages = {1{\textendash}4}, abstract = {

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

}, author = {Landabaso, J. and M. Pard{\`a}s and Casas, J.} } @conference {cLopez06, title = {Rotating convection: Eckhauss-Benjamin-Feir instability}, booktitle = {59th Annual Meeting of the APS Division of Fluid Dynamics}, year = {2006}, pages = {2004{\textendash}2004}, isbn = {-}, author = {Lopez, J. and Mercader, M. and Marqu{\'e}s, F. and Batiste, O.} } @conference {cAbad06, title = {UPC Audio, Video and Multimodal Person Tracking Systems in the CLEAR Evaluation Campaign}, booktitle = {CLEAR{\textquoteright}06 Evaluation Campaign and Workshop - Classification of Events, Activities and Relationships}, year = {2006}, pages = {93{\textendash}104}, isbn = {978-3-540-69567-7}, author = {Abad, A. and Cristian Canton-Ferrer and Cristian Canton-Ferrer and Segura, C. and Landabaso, J. and Macho, D. and Casas, J. and Hernando, J. and M. Pard{\`a}s and Nadeu, C.} } @conference {cLandabaso05a, title = {Foreground Regions Extraction and Characterization Towards Real-Time Object Tracking}, booktitle = {2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms}, year = {2005}, pages = {241{\textendash}249}, author = {Landabaso, J. and M. Pard{\`a}s} } @conference {cLandabaso05, title = {Hierarchical Representation of Scenes using Activity Information}, booktitle = {2005 IEEE International Conference on Acoustics, Speech, and Signal Processing}, year = {2005}, pages = {677{\textendash}680}, isbn = {0780388755}, author = {Landabaso, J. and M. Pard{\`a}s} } @conference {cQun05, title = {Shadow removal with blob-based Morphological Reconstruction for Error Corretion}, booktitle = {2005 IEEE International Conference on Acoustics, Speech, and Signal Processing}, year = {2005}, pages = {729{\textendash}732}, author = {Xu, Li-Qun and Landabaso, J. and M. Pard{\`a}s} } @conference {cLobo04, title = {Enhanced audio data hiding synchronization using non-linear filter}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing}, year = {2004}, pages = {885{\textendash}888}, author = {Lobo, A. and Bas, P. and Marqu{\'e}s, F.} } @inbook {bPardas04, title = {The InterFace Software Platform for Interactive Virtual Characters}, booktitle = {Mpeg-4 facial animation: the standard, implementation and applications}, year = {2004}, pages = {169{\textendash}183}, isbn = {0-470-84465-5}, author = {M. Pard{\`a}s and Pandzic, I. and Cannella, M. and Davoine, F. and Forchheimer, R. and Lavagetto, F. and Marriott, A. and Malassiotis, S.} } @conference {cJose-Luis04, title = {Robust Tracking and Object Classification Towards Automated Video Surveillance}, booktitle = {International Conference on Image Analysis and Recognition}, year = {2004}, pages = {46333{\textendash}470}, isbn = {3540232230}, author = {Landabaso, J. and M. Pard{\`a}s and Xu, Li-Qun} } @article {aLandabaso04, title = {Robust Tracking and Object Classification Towards Automated Video Surveillance}, journal = {Lecture notes in computer science}, volume = {3212}, year = {2004}, pages = {463{\textendash}470}, issn = {0302-9743}, author = {Landabaso, J. and Xu, Li-Qun and M. Pard{\`a}s} } @conference {cCasas03, title = {Advanced content-based semantic scene analysis and information retrieval: the Schema Project}, booktitle = {4th European Workshop on Image Analysis for Multimedia Interactive Services_x000D_}, year = {2003}, pages = {43{\textendash}45}, abstract = {

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

}, author = {Izquierdo, E. and Casas, J. and Leonardi, R. and Migliorati, P. and O{\textquoteright}Connor, N. and Kompatsiaris, I. and Strintzis, M.} } @phdthesis {dLlach03, title = {Analysis of Video Sequences for Content Description. Table of Contents \& Index Creation and Scene Classification}, year = {2003}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, author = {Llach, J.}, editor = {Salembier, P.} } @conference {cLandabaso03, title = {HMM recognition of expressions in unrestrained video intervals}, booktitle = {International conference on Acoustics, Speech, and Signal Processing}, year = {2003}, pages = {197{\textendash}200}, isbn = {0-7803-7664-1}, author = {Landabaso, J. and M. Pard{\`a}s and Bonafonte, A.} } @conference {cO{\textquoteright}Connor03, title = {Region and object segmentation algorithms in the Qimera segmentation platform}, booktitle = {Third International Workshop on Content-Based Multimedia Indexing}, year = {2003}, pages = {95{\textendash}103}, abstract = {

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

}, isbn = {978-84-612-2373-2}, author = {O{\textquoteright}Connor, N. and Sav, S. and Adamek, T. and Mezaris, V. and Kompatsiaris, I. and Lui, T. and Izquierdo, E. and Ferran, C. and Casas, J.} } @conference {cPardas02, title = {Emotion recognition based on MPEG-4 facial animation parameters}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, year = {2002}, pages = {3624{\textendash}3627}, author = {M. Pard{\`a}s and Bonafonte, A. and Landabaso, J.} } @article {aSalembier02, title = {Visual Segment Tree Creation for MPEG-7 Description Schemes}, journal = {Pattern recognition}, volume = {35}, number = {1}, year = {2002}, pages = {563{\textendash}579}, issn = {0031-3203}, author = {Salembier, P. and Llach, J. and Garrido, L.} } @conference {cSalembier00, title = {Creation of visual segment trees for MPEG-7 Description Schemes}, booktitle = {International Symposium on Image / video Communications over Fixed and Mobile Networks, ISIVC{\textquoteright}2000}, year = {2000}, pages = {127{\textendash}134}, address = {Rabat, Morocco}, isbn = {?}, author = {Salembier, P. and Llach, J. and Garrido, L.} } @conference {cSalembier00b, title = {Visual Segment Tree Creation for MPEG-7 Description Schemes}, booktitle = {IEEE International Conference on Multimedia and Expo, ICME{\textquoteright}2000 }, year = {2000}, pages = {56{\textendash}61}, address = {New York City, NY, USA}, author = {Salembier, P. and Llach, J. and Garrido, L.} } @article {pSalembier99a, title = {Analysis of Video Sequence. Method for Defining the Structure of a Video Sequence. Part II}, number = {994026615-}, year = {1999}, type = {Invention patent}, author = {Llach, J. and Salembier, P.} } @article {pSalembier99, title = {Analysis of Video Sequence. Method for Defining the Structure of a Video Sequence. Part I}, number = {994025948-}, year = {1999}, type = {Invention patent}, author = {Llach, J. and Salembier, P.} } @conference {cLlach99, title = {Analysis of video sequences : table of content and index creation}, booktitle = {International Workshop on Very Low Bitrate Video, VLBV{\textquoteright}99}, year = {1999}, pages = {51{\textendash}54}, address = {Kobe, Japan}, author = {Llach, J. and Salembier, P.} } @inbook {aTorres99, title = {Face Location and Recognition for Video Indexing in the Hypermedia Project}, booktitle = {Lecture Notes in Computer Science}, volume = {1629}, year = {1999}, issn = {364-377}, doi = {DOI: 10.1007/3-540-48757-3_25}, author = {Torres, L. and Marqu{\'e}s, F. and Lorente, L. and Ver{\'o}nica Vilaplana} } @conference {cTorres99, title = {Face location and recognition for video indexing in the Hypermedia project}, booktitle = {European Conference on Multimedia Applications, Services and Techniques}, year = {1999}, pages = {364{\textendash}377}, author = {Torres, L. and Marqu{\'e}s, F. and Lorente, L. and Ver{\'o}nica Vilaplana} } @conference {cVidal98, title = {Autentificacion digital de imagen, marcado y detecci{\'o}n {\'o}ptimos}, booktitle = {XIII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1998}, pages = {105{\textendash}106}, isbn = {84-89654-12-3}, author = {Vidal, J. and Elisa Sayrol and Lagunas, M.} } @article {aMarques98, title = {Prediction of image partitions using fourier descriptors : application to segmentation-based coding schemes}, journal = {IEEE transactions on image processing}, volume = {7}, number = {4}, year = {1998}, pages = {529{\textendash}542}, issn = {1057-7149}, author = {Marqu{\'e}s, F. and Llorens, B. and Gasull, A.} } @conference {cMarques95a, title = {Interpolation and extrapolation of iomage partitions using fourier descriptions: to segmentation-based coding schemes}, booktitle = {IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING{\textquoteright}95.}, year = {1995}, pages = {584{\textendash}587}, author = {Marqu{\'e}s, F. and Llorens, B. and Gasull, A.} } @conference {cSalembier94 , title = {Self-referred texture coding for segmentation-based codec}, booktitle = {SPIE Visual Communication and Image Processing, VCIP{\textquoteright}94}, year = {1994}, address = {Chicago (IL), USA}, author = {Salembier, P. and Lopez, F} } @book {eTorres93, title = {Sistemas Anal{\'o}gicos y Digitales de Televisi{\'o}n}, year = {1993}, publisher = {Edicions UPC}, organization = {Edicions UPC}, address = {Barcelona}, isbn = {84-7653-288-1}, author = {Torres, L. and Lleida, E. and Casas, J.} } @conference {cLleida91, title = {Two level c.s.r. using demisyllable..}, booktitle = {EUROPEAN CONF. ON SPEECH COMMUN. AND TECHN. 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