@article {aCumplido-Mayoral, title = {The mediating role of neuroimaging-derived biological brain age between risk factors for dementia and cognitive decline in middle/late-aged asymptomatic individuals: a cohort study}, journal = {The Lancet Healthy Longevity}, year = {Submitted}, author = {Irene Cumplido-Mayoral and Anna Brugulat-Serra and Gonzalo S{\'a}nchez-Benavides and Jose Luis Molinuevo and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan Domingo Gispert} } @article {aCumplido-Mayoral22, title = {Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi-Cohort Validation Against Biomarkers of Alzheimer{\textquoteright}s Disease and Neurodegeneration stratified by sex}, journal = {eLife}, volume = {12}, year = {2023}, month = {04/2023}, abstract = {

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

}, issn = {2050-084X}, doi = {https://doi.org/10.7554/eLife.81067}, author = {Irene Cumplido-Mayoral and Marina Garc{\'\i}a-Prat and Gregory Operto and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Marta Mila-Aloma and Luigi Lorenzini and Carolina Minguillon and Jose Luis Molinuevo and Marc Suarez-Calvet and Ver{\'o}nica Vilaplana and Juan Domingo Gispert} } @conference {cCumplido-Mayoral23a, title = {Brain-age mediates the association between modifiable risk factors and cognitive decline early in the AD continuum}, booktitle = {Alzheimer{\textquoteright}s Association International Conference (AAIC)}, year = {2023}, month = {07/2023}, address = {Amsterdam, Netherlands}, author = {Irene Cumplido-Mayoral and Anna Brugulat-Serrat and Gonzalo S{\'a}nchez-Benavides and Armand Gonz{\'a}lez-Escalante and Federica Anastasi and Marta Mila-Aloma and Carles Falcon and Mahnaz Shekari and Raffaele Cacciaglia and Carolina Minguillon and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cCumplido-Mayoral23, title = {Brain-age prediction and its associations with glial and synaptic CSF markers}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2023}, month = {07/2023}, address = {Amsterdam, Netherlands}, author = {Irene Cumplido-Mayoral and Marta Mila-Aloma and Carles Falcon and Raffaele Cacciaglia and Carolina Minguillon and Karine Fauria and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @article {ade-Mas-Gimenez23, title = {Gradient-Based Metrics for the Evaluation of Image Defogging}, journal = {World Electric Vehicle Journal}, volume = {14}, year = {2023}, month = {09/2023}, chapter = {254}, abstract = {

Fog, haze, or smoke are standard atmospheric phenomena that dramatically compromise the overall visibility of any scene, critically affecting features such as the illumination, contrast, and contour detection of objects. The decrease in visibility compromises the performance of computer vision algorithms such as pattern recognition and segmentation, some of which are very relevant to decision-making in the field of autonomous vehicles. Several dehazing methods have been proposed that either need to estimate fog parameters through physical models or are statistically based. But physical parameters greatly depend on the scene conditions, and statistically based methods require large datasets of natural foggy images together with the original images without fog, i.e., the ground truth, for evaluation. Obtaining proper fog-less ground truth images for pixel-to-pixel evaluation is costly and time-consuming, and this fact hinders progress in the field. This paper aims to tackle this issue by proposing gradient-based metrics for image defogging evaluation that do not require a ground truth image without fog or a physical model. A comparison of the proposed metrics with metrics already used in the NTIRE 2018 defogging challenge as well as several state-of-the-art defogging evaluation metrics is performed to prove its effectiveness in a general situation, showing comparable results to conventional metrics and an improvement in the no-reference scene. A Matlab implementation of the proposed metrics has been developed and it is open-sourced in a public GitHub repository.

}, doi = {10.3390/wevj14090254}, url = {https://www.mdpi.com/2032-6653/14/9/254}, author = {Gerard de-Mas-Gim{\'e}nez and Pablo Garc{\'\i}a-G{\'o}mez and Casas, J. and S. Royo} } @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} } @article {aPlasencia23, title = {A Preliminary Study of Deep Learning Sensor Fusion for Pedestrian Detection}, journal = {Sensors}, volume = {23}, year = {2023}, month = {04/2023}, chapter = {4167}, abstract = {

Most pedestrian detection methods focus on bounding boxes based on fusing RGB with lidar. These methods do not relate to how the human eye perceives objects in the real world. Furthermore, lidar and vision can have difficulty detecting pedestrians in scattered environments, and radar can be used to overcome this problem. Therefore, the motivation of this work is to explore, as a preliminary step, the feasibility of fusing lidar, radar, and RGB for pedestrian detection that potentially can be used for autonomous driving that uses a fully connected convolutional neural network architecture for multimodal sensors. The core of the network is based on SegNet, a pixel-wise semantic segmentation network. In this context, lidar and radar were incorporated by transforming them from 3D pointclouds into 2D gray images with 16-bit depths, and RGB images were incorporated with three channels. The proposed architecture uses a single SegNet for each sensor reading, and the outputs are then applied to a fully connected neural network to fuse the three modalities of sensors. Afterwards, an up-sampling network is applied to recover the fused data. Additionally, a custom dataset of 60 images was proposed for training the architecture, with an additional 10 for evaluation and 10 for testing, giving a total of 80 images. The experiment results show a training mean pixel accuracy of 99.7\% and a training mean intersection over union of 99.5\%. Also, the testing mean of the IoU was 94.4\%, and the testing pixel accuracy was 96.2\%. These metric results have successfully demonstrated the effectiveness of using semantic segmentation for pedestrian detection under the modalities of three sensors. Despite some overfitting in the model during experimentation, it performed well in detecting people in test mode. Therefore, it is worth emphasizing that the focus of this work is to show that this method is feasible to be used, as it works regardless of the size of the dataset. Also, a bigger dataset would be necessary to achieve a more appropiate training. This method gives the advantage of detecting pedestrians as the human eye does, thereby resulting in less ambiguity. Additionally, this work has also proposed an extrinsic calibration matrix method for sensor alignment between radar and lidar based on singular value decomposition.

}, keywords = {autonomous driving, Convolutional Neural Networks, sensor calibration, sensor fusion}, doi = {10.3390/s23084167}, url = {https://www.mdpi.com/1424-8220/23/8/4167}, author = {Alfredo Ch{\'a}vez Plasencia and Pablo Garc{\'\i}a-G{\'o}mez and Eduardo Bernal P{\'e}rez and Gerard de-Mas-Gim{\'e}nez and Casas, J. and S. Royo} } @conference {cCaselles, title = {SIRA: Relightable Avatars from a Single Image}, booktitle = {Winter Conference on Applications of Computer Vision (WACV)}, year = {2023}, abstract = {

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

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

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

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

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

}, keywords = {artificial intelligence, computational pathology, deep learning, digital pathology, implementation, LIS, primary diagnosis, telepathology, workflow}, doi = {10.3390/diagnostics12040852}, url = {https://www.mdpi.com/2075-4418/12/4/852}, author = {Jordi Temprana-Salvador and Pau L{\'o}pez-Garc{\'\i}a and Josep Castellv{\'\i} Vives and Llu{\'\i}s de Haro and Eudald Ballesta and Matias Rojas Abusleme and Miquel Arrufat and Ferran Marques and Casas, J. and Carlos Gallego and Laura Pons and Jos{\'e} Luis Mate and Pedro Luis Fern{\'a}ndez and Eugeni L{\'o}pez-Bonet and Ramon Bosch and Salom{\'e} Mart{\'\i}nez and Santiago Ram{\'o}n y Cajal and Xavier Matias-Guiu} } @article {aPedersen22, title = {Experimental confirmation of efficient island divertor operation and successful neoclassical transport optimization in Wendelstein 7-X}, journal = {Nuclear Fusion}, volume = {62}, year = {2022}, month = {04/2022}, issn = {1741-4326}, doi = {10.1088/1741-4326/ac2cf5}, author = {Thomas Sunn Pedersen and al et and Casas, J. and Salembier, P.} } @conference {cGarcia-Gomez22, title = {Multimodal imaging System based on s{\`o}lid-State LiDAR for Advanced perception applications}, booktitle = {10th International Symposium on Optronics in defence \& security}, year = {2022}, month = {06/2022}, publisher = {3AF OPTRO2022}, organization = {3AF OPTRO2022}, address = {Versailles, France}, url = {https://www.3af-optro.com/}, author = {Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Noel Rodrigo and Casas, J. and Jordi Riu} } @conference {cPinazo22, title = {Perception in the era of Autonomous Vehicles}, booktitle = {Photonics 4 Smart Cities, SCEWC 2022}, year = {2022}, month = {11/2022}, publisher = {Photonics21}, organization = {Photonics21}, address = {Barcelona}, url = {https://www.fotonica21.org/photonics-4-smart-cities}, author = {Jorge Pinazo and Adolfo Ler{\'\i}n and Francesc Xavier de Gibert and {\'A}lvaro Moliner and Daniel Sevilla and Antonio Jurado and Iv{\'a}n R{\'\i}os and Rodrigo Jerez and Jaime Santiago and {\'A}lvaro Linuesa and Antonio Cano and Federico Dios and Adolfo Comer{\'o}n and Casas, J. and Jos{\'e} Antonio L{\'a}zaro} } @conference {cGeleta21, title = {PixInWav: Residual Steganography for Hiding Pixels in Audio}, booktitle = {ICASSP}, year = {2022}, month = {06/2021}, abstract = {

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

\ Deep Learning Barcelona Symposium 2022

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

}, author = {Geleta, Margarita and Punt{\'\i}, Cristina and McGuinness, Kevin and Pons, Jordi and Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @conference {cHernandeza, title = {Sentinel lymph node status prediction using self-attention networks and contrastive learning from routine histology images of primary tumours}, booktitle = {Medical Imaging with Deep Learning MIDL 2022}, year = {2022}, month = {07/2022}, abstract = {

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

}, author = {Carlos Hernandez and Marc Combalia and Josep Malvehy and Ver{\'o}nica Vilaplana} } @conference {cHernandez22, title = {Sentinel lymph node status prediction with self-attention neural networks using histologies of primary melanoma tumours}, booktitle = {European Association of Dermato Oncology (EADO 2022)}, year = {2022}, month = {04/2022}, author = {Carlos Hernandez and Ver{\'o}nica Vilaplana and Marc Combalia and Sergio Garc{\'\i}a and Sebastian Podlipnik and Julio Burgos and Susana Puig and Josep Malvehy} } @article {xCabot22, title = {Sign Language Translation based on Transformers for the How2Sign Dataset}, year = {2022}, abstract = {

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

The end goal of Sign Language Translation is to either produce spoken sentences from sign videos or generate sign videos from their corresponding written transcriptions. In this situation, this task has been address in multiple approaches in recent years. Moreover, it has been proved that taking advantage of the sign gloss representations improves substantially the model{\textquoteright}s performance in this task. Therefore, in this work we replicate the state-of-the-art Transformer-based approach on the task and evaluate it on the multimodal American Sign Language How2Sign dataset. Furthermore, we provide baseline recognition and translation results that represent an starting point to further research on the topic. In addition, we provide a new sentence-based alignment for the How2Sign videos, as their current alignment was with speech, which we have used to tackle the Sign Language Translation task properly.\ 

}, author = {Patricia Cabot}, editor = {Laia Tarr{\'e}s and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCabot22a, title = {Sign-Language Translation with Pseudo-Glosses}, year = {2022}, abstract = {

Sign Language Translation is an open problem whose goal is to generate written sentences from sign videos. In recent years, many research works that have been developed in this field mainly addressed the Sign Language Recognition task, which consists in understanding the input signs and transcribing them into sequences of annotations. Moreover, current studies show that taking advantage of the latter task helps to learn meaningful representations and can be seen as an intermediate step towards the end goal of translation.


In this work, we present a method to generate automatic pseudo-glosses from written sentences, which can work as a replacement for real glosses. This addresses the issue of their collection, as they need to be manually annotated and it is extremely costly.

Furthermore, we introduce a new implementation built on Fairseq of the Transformer-model approach introduced by Camgoz et al., which is jointly trained to solve the recognition and translation tasks. Besides, we provide new baseline results on both implementations: first, on the Phoenix dataset, we present results that outperform the ones provided by Camgoz et al. in their work, and, second, on the How2Sign dataset, we present the first results on the translation task. These results can work as a baseline for future research in the field.

Patricia Cabot with her advisors Laia Tarr{\'e}s, Gerard I. G{\'a}llego and Xavier Gir{\'o}-i-Nieto.
}, author = {Patricia Cabot and Laia Tarr{\'e}s and Xavier Gir{\'o}-i-Nieto} } @conference {cPina, title = {Structural Networks for Brain Age Prediction}, booktitle = {Medical Imaging with Deep Learning (MIDL 2022)}, year = {2022}, month = {08/2022}, abstract = {

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

}, author = {Oscar Pina and Irene Cumplido-Mayoral and Raffaele Cacciaglia and Jos{\'e} Mar{\'\i}a Gonz{\'a}lez-de-Ech{\'a}varri and Juan D. Gispert and Ver{\'o}nica Vilaplana} } @conference {cMayoral21a, title = {Brain structural alterations in cognitively unimpaired individuals with discordant amyloid-β PET and CSF Aβ42 status: findings using Machine Learning}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Mahnaz Shekari and Gemma Salvad{\'o} and Greg Operto and Raffaele Cacciaglia and Carles Falcon and Aida Ni{\~n}erola Baiz{\'a}n and Andr{\'e}s Perissinotti and Carolina Minguillon and Karine Fauria and Ivonne Suridjan and Gwendlyn Kollmorgen and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @mastersthesis {xNietoa, title = {Discovery and Learning of Navigation Goals from Pixels in Minecraft}, year = {2021}, abstract = {

Pre-training Reinforcement Learning (RL) agents in a task-agnostic manner has shown promising results. However, previous works still struggle to learn and discover meaningful skills in high-dimensional state-spaces. We approach the problem by leveraging unsupervised skill discovery and self-supervised learning of state representations. In our work, we learn a compact latent representation by making use of variational or contrastive techniques. We demonstrate that both allow learning a set of basic navigation skills by maximizing an information theoretic objective. We assess our method in Minecraft 3D maps with different complexities. Our results show that representations and conditioned policies learned from pixels are enough for toy examples, but do not scale to realistic and complex maps. We also explore alternative rewards and input observations to overcome these limitations.



}, author = {Nieto, Juan Jos{\'e}}, editor = {V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCaselles21, title = {Disentangling neural network structure from the weights space}, year = {2021}, abstract = {

Deep Neural Networks have been used to tackle a wide variety of tasks achieving great performance. However, there is still a lack of knowledge of how the training of these models converge and how weights relate to their properties. In this thesis we investigate the structure of the weight space and try to disentangle its properties. Attention mechanisms are introduced to capture relations among neurons{\textquoteright} weights that help in weight reconstruction, hyper-parameter classification and accuracy prediction. Our approach further has the potential to work with variable input size allowing different network width, depth or even architecture types.

}, author = {Caselles, Pol}, editor = {Sch{\"u}rholt, Konstantin and Borth, Damian and Xavier Gir{\'o}-i-Nieto} } @conference {cHernandez, title = {Implementation of personalized medicine in cutaneous melanoma patients aided by artificial intelligence}, booktitle = {10th World Congress of2 Melanoma / 17th EADO Congress}, year = {2021}, month = {04/2021}, author = {Carlos Hernandez and Anil Kiroglu and Sergio Garc{\'\i}a and Joan Ficapal and Julio Burgos and Sebastian Podlipnik and Neus Calbet and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana and Marc Combalia} } @conference {cMayoral21, title = {Machine learning on combined neuroimaging and plasma biomarkers for triaging participants of secondary prevention trials in Alzheimer{\textquoteright}s Disease}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Gemma Salvad{\'o} and Mahnaz Shekari and Carles Falcon and Marta Mil{\`a} Alom{\`a} and Aida Ni{\~n}erola Baiz{\'a}n and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cGarcia-Gomez21, title = {Multimodal solid-state LiDAR for advanced perception applications}, booktitle = {OPTOEL}, year = {2021}, month = {06/2021}, abstract = {

Perception of the environment is an essential requirement for the fields of autonomous vehicles and robotics. Consequently, LiDAR imaging sensors have become crucial sen-sors for such applications due to their 3D geometry sensing capability. However, auton-omous systems claim for high amounts of data to make reliable decisions so many dif-ferent sensors are often combined. In this context, we present a multimodal imaging sys-tem based on a solid-state LiDAR combined with three other imaging sensors that pro-vides multimodal information with low parallax fusion error.

}, keywords = {artificial intelligence, autonomous navigation, computer Vision, enhanced perception, robotics, sensor fusion, solid-state LiDAR}, author = {Pablo Garc{\'\i}a-G{\'o}mez and Noel Rodrigo and Jordi Riu and Casas, J. and S. Royo} } @conference {cPodlipnik21, title = {Personalized medicine in melanoma patients aided by artificial intelligence}, booktitle = {Clinical Translation of Medical Image Computing and Computer Assisted Interventions (CLINICCAI) Workshop at MICCAI}, year = {2021}, month = {09/2021}, abstract = {

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

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

}, author = {Sebastian Podlipnik and Carlos Hernandez and Anil Kiroglu and Sergio Garc{\'\i}a and Joan Ficapal and Julio Burgos and Neus Calbet and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana and Marc Combalia} } @conference {cNieto, title = {PiCoEDL: Discovery and Learning of Minecraft Navigation Goals from Pixels and Coordinates}, booktitle = {CVPR 2021 Embodied AI Workshop}, year = {2021}, month = {06/2021}, abstract = {

Defining a reward function in Reinforcement Learning (RL) is not always possible or very costly. For this reason, there is a great interest in training agents in a task-agnostic manner making use of intrinsic motivations and unsupervised techniques. Due to the complexity to learn useful behaviours in pixel-based domains, the results obtained in RL are still far from the remarkable results obtained in domains such as computer vision and natural language processing. We hypothesize that RL agents will also benefit from unsupervised pre-trainings with no extrinsic rewards, analogously to how humans mostly learn, especially in the early stages of life. Our main contribution is the deployment of the Explore, Discover and Learn (EDL) paradigm for unsupervised learning to the pixel space. In particular, our work focuses on the MineRL environment, where the observation of the agent is represented by: (a) its spatial coordinates in the Minecraft virtual world, and (b) an image from an egocentric viewpoint.

}, author = {Nieto, Juan Jos{\'e} and Creus, Roger and Xavier Gir{\'o}-i-Nieto} } @conference {cCreus, title = {PixelEDL: Unsupervised Skill Discovery and Learning from Pixels}, booktitle = {CVPR 2021 Embodied AI Workshop}, year = {2021}, month = {06/2021}, abstract = {

We tackle embodied visual navigation in a task-agnostic set-up by putting the focus on the unsupervised discovery of skills (or options) that provide a good coverage of states. Our approach intersects with empowerment: we address the reward-free skill discovery and learning tasks to discover {\textquotedblleft}what{\textquotedblright} can be done in an environment and {\textquotedblleft}how{\textquotedblright}. For this reason, we adopt the existing Explore, Discover and Learn (EDL) paradigm, tested only in toy example mazes, and extend it to pixel-based state representations available for embodied AI agents.

}, author = {Creus, Roger and Nieto, Juan Jos{\'e} and Xavier Gir{\'o}-i-Nieto} } @conference {cMayoral21b, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using structural MRI}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Silvia Ingala and Luigi Lorenzini and Alle Meije Wink and Sven Haller and Jose Luis Molinuevo and Robin Wolz and Alessandro Palombit and Adam J Schwarz and Ga{\"e}l Chetelat and Pierre Payoux and Pablo Martinez-Lage and Giovanni Frisoni and Nick C Fox and Craig W Ritchie and Joanna M Wardlaw and Adam Waldman and Frederik Barkhof and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @article {aPuig-Sitjes21, title = {Real-time detection of overloads on the plasma-facing components of Wendelstein 7-X}, journal = {Applied sciences (Basel)}, volume = {11}, year = {2021}, month = {12/2021}, chapter = {1}, issn = {2076-3417}, doi = {10.3390/app112411969}, url = {http://hdl.handle.net/2117/361558}, author = {Puig-Sitjes, A. and Jakubowski, M. and Naujoks, D. and Gao, Y. and Drewelow, P. and Niemann, H. and Felinger, J. and Casas, J. and Salembier, P. and Clemente, R.} } @conference {cPuig-Sitjes21, title = {Spatio-temporal Detection and Tracking of Thermal Events on the Plasma Facing Components of Wendelstein 7-X}, booktitle = {4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis}, year = {2021}, month = {11/2021}, publisher = {iaea.org}, organization = {iaea.org}, address = {Shanghai (online)}, abstract = {

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

}, url = {https://conferences.iaea.org/event/251/contributions/20704/}, author = {Puig-Sitjes, A. and Casas, J. and Salembier, P. and Vizcarro, D. and Clemente, R. and Jakubowski, M. and Gao, Y. and W7-X Team} } @mastersthesis {xCreus, title = {Unsupervised skill learning from pixels}, year = {2021}, abstract = {

This work focuses on the self-acquirement of the fundamental task-agnostic knowledge available within an environment. The aim is to discover and learn baseline representations and behaviours that can later be useful for solving embodied visual navigation downstream tasks. Specifically, the presented approach extends the idea of the "Explore, Discover and Learn" (EDL) paradigm to the pixel domain. This way, this work is centered in the representations and behaviours that can be learnt by an agent that only integrates an image capture sensor. Both the agents and the environment that is used in this work run over the Habitat AI simulator, which is developed by Facebook AI, and renders 3D fotorealistic views of the insides of apartments.





}, author = {Creus, Roger}, editor = {Nieto, Juan Jos{\'e} and Xavier Gir{\'o}-i-Nieto} } @conference {cNietoa, title = {Unsupervised Skill-Discovery and Skill-Learning in Minecraft}, booktitle = {ICML 2021 Workshop on Unsupervised Reinforcement Learning (URL)}, year = {2021}, month = {07/2021}, abstract = {

Pre-training Reinforcement Learning agents in a task-agnostic manner has shown promising results. However, previous works still struggle in learning and discovering meaningful skills in high-dimensional state-spaces, such as pixel-spaces. We approach the problem by leveraging unsupervised skill discovery and self-supervised learning of state representations. In our work, we learn a compact latent representation by making use of variational and contrastive techniques. We demonstrate that both enable RL agents to learn a set of basic navigation skills by maximizing an information theoretic objective. We assess our method in Minecraft 3D pixel maps with different complexities. Our results show that representations and conditioned policies learned from pixels are enough for toy examples, but do not scale to realistic and complex maps. To overcome these limitations, we explore alternative input observations such as the relative position of the agent along with the raw pixels.

}, author = {Nieto, Juan Jos{\'e} and Creus, Roger and Xavier Gir{\'o}-i-Nieto} } @conference {xCaros19, title = {Automatic Reminiscence Therapy for Dementia}, booktitle = {ACM International Conference on Multimedia Retrieval (ICMR)}, year = {2020}, month = {06/2020}, publisher = {ACM}, organization = {ACM}, address = {Dublin, Ireland}, abstract = {

With people living longer than ever, the number of cases with dementia such as Alzheimer{\textquoteright}s disease increases steadily. It affects more than 46 million people worldwide, and it is estimated that in 2050 more than 100 million will be affected. While there are not effective treatments for these terminal diseases, therapies such as reminiscence, that stimulate memories from the past are recommended. Currently, reminiscence therapy takes place in care homes and is guided by a therapist or a carer. In this work, we present an AI-based solution to automatize the reminiscence therapy, which consists in a dialogue system that uses photos as input to generate questions. We run a usability case study with patients diagnosed of mild cognitive impairment that shows they found the system very entertaining and challenging. Overall, this paper presents how reminiscence therapy can be automatized by using machine learning, and deployed to smartphones and laptops, making the therapy more accessible to every person affected by dementia. (demo paper)

Automatic Reminiscence Therapy for Dementia from Universitat Polit{\`e}cnica de Catalunya



}, doi = {https://doi.org/10.1145/3372278.3391927}, url = {https://arxiv.org/abs/1910.11949}, author = {Caros, Mariona and Garolera, Maite and Radeva, Petia and Xavier Gir{\'o}-i-Nieto} } @phdthesis {dCampos20, title = {Deep Learning that Scales: Leveraging Compute and Data}, year = {2020}, month = {12/2020}, school = {Universitat Polit{\`e}cnica de Catalunya}, address = {Barcelona, Catalonia}, abstract = {

Deep learning has revolutionized the field of artificial intelligence in the past decade. Although the development of these techniques spans over several years, the recent advent of deep learning is explained by an increased availability of data and compute that have unlocked the potential of deep neural networks. They have become ubiquitous in domains such as natural language processing, computer vision, speech processing, and control, where enough training data is available. Recent years have seen continuous progress driven by ever-growing neural networks that benefited from large amounts of data and computing power. This thesis is motivated by the observation that scale is one of the key factors driving progress in deep learning research, and aims at devising deep learning methods that scale gracefully with the available data and compute. We narrow down this scope into two main research directions. The first of them is concerned with designing hardware-aware methods which can make the most of the computing resources in current high performance computing facilities. We then study bottlenecks preventing existing methods from scaling up as more data becomes available, providing solutions that contribute towards enabling training of more complex models. This dissertation studies the aforementioned research questions for two different learning paradigms, each with its own algorithmic and computational characteristics. The first part of this thesis studies the paradigm where the model needs to learn from a collection of examples, extracting as much information as possible from the given data. The second part is concerned with training agents that learn by interacting with a simulated environment, which introduces unique challenges such as efficient exploration and simulation.

}, url = { http://hdl.handle.net/10803/670372}, author = {V{\'\i}ctor Campos}, editor = {Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cFernandezf, title = {Enhancing Online Knowledge Graph Population with Semantic Knowledge}, booktitle = {19th International Semantic Web Conference (ISWC)}, year = {2020}, month = {11/2020}, address = {Virtual}, abstract = {

Knowledge Graphs (KG) are becoming essential to organize, represent and store the world{\textquoteright}s knowledge, but they still rely heavily on humanly-curated structured data. Information Extraction (IE) tasks, like disambiguating entities and relations from unstructured text, are key to automate KG population. However, Natural Language Processing (NLP) methods alone can not guarantee the validity of the facts extracted and may introduce erroneous information into the KG.\ This work presents an end-to-end system that combines Semantic Knowledge and Validation techniques with NLP methods, to provide KG population of novel facts from clustered news events.\ The contributions of this paper are two-fold: First, we present a novel method for including entity-type knowledge into a Relation Extraction model, improving F1-Score over the baseline with TACRED and TypeRE datasets. Second, we increase the precision by adding data validation on top of the Relation Extraction method. These two contributions are combined in an industrial pipeline for automatic KG population over aggregated news, demonstrating increased data validity when performing online learning from unstructured web data. Finally, the TypeRE and AggregatedNewsRE datasets build to benchmark these results are also published to foster future research in this field.

}, keywords = {Data Validation, Knowledge Graph, Relation Extraction}, author = {Fern{\`a}ndez, D{\`e}lia and Rimmek, Joan Marco and Espadaler, Joan and Garolera, Blai and Barja, Adri{\`a} and Codina, Marc and Sastre, Marc and Xavier Gir{\'o}-i-Nieto and Riveiro, Juan Carlos and Bou-Balust, Elisenda} } @conference {cCamposb, title = {Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills}, booktitle = {International Conference on Machine Learning (ICML) 2020}, year = {2020}, month = {07/2020}, abstract = {

Acquiring abilities in the absence of a task-oriented reward function is at the frontier of reinforcement learning research. This problem has been studied through the lens of empowerment, which draws a connection between option discovery and information theory. Information-theoretic skill discovery methods have garnered much interest from the community, but little research has been conducted in understanding their limitations. Through theoretical analysis and empirical evidence, we show that existing algorithms suffer from a common limitation -- they discover options that provide a poor coverage of the state space. In light of this, we propose {\textquoteright}Explore, Discover and Learn{\textquoteright} (EDL), an alternative approach to information-theoretic skill discovery. Crucially, EDL optimizes the same information-theoretic objective derived from the empowerment literature, but addresses the optimization problem using different machinery. We perform an extensive evaluation of skill discovery methods on controlled environments and show that EDL offers significant advantages, such as overcoming the coverage problem, reducing the dependence of learned skills on the initial state, and allowing the user to define a prior over which behaviors should be learned.




}, author = {V{\'\i}ctor Campos and Trott, Alexander and Xiong, Caiming and Socher, Richard and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @article {aGarcia-Gomez20, title = {Geometric Model and Calibration Method for a Solid-State LiDAR}, journal = {Sensors}, volume = {20}, year = {2020}, month = {05/2020}, pages = {2898}, abstract = {

This paper presents a novel calibration method for solid-state LiDAR devices based on a geometrical description of their scanning system, which has variable angular resolution. Determining this distortion across the entire Field-of-View of the system yields accurate and precise measurements which enable it to be combined with other sensors. On the one hand, the geometrical model is formulated using the well-known Snell{\textquoteright}s law and the intrinsic optical assembly of the system, whereas on the other hand the proposed method describes the scanned scenario with an intuitive camera-like approach relating pixel locations with scanning directions. Simulations and experimental results show that the model fits with real devices and the calibration procedure accurately maps their variant resolution so undistorted representations of the observed scenario can be provided. Thus, the calibration method proposed during this work is applicable and valid for existing scanning systems improving their precision and accuracy in an order of magnitude.

}, keywords = {solid-state LiDAR; LiDAR calibration; distortion correction; FOV mapping}, issn = {1424-8220}, doi = {10.3390/s20102898}, url = {https://www.mdpi.com/1424-8220/20/10/2898}, author = {Pablo Garc{\'\i}a-G{\'o}mez and S. Royo and Noel Rodrigo and Casas, J.} } @article {9103248, title = {Grounded Sequence to Sequence Transduction}, journal = {IEEE Journal of Selected Topics in Signal Processing}, volume = {14}, year = {2020}, month = {05/2020}, pages = {577-591}, abstract = {

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

}, author = {L. Specia and L. Barrault and O. Caglayan and Amanda Duarte and D. Elliott and S. Gella and N. Holzenberger and C. Lala and S. J. Lee and J. Libovicky and P. Madhyastha and F. Metze and K. Mulligan and A. Ostapenko and S. Palaskar and R. Sanabria and J. Wang and R. Arora} } @phdthesis {dPujol-Miro20, title = {Learning to extract features for 2D-3D multimodal registration}, year = {2020}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

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

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

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

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

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

}, url = {http://hdl.handle.net/2117/330132}, author = {A. Pujol-Mir{\'o}}, editor = {Casas, J. and Ruiz-Hidalgo, J.} } @article {aCasamitjanab, title = {NeAT: a nonlinear analysis toolbox for neuroimaging}, journal = {Neuroinformatics}, year = {2020}, month = {03/2020}, abstract = {

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

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

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

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

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

}, keywords = {Alzheimer{\textquoteright}s disease, brain morphology, CSF biomarkers, Latent model, PLS, preclinical AD}, author = {Adri{\`a} Casamitjana and Paula Petrone and Jose Luis Molinuevo and Juan D. Gispert and Ver{\'o}nica Vilaplana} } @conference {cPardas20, title = {Refinement network for unsupervised on the scene foreground segmentation}, booktitle = {EUSIPCO European Signal Processing Conference}, year = {2020}, month = {08/2020}, publisher = {European Association for Signal Processing (EURASIP)}, organization = {European Association for Signal Processing (EURASIP)}, abstract = {

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

}, url = {https://www.eurasip.org/Proceedings/Eusipco/Eusipco2020/pdfs/0000705.pdf}, author = {M. Pard{\`a}s and G. Canet} } @conference {cPuig-Sitjes20, title = {Strategy for the real-time detection of thermal events on the plasma facing components of Wendelstein 7-X}, booktitle = {31st Symposium on Fusion Technology (SOFT2020)}, year = {2020}, month = {09/2020}, address = {Dubrovnik, Croatia}, abstract = {

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

}, url = {http://hdl.handle.net/21.11116/0000-0007-7FF5-7}, author = {Puig-Sitjes, A. and Jakubowski, M. and Fellinger, J. and Drewelow, P. and Gao, Y. and Niemann, H. and Sunn-Pedersen, T. and K{\"o}nig, R. and Naujoks, D. and Winter, A. and Laqua, H. and Dumke, S. and Moncada, V. and Belafdil, C. and Mitteau, R. and Aumeunier, M.-H. and Pisano, F. and Aymerich, E. and Cannas, B. and Kocsis, G. and Szepesi, T. and Cseh, G. and Szabolics, T. and Casas, J. and Morros, J.R. and Salembier, P. and Clemente, R. and Cobos, M. and I. Caminal and Palacios Corral, A. and Moreno Punzano, A. and Quiceno Lopera, S.} } @conference {cCombaliad, title = {Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification}, booktitle = {CVPR 2020, ISIC Skin Image Analysis Workshop}, year = {2020}, month = {2020}, abstract = {

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

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

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

}, author = {Marc Combalia and Ferran Hueto and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana} } @conference {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}, author = {Manuel L{\'o}pez-Palma and Morros, J.R. and Montserrat Corbal{\'a}n and Javier Gago} } @conference {cCombaliac, title = {BCN20000: Dermoscopic Lesions in the Wild}, booktitle = {International Skin Imaging Collaboration (ISIC) Challenge on Dermoscopic Skin Lesion Analysis 2019}, year = {2019}, month = {10/2019}, abstract = {

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

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

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

}, doi = {10.1109/TMI.2019.2901712}, author = {Li Wang and Dong Nie and Guannan Li and Elodie Puybareau and Jose Dolz and Qian Zhang and Fan Wang and Jing Xia and Zhengwang Wu and Jiawei Chen and Kim-HanThung and Toan Duc Bui and Jitae Shin and Guodong Zeng and Guoyan Zheng and Vladimir S. Fonov and Andrew Doyle and Yongchao Xu and Pim Moeskops and Josien Pluim and Christian Desrosiers and Ismail Ben Ayed and Gerard Sanroma and Oualid Benkarim and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Weili Lin and Gang Li and Dinggang Shen} } @mastersthesis {xTarres19, title = {Clasificaci{\'o}n de lesiones de piel con un ensemble de redes neuronales residuales}, year = {2019}, abstract = {

Convolutional Neural Networks have gained popularity in the recent years due to their performance regarding image analysis, both in classification and segmentation. Especially in the medical field, it is increasingly common to use automatic techniques to help specialists with the diagnosis.

In this thesis, the problem of skin lesion classification is studied. The study is based on the ISIC Challenges, given the collaboration with Hospital Cl{\'\i}nic de Barcelona, and we help in the development of the database for the ISIC Challenge 2019.

One of the key points of the development is obtaining a model that manages to classify with accuracy a database provided. To do so, we study residual neural networks and an ensemble of them to further improve the results.

The purpose of this project, therefore is the study, analysis and evaluation of the variants and modifications of residual neural networks so that it adapts to our problem using an ensemble of them. In the process, the neural network will have to tackle the problem of class imbalance

}, author = {Laia Tarr{\'e}s}, editor = {Ver{\'o}nica Vilaplana and Marc Combalia} } @article {aPujol-Miro19, title = {Correspondence matching in unorganized 3D point clouds using Convolutional Neural Networks}, journal = {Image and Vision Computing}, volume = {83-84}, year = {2019}, month = {04/2019}, chapter = {51-60}, abstract = {

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

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

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

}, keywords = {depth estimation; semantic segmentation; convolutional neural networks; hybrid architecture}, issn = {1424-8220}, doi = {10.3390/s19081795}, url = {https://www.mdpi.com/1424-8220/19/8/1795}, author = {X. Lin and D. Sanchez-Escobedo and Casas, J. and M. Pard{\`a}s} } @conference {cCasamitjana19, title = {Detection of Amyloid Positive Cognitively unimpaired individuals using voxel-based machine learning on structural longitudinal brain MRI}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2019}, month = {07/2019}, author = {Adri{\`a} Casamitjana and P. Petrone and C. Falcon and M. Artigues and G. Operto and R. Cacciaglia and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @article {aCasamitjana19, title = {Detection of Amyloid-Positive Cognitively Unimpaired Individuals Using Voxel-Based Machine Learning on Structural Longitudinal Brain MRI}, journal = {Alzheimer{\textquoteright}s \& Dementia}, volume = {15}, year = {2019}, month = {07/2019}, chapter = {752}, abstract = {

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

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

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

}, author = {Marc Combalia and Javiera P{\'e}rez-Anker and Adriana Garc{\'\i}a-Herrera and Ll{\'u}cia Alos and Ver{\'o}nica Vilaplana and Ferran Marques and Susana Puig and Josep Malvehy} } @mastersthesis {xComas19, title = {Exploring Methods for Enhancing Linear Prediction of Video Sequences}, year = {2019}, abstract = {

Video prediction has for a long time received attention within the field of computer\ vision, but it has gained importance during the last decade with the popularization of deep\ neural networks and their applications to computer vision.\ In this thesis, the main focus will be to linearize the dynamics of time sequences by exploiting the spatial context that video offers, with the final scope of obtaining better predictions.\ In the first place, we provide the theoretical base for dynamics. Following, we present\ several modifications for an existing deterministic predictor network called Dynamical\ Atoms-based Network (DYAN) [1], which models time sequences as the output of Linear\ Time-Invariant (LTI) systems using system identification and dynamics foundations. The\ solutions present different levels of success and an in some cases they beat the State Of\ The Art (SOTA) for at least one dataset, in the metrics SSIM, MSE and MMF.\ We also present two novel convolutional autoencoder architectures (LODAEs) for low order\ dynamics manifold embedding, strongly based on deep neural networks, with the primary\ aim of giving a generalized solution for mapping video sequences into a new manifold, to\ adapt them to the pipeline of predictors such as DYAN, based on system identification.\ The results for the LODAEs are promising as they seem to achieve their goal for a very\ simple synthetic dataset by lowering the order of the latent space sequences and providing\ good reconstructions and in some cases, predictions.

}, author = {Comas, Armand}, editor = {Camps, Octavia and Xavier Gir{\'o}-i-Nieto} } @article {aGene-Molab, title = {Fruit Detection in an Apple Orchard Using a Mobile Terrestrial Laser Scanner}, journal = {Biosystems Engineering}, volume = {187}, year = {2019}, month = {09/2019}, chapter = {171}, abstract = {

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

}, issn = {1537-5110}, doi = {10.1016/j.biosystemseng.2019.08.017}, url = {https://authors.elsevier.com/c/1Zmc45Tbkk9EHW}, author = {Gen{\'e}-Mola, Jordi and Gregorio, Eduard and Guevara, Javier and Auat Cheein, Fernando and Sanz, Ricardo and Escol{\`a}, Alexandre and Llorens Calveras, Jordi and Morros, J.R. and Ruiz-Hidalgo, J. and Ver{\'o}nica Vilaplana and Rosell-Polo, Joan R.} } @mastersthesis {xCaros, title = {A Generative Dialogue System for Reminiscence Therapy}, year = {2019}, abstract = {

With people living longer than ever, the number of cases with neurodegenerative diseases such as Alzheimer{\textquoteright}s or cognitive impairment increases steadily. In Spain it affects more than 1.2 million patients and it is estimated that in 2050 more than 100 million people will be affected. While there are not effective treatments for this terminal disease, therapies such as reminiscence, that stimulate memories of the patient{\textquoteright}s past are recommended, as they encourage the communication and produce mental and emotional benefits on the patient. Currently, reminiscence therapy takes place in hospitals or residences, where the therapists are located. Since people that receive this therapy are old and may have mobility difficulties, we present an AI solution to guide older adults through reminiscence sessions by using their laptop or smartphone.\ 

Our solution consists in a generative dialogue system composed of two deep learning architectures to recognize image and text content. An Encoder-Decoder with Attention is trained to generate questions from photos provided by the user, which is composed of a pretrained Convolution Neural Network to encode the picture, and a Long Short-Term Memory to decode the image features and generate the question. The second architecture is a sequence-to-sequence model that provides feedback to engage the user in the conversation.

Thanks to the experiments, we realise that we obtain the best performance by training the dialogue model with Persona-Dataset and fine-tuning it with Cornell Movie-Dialogues dataset. Finally, we integrate Telegram as the interface for the user to interact with Elisabot, our trained conversational agent.

}, author = {Caros, Mariona}, editor = {Radeva, Petia and Xavier Gir{\'o}-i-Nieto} } @conference {cOriol, title = {Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation}, booktitle = {NeurIPS 2019 Workshop on AI for Social Good}, year = {2019}, month = {09/2019}, address = {Vancouver, Canada}, abstract = {

This work addresses the challenge of hate speech detection in Internet memes, and attempts using visual information to automatically detect hate speech, unlike any previous work of our knowledge.\ Memes are pixel-based multimedia documents that contain photos or illustrations together with phrases which, when combined, usually adopt a funny meaning.\ However, hate memes are also used to spread hate through social networks, so their automatic detection would help reduce their harmful societal impact.\ Our results indicate that the model can learn to detect some of the memes, but that the task is far from being solved with this simple architecture.\ While previous work focuses on linguistic hate speech, our experiments indicate how the visual modality can be much more informative for hate speech detection than the linguistic one in memes.\ In our experiments, we built a dataset of 5,020 memes to train and evaluate a multi-layer perceptron over the visual and language representations, whether independently or fused.

Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation from Universitat Polit{\`e}cnica de Catalunya

\>
}, author = {Oriol, Benet and Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCaselles, title = {Integrating low-level motion cues in deep video saliency}, year = {2019}, abstract = {

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

}, keywords = {Motion, Saliency, video}, author = {Caselles, Pol}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xOriol, title = {Multimodal Hate Speech Detection in Memes}, year = {2019}, abstract = {

This thesis explores a multimodal approach to Hate Speech detection, involving vision and language (text). More specifically, we deal with the context of memes, a form of internet humour which will present additional challenges.\ We first gather meme data from different sources. This way, we create a hate memes dataset for this task. Then, we use this data for the training and evaluation of statistical models, which are based on state-of-the art neural networks.\ We study different ways to fine-tune pretrained descriptors for our specific task. We propose a way to add expert knowledge into the system and orient it into a real world issue-solving system. We also discuss ways to deal with the issue of reduced amount of data, experimenting with a self-supervised learning approach for pre-training.\ We also compare the effect or contribution of each modality in the overall performance of the model.

}, author = {Oriol, Benet}, editor = {Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @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 {aPetrone, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using voxelwise analysis of longitudinal structural brain MRI}, journal = {Alzheimer{\textquoteright}s Research \& Therapy}, volume = {11}, year = {2019}, month = {12/2019}, abstract = {

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

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

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

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

}, doi = {https://doi.org/10.1186/s13195-019-0526-8}, url = {https://link.springer.com/article/10.1186/s13195-019-0526-8}, author = {Paula Petrone and Adri{\`a} Casamitjana and Carles Falcon and Miguel Artigues C{\`a}naves and G. Operto and R. Cacciaglia and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cGarcia-Gomez19, title = {Self-registered lidar and polarimetric images in real-time: application to detection of small objects at sea}, booktitle = {7th Workshop on Active Imaging}, year = {2019}, month = {11/2019}, publisher = {French-German Research Institute of Saint-Louis (ISL)}, organization = {French-German Research Institute of Saint-Louis (ISL)}, address = {Saint-Louis, France}, author = {Pablo Garc{\'\i}a-G{\'o}mez and Jordi Riu and Casas, J. and S. Royo} } @article {aCasamitjanaa, title = {Shared latent structures between imaging features and biomarkers in early stages of Alzheimer{\textquoteright}s disease: a predictive study}, journal = {IEEE Journal of Biomedical and Health Informatics}, year = {2019}, month = {08/2019}, abstract = {

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

}, keywords = {CSF biomarkers, Latent model, MRI, PLS, preclinical AD}, doi = {10.1109/JBHI.2019.2932565}, author = {Adri{\`a} Casamitjana and Paula Petrone and J.L. Molinuevo and Juan D. Gispert and Ver{\'o}nica Vilaplana} } @article {aKuijf, title = {Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge}, journal = {IEEE Transactions on Medical Imaging}, year = {2019}, month = {03/2019}, abstract = {

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

}, keywords = {brain, Evaluation and performance, Magnetic resonance imaging (MRI), segmentation}, issn = {0278-0062}, doi = {10.1109/TMI.2019.2905770}, author = {Hugo Kuijf and Matthijs Biesbroek and Jeroen de Bresser and Rutger Heinen and Simon Andermatt and Mariana Bento and Matt Berseth and Mikhail Belyaev and Jorge Cardoso and Adri{\`a} Casamitjana and Louis Collins and Mahsa Dadar and Achileas Georgiou and Mohsen Ghafoorian and Dakai Jin and April Khademi and Jesse Knight and Hongwei Li and Xavier Llado and Miguel Luna and Qaiser Mahmood and Richard McKinley and Alireza Mehrtash and Sebastien Ourselin and Bo-yong Park and Hyunkin Park and Sang Hyun Park and Simon Pezold and Elodie Puybareau and Leticia Rittner and Carole Sudre and Sergi Valverde and Ver{\'o}nica Vilaplana and Rolan Wiest and Yongchao Xu and Ziyue Xu and Guodong Zeng and Jianguo Zhang and Guoyan Zheng and Christoper Chen and Wiesje van der Flier and Frederik Barkhof and Max Viergever and Geert Jan Biessels} } @phdthesis {dCasamitjana19, title = {Study of early stages of Alzheimer{\textquoteright}s disease using magnetic resonance imaging}, volume = {PhD}, year = {2019}, school = {Universitat Polit{\`e}cnica de Catalunya}, address = {Barcelona}, abstract = {

Alzheimer{\textquoteright}s disease (AD) is a neurodegenerative disorder that constitutes the most common dementia pathology. It represents a global epidemic that expands exponentially as the life expectancy increases with no yet useful treatment. Currently, it represents a huge social and economic burden for our societies and it is expected to tension public health infraestructures and finances in the near future.

AD is characterized by amyloid plaque deposition and neurofibrillary tangles measured by ex-vivo examination of the brain. Recent developments in fluid biomarkers and brain imaging allow in-vivo quantification of pathophysiological processes of amyloid deposition or tau tangles formation in the brain, providing the community with highly sensitive and specific in-vivo biomarkers for Alzheimer{\textquoteright}s disease diagnosis. Abnormal levels of these biomarkers are thought as the initiating event to a cascade of subsequent events that continue with synapse loss, cell death, memory impairment, functional dysfunction and cognitive decline. All these events constitute the Alzheimer{\textquoteright}s continuum which can be broadly split into two main parts: an initial long and silent preclinical stage characterized by abnormal AD biomarkers and cognition within the normal range that could last from 15 to 30 years and a posterior clinical stage where subjects develop dementia symptoms.

The etiology of AD is still poorly understood even though several risk factors are identified. Large observational studies can help the study of AD and its related biomarkers and risk factors. In this thesis we provide methodological tools for the analysis of Alzheimer{\textquoteright}s disease using magnetic resonance imaging (MRI). We focus on the study of subjects within the preclinial stage of AD by using statistical learning and pattern recognition frameworks to perform inferential statistics and develop predictive models.

The main outcomes of this thesis are three-fold: firstly, we develop an open-source toolbox for nonlinear neuroimage analysis in population studies. While nonlinear association between medical images and several factors is already known, standard neuroimaging softwares only provide linear statistical frameworks that limit the analyses. Secondly, we study the relationship between brain structure using MRI and the underlying Alzheimer{\textquoteright}s pathology along the disease continuum and at different stages. The close relationship between MRI and clinical symptoms has been widely studied but describing AD using biomarkers instead of clinical phenotypes allows us to study preclinical stages of AD. Finally, we present a framework to predict cognitively unimpaired and amyloid positive subjects using MR imaging and machine learning. We report the results in a cross-sectional study and in a longitudinal study that compares the volumetric rate-of-change between subjects with different amyloid status. We further test the proposed methodology as a part of the triaging process in clinical trials showing great potential benefits.

}, url = {http://hdl.handle.net/10803/668251}, author = {Adri{\`a} Casamitjana}, editor = {Ver{\'o}nica Vilaplana} } @mastersthesis {xCasals19, title = {Synthesis of acne images for data augmentation with generative adversarial networks}, year = {2019}, abstract = {

Generative Adversarial Networks (GANs) are deep learning architectures known for their usefulness on synthesizing new images. Conditioned image generation or the synthesis of super-resolution images are some of their main uses, but they are also helpful when tackling particular image classification and segmentation problems. The latter application is the motivation for the work presented in this document.

This work studies the synthesis of acne images for data augmentation, a procedure validated using said synthetic images to tackle an image classification problem.

The main challenge will be to work around the instability in the training of GANs. Therefore, different known solutions will be implemented in order to overcome this problem.

}, author = {Roger Casals}, editor = {Ver{\'o}nica Vilaplana} } @inbook {bTochon18, title = {Advances in utilization of hierarchical representations in remote sensing data analysis}, booktitle = {Reference Module in Earth Systems and Environmental Sciences}, volume = {2}, year = {2018}, pages = {77-107}, publisher = {Elsevier}, organization = {Elsevier}, issn = {978-0-12-409548-9}, author = {G. Tochon and Dalla Mura, M. and Veganzones, M.A. and Valero, S. and Salembier, P. and Chanussot, J.} } @mastersthesis {xRosello18, title = {Brain lesion segmentation using Convolutional Neuronal Networks}, year = {2018}, abstract = {

Convolutional neural networks (CNN) are powerful tools for learning representations from images. They are being used in a large range of applications, being the state-of-the art in many computer vision tasks. In this work, we study the brain tumor segmentation problem using CNNs and the publicly available BraTS dataset. One of the key factors for this task is which training scheme is used since it should deal with memory constraints and should alleviate the high-imbalance nature between healthy and lesion tissue in the brain.

Thus, the purpose of this project is to propose a comparison between several training schemes and extensively analyze and evaluate them in terms of the dice score. We evaluate densetraining against patch-sampling, and particularly, xed-rule against adaptive sampling scheme. Furthermore, variants and modications of the existing training schemes have been proposed in order to enhance their performance. Finally, several loss functions for each training scheme have been analyzed.

}, author = {Clara Bon{\'\i}n Rosell{\'o}}, editor = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana} } @inbook {bCasamitjana18, title = {Cascaded V-Net Using ROI Masks for Brain Tumor Segmentation}, booktitle = {Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2017}, volume = { 10670}, number = {Lecture Notes in Computer Science}, year = {2018}, pages = {381-391}, publisher = {Springer}, organization = {Springer}, edition = {Crimi A., Bakas S., Kuijf H., Menze B., Reyes M. (eds)}, address = {Cham}, abstract = {

In this work we approach the brain tumor segmentation problem with a cascade of two CNNs inspired in the V-Net architecture, reformulating residual connections and making use of ROI masks to constrain the networks to train only on relevant voxels. This architecture allows dense training on problems with highly skewed class distributions, such as brain tumor segmentation, by focusing training only on the vecinity of the tumor area. We report results on BraTS2017 Training and Validation sets.

}, isbn = {978-3-319-75238-9}, issn = {978-3-319-75237-2}, doi = {https://doi.org/10.1007/978-3-319-75238-9_33}, url = {https://link.springer.com/chapter/10.1007/978-3-319-75238-9_33}, author = {Adri{\`a} Casamitjana and Marcel Cat{\`a} and Irina S{\'a}nchez and Marc Combalia and Ver{\'o}nica Vilaplana} } @article {aPetrone18, title = {Characteristic Brain Volumetric Changes in the AD Preclinical Signature.}, journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association}, volume = {14}, year = {2018}, month = {07/2018}, pages = {P1235}, doi = {10.1016/j.jalz.2018.06.1737}, author = {P. Petrone and Adri{\`a} Casamitjana and C. Falcon and M. Artigues and G. Operto and S. Skouras and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @conference {cPetrone, title = {Characteristic Brain Volumetric Changes in the AD Preclinical Signature}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2018}, month = {07/2018}, address = {Chicago, USA}, abstract = {

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

}, author = {P. Petrone and Adri{\`a} Casamitjana and C. Falcon and M. Artigues and G. Operto and S. Skouras and R. Cacciaglia and J.L. Molinuevo and Ver{\'o}nica Vilaplana and J.D. Gispert} } @mastersthesis {xPelegri18, title = {Clasificaci{\'o}n de im{\'a}genes histol{\'o}gicas mediante redes neuronales convolucionales}, year = {2018}, author = {Joel Bustos Pelegr{\'\i}}, editor = {Marc Combalia and Ver{\'o}nica Vilaplana} } @conference {cFojo, title = {Comparing Fixed and Adaptive Computation Time for Recurrent Neural Network}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2018}, month = {02/2018}, address = {Vancouver, Canada}, abstract = {

Deep networks commonly perform better than shallow ones, but allocating the proper amount of computation for each particular input sample remains an open problem. This issue is particularly challenging in sequential tasks, where the required complexity may vary for different tokens in the input sequence. Adaptive Computation Time (ACT) was proposed as a method for dynamically adapting the computation at each step for Recurrent Neural Networks (RNN). ACT introduces two main modifications to the regular RNN formulation: (1) more than one RNN steps may be executed between an input sample is fed to the layer and and this layer generates an output,\  and (2) this number of steps is dynamically predicted depending on the input token and the hidden state of the network. In our work, we aim at gaining intuition about the contribution of these two factors to the overall performance boost observed when augmenting RNNs with ACT. We design a new baseline, Repeat-RNN, which performs a constant number of RNN state updates larger than one before generating an output. Surprisingly, such uniform distribution of the computational resources matches the performance of ACT in the studied tasks. We hope that this finding motivates new research efforts towards designing RNN architectures that are able to dynamically allocate computational resources.

Reproducing and Analyzing Adaptive Computation Time in PyTorch and TensorFlow from Universitat Polit{\`e}cnica de Catalunya
}, author = {Fojo, Daniel and V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto} } @unpublished {xMoreno18, title = {English to ASL Translator for Speech2Signs}, year = {2018}, month = {01/2018}, type = {Internal report}, abstract = {

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

}, author = {Moreno, Daniel and Costa-juss{\`a}, Marta R. and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {x, title = {Exploring Automatic Speech Recognition with TensorFlow}, year = {2018}, abstract = {

Advisors: Marta R. Costa-juss{\`a} (TALP, UPC) and Xavier Giro-i-Nieto (GPI, UPC)

Grade: A (9.8/10.9)

Speech recognition is the task aiming to identify words in spoken language and convert them into text. This bachelor{\textquoteright}s thesis focuses on using deep learning techniques to build an end-to-end Speech Recognition system. As a preliminary step, we overview the most relevant methods carried out over the last several years. Then, we study one of the latest proposals for this end-to-end approach that uses a sequence to sequence model with attention-based mechanisms. Next, we successfully reproduce the model and test it over the TIMIT database. We analyze the similarities and differences between the current implementation proposal and the original theoretical work. And finally, we experiment and contrast using different parameters (e.g. number of layer units, learning rates and batch sizes) and reduce the Phoneme Error Rate in almost 12\% relative.

Exploring Automatic Speech Recognition with Rensorflow from Universitat Polit{\`e}cnica de Catalunya
}, author = {Escur, Janna}, editor = {Costa-juss{\`a}, Marta R. and Xavier Gir{\'o}-i-Nieto} } @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} } @conference {cBakas19, title = {Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge}, booktitle = {MICCAI - Multimodal Brain Tumor Segmentation Challenge}, year = {2018}, month = {12/2018}, abstract = {

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

}, url = {https://arxiv.org/pdf/1811.02629.pdf}, author = {Spyridon Bakas and Mauricio Reyes and Andras Jakab and Stefan Bauer and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and et. Al.} } @mastersthesis {xColl-Pol, title = {The Importance of Time in Visual Attention Models}, year = {2018}, abstract = {

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

The Importance of Time in Visual Attention Models from Universitat Polit{\`e}cnica de Catalunya
}, author = {Coll-Pol, Marta}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @conference {cCamposa, title = {Importance Weighted Evolution Strategies}, booktitle = {NeurIPS 2018 Deep Reinforcement Learning Workshop }, year = {2018}, month = {11/2018}, address = {Montreal, Quebec}, abstract = {

Evolution Strategies (ES) emerged as a scalable alternative to popular Reinforcement Learning (RL) techniques, providing an almost perfect speedup when distributed across hundreds of CPU cores thanks to a reduced communication overhead. Despite providing large improvements in wall-clock time, ES is data inefficient when compared to competing RL methods. One of the main causes of such inefficiency is the collection of large batches of experience, which are discarded after each policy update. In this work, we study how to perform more than one update per batch of experience by means of Importance Sampling while preserving the scalability of the original method. The proposed method, Importance Weighted Evolution Strategies (IW-ES), shows promising results and is a first step towards designing efficient ES algorithms.

}, author = {V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @article {aDimiccoli18, title = {Introduction to the special issue: Egocentric Vision and Lifelogging}, journal = {Journal of Visual Communication and Image Representation}, year = {2018}, month = {06/2018}, abstract = {

}, doi = {10.1016/j.jvcir.2018.06.010}, url = {https://www.sciencedirect.com/science/article/pii/S1047320318301354}, author = {Dimiccoli, M. and Gurrin, Cathal and Crandall, David and Xavier Gir{\'o}-i-Nieto and Radeva, Petia} } @conference {cCombaliaa, title = {Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification}, booktitle = {Workshop on Deep Learning in Medical Image Analysis, MICCAI}, year = {2018}, month = {2018}, address = {Granada, Spain}, abstract = {

We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared with grid sampling and uniform sampling techniques, it achieves higher generalization performance. We validate the strategy on two artificial datasets and two histological datasets for breast cancer and sun exposure classification.\ 

}, author = {Marc Combalia and Ver{\'o}nica Vilaplana} } @inbook {bCombalia18, title = {Monte-Carlo Sampling Applied to Multiple Instance Learning for Histological Image Classification}, booktitle = {Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support}, number = {11045}, year = {2018}, pages = {274-281}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared with grid sampling and uniform sampling techniques, it achieves higher generalization performance. We validate the strategy on two artificial datasets and two histological datasets for breast cancer and sun exposure classification.

}, isbn = {978-3-030-00889-5}, issn = {10.1007/978-3-030-00889-5}, doi = {10.1007/978-3-030-00889-5}, author = {Marc Combalia and Ver{\'o}nica Vilaplana} } @conference {cCombalia, title = {Monte-Carlo Sampling applied to Multiple Instance Learning for Whole Slide Image Classification}, booktitle = {International Conference on Medical Imaging with Deep Learning}, year = {2018}, month = {07/2018}, address = {Amsterdam, The Netherlands}, author = {Marc Combalia and Ver{\'o}nica Vilaplana} } @article {aCasamitjana, title = {MRI-Based Screening of Preclinical Alzheimer{\textquoteright}s Disease for Prevention Clinical Trials}, journal = {Journal of Alzheimer{\textquoteright}s Disease}, volume = {64}, year = {2018}, month = {07/2018}, chapter = {1099}, abstract = {

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

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

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

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

}, author = {Manuel L{\'o}pez-Palma and Morros, J.R. and Javier Gago and Montserrat Corbal{\'a}n} } @mastersthesis {xCanaves18, title = {Prevention of Alzheimer{\textquoteright}s Disease: a contribution from MRI and machine learning}, year = {2018}, abstract = {

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

}, author = {Miguel Artigues C{\`a}naves}, editor = {Paula Petrone and Ver{\'o}nica Vilaplana} } @conference {cCasamitjanab, title = {Projection to Latent Spaces Disentangles Specific Cerebral Morphometric Patterns Associated to Aging and Preclinical AD}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2018}, month = {07/2018}, address = {Chicago, USA}, abstract = {

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

}, author = {Adri{\`a} Casamitjana and P. Petrone and M. Artigues and J.L. Molinuevo and J.D. Gispert and Ver{\'o}nica Vilaplana} } @article {aCasamitjana18, title = { Projection to Latent Spaces Disentangles Specific Cerebral Morphometric Patterns Associated to Aging and Preclinical AD}, journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association }, volume = {14}, year = {2018}, month = {07/2018}, pages = {P869-P870}, doi = {10.1016/j.jalz.2018.06.1111}, author = {Adri{\`a} Casamitjana and P. Petrone and M. Artigues and J.L. Molinuevo and J.D. Gispert and Ver{\'o}nica Vilaplana} } @conference {cSalvadore, title = {Recurrent Neural Networks for Semantic Instance Segmentation}, booktitle = {CVPR 2018 DeepVision Workshop}, year = {2018}, month = {06/2018}, abstract = {

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

}, author = {Amaia Salvador and M{\'\i}riam Bellver and Baradad, Manel and V{\'\i}ctor Campos and Marqu{\'e}s, F. and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cSalvadord, title = {Recurrent Neural Networks for Semantic Instance Segmentation}, booktitle = {ECCV 2018 Women in Computer Vision (WiCV) Workshop}, year = {2018}, month = {12/2017}, abstract = {

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

Recurrent Neural Networks for Semantic Instance Segmentation from Universitat Polit{\`e}cnica de Catalunya
}, url = {https://imatge-upc.github.io/rsis/}, author = {Amaia Salvador and M{\'\i}riam Bellver and Baradad, Manel and V{\'\i}ctor Campos and Marqu{\'e}s, F. and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xFojo, title = {Reproducing and Analyzing Adaptive Computation Time in PyTorch and TensorFlow}, year = {2018}, abstract = {

The complexity of solving a problem can differ greatly to the complexity of posing that problem. Building a Neural Network capable of dynamically adapting to the complexity of the inputs would be a great feat for the machine learning community. One of the most promising approaches is Adaptive Computation Time for Recurrent Neural Network (ACT) \parencite{act}. In this thesis, we implement ACT in two of the most used deep learning frameworks, PyTorch and TensorFlow. Both are open source and publicly available. We use this implementations to evaluate the capability of ACT to learn algorithms from examples. We compare ACT with a proposed baseline where each input data sample of the sequence is read a fixed amount of times, learned as a hyperparameter during training. Surprisingly, we do not observe any benefit from ACT when compared with this baseline solution, which opens new and unexpected directions for future research.

Reproducing and Analyzing Adaptive Computation Time in PyTorch and TensorFlow from Universitat Polit{\`e}cnica de Catalunya
}, author = {Fojo, Daniel}, editor = {V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto} } @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}, address = {download link}, abstract = {

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

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

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

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

}, url = {http://hdl.handle.net/10803/620762}, author = {X. Lin}, editor = {Casas, J. and M. Pard{\`a}s} } @inbook {bCampos, title = {Sentiment concept embedding for visual affect recognition}, booktitle = {Multimodal Behavior Analysis in theWild}, year = {2018}, publisher = {Elsevier}, organization = {Elsevier}, edition = {1}, chapter = {16}, abstract = {

Automatic sentiment and emotion understanding of general visual content has recently garnered much research attention. However, the large visual variance associated with\ high-level affective concepts presents a challenge when designing systems with high-performance requirements. One\ popular approach to bridge the {\textquotedblleft}affective gap{\textquotedblright} between\ low-level visual features and affective semantics consists of\ using Adjective Noun Pair (ANP) semantic constructs for\ concepts, e.g. {\textquotedblleft}beautiful landscape{\textquotedblright} or {\textquotedblleft}scary face{\textquotedblright} which\ act as a mid-level representation that can be recognized by\ visual classifers while still carrying an affective bias. In\ this work, we formulate the ANP detection task in images\ over a continuous space defined over an embedding that\ captures the inter-concept relationships between ANPs. We\ show how the compact representations obtained from the\ embeddeding extend the discrete concepts in the ontology\ and can be used for improved visual sentiment and emotion\ prediction, as well as new applications such as zero-shot\ ANP detection.

}, url = {https://www.elsevier.com/books/multimodal-behavior-analysis-in-the-wild/alameda-pineda/978-0-12-814601-9}, author = {V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto and Jou, Brendan and Jordi Torres and Chang, Shih-Fu} } @inbook {bCasamitjana18a, title = {Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer{\textquoteright}s Disease}, booktitle = {PRedictive Intelligence in MEdicine}, volume = {11121}, year = {2018}, pages = {60-67}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, abstract = {

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

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

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

}, author = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Paula Petrone and Jose Luis Molinuevo and Juan D. Gispert} } @conference {cCampos18, title = {Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2018}, month = {01/2018}, abstract = {

Recurrent Neural Networks (RNNs) continue to show\  outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time. We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This model can also be encouraged to perform fewer state updates through a budget constraint. We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline RNN models.

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks from Xavier Giro-i-Nieto
}, author = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Jordi Torres and Chang, Shih-Fu} } @conference {cCaminal18, title = {SLAM-based 3D outdoor reconstructions from LIDAR data}, booktitle = {IC3D}, year = {2018}, month = {12/2018}, publisher = {IEEE}, organization = {IEEE}, address = {Brussels, Belgium}, abstract = {

The use of depth (RGBD) cameras to reconstruct large outdoor environments is not feasible due to lighting conditions and low depth range. LIDAR sensors can be used instead. Most state of the art SLAM methods are devoted to indoor environments and depth (RGBD) cameras. We have adapted two SLAM systems to work with LIDAR data. Quantitative evaluations have been performed with LIDAR and RGBD data allowing to compare the systems. Results show that the best method with LIDAR is RTAB-Map with clear difference. Additionally, RTAB-Map has been used to create 3D reconstructions with and without the use of a visible color camera. This proves the potential of Lidar sensors for the reconstructionof outdoor environments for immersion or audiovisual production applications.

}, keywords = {3D imaging, Lidar cameras, mapping, point-cloud processing, SLAM, time-of-flight}, url = {http://www.stereopsia.com/international-conference-3d-immersion-ic3d}, author = {I. Caminal and Casas, J. and S. Royo} } @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 {cDuarte, title = {Towards Speech to Sign Language Translation}, booktitle = {ECCV 2018 Workshop on Shortcomings in Vision and Language}, year = {2018}, month = {08/2018}, abstract = {

Sign Language (SL) is the primary means of communication for a majority of the hearing-impaired community. Current computational approaches in this research area have focused specifically on Sign Language Recognition (SLR) and Sign Language Translation (from SL to text) (SLT). However, the reverse problem of translating from spoken language to sign language has so far been unexplored. The goal of our ongoing project is to provide to people with hearing disabilities the audio tracks from online videos, by automatically generating a video-based speech to sign language translation. In this paper, we will point out the shortcomings that limit the advances of this research area and propose first steps towards this end.

}, author = {Amanda Duarte and Camli, Gorkem and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xArenas, title = {Video Understanding through the Disentanglement of Appearance and Motion}, year = {2018}, abstract = {

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

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

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

}, keywords = {Image color analysis, Image segmentation, Labeling, Object segmentation, Three-dimensional displays, Two dimensional displays}, doi = {10.23919/EUSIPCO.2017.8081170}, url = {https://www.eusipco2017.org/}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @conference {cGorriz, title = {Active Deep Learning for Medical Imaging Segmentation}, booktitle = {Medical Image meets NIPS 2017 Workshop}, year = {2017}, month = {11/2017}, abstract = {

We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using Dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance.

Active Deep Learning for Medical Imaging from Xavier Giro-i-Nieto
}, author = {G{\'o}rriz, Marc and Xavier Gir{\'o}-i-Nieto and Carlier, Axel and Faure, Emmanuel} } @mastersthesis {xGorriz, title = {Active Deep Learning for Medical Imaging Segmentation}, year = {2017}, abstract = {

Grade: A (9.7/10)

This thesis proposes a novel active learning framework capable to train e ectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our approach tries to apply in segmentation existing active learning techniques, which is becoming an important topic today because of the many problems caused by the lack of large amounts of data. We explore di erent strategies to study the image information and introduce a previously used cost-e ective active learning method based on the selection of high con dence predictions to assign automatically pseudo-labels with the aim of reducing the manual annotations. First, we made a simple application for handwritten digit classi cation to get started to the methodology and then we test the system with a medical image database for the treatment of melanoma skin cancer. Finally, we compared the traditional training methods with our active learning proposals, specifying the conditions and parameters required for it to be optimal.

Active Deep Learning for Medical Imaging from Xavier Giro-i-Nieto
}, url = {http://hdl.handle.net/2117/109304}, author = {G{\'o}rriz, Marc}, editor = {Xavier Gir{\'o}-i-Nieto and Carlier, Axel and Faure, Emmanuel} } @conference {cCasamitjanaa, title = {Augmented V-Net for infant brain segmentation}, booktitle = {MICCAI Grand Challenge on 6-month Infant Brain MRI Segmentation, MICCAI 2017}, year = {2017}, month = {09/2017}, author = {Adri{\`a} Casamitjana and Irina S{\'a}nchez and Marc Combalia and Ver{\'o}nica Vilaplana} } @conference {cCasamitjana, title = {Augmented V-Net for White Matter Hyperintensities segmentation}, booktitle = {WMH Segmentation Challenge, Brain-lesion Workshop, MICCAI 2017 }, year = {2017}, month = {09/2017}, author = {Adri{\`a} Casamitjana and Irina S{\'a}nchez and Marc Combalia and Ver{\'o}nica Vilaplana} } @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 {cvan Sabben17, title = {Collaborative voting of 3D features for robust gesture estimation}, booktitle = {International Conference on Acoustics, Speech and Signal Processing}, year = {2017}, month = {03/2017}, address = {New Orleans, USA}, abstract = {

Human body analysis raises special interest because it enables a wide range of interactive applications. In this paper we present a gesture estimator that discriminates body poses in depth images. A novel collaborative method is proposed to learn 3D features of the human body and, later, to estimate specific gestures. The collaborative estimation framework is inspired by decision forests, where each selected point (anchor point) contributes to the estimation by casting votes. The main idea is to detect a body part by accumulating the inference of other trained body parts. The collaborative voting encodes the global context of human pose, while 3D features represent local appearance. Body parts contributing to the detection are interpreted as a voting process. Experimental results for different 3D features prove the validity of the proposed algorithm.

}, author = {van Sabben, D. and Ruiz-Hidalgo, J. and Suau, X. and Casas, J.} } @conference {cGorriza, title = {Cost-Effective Active Learning for Melanoma Segmentation}, booktitle = {ML4H: Machine Learning for Health Workshop at NIPS 2017}, year = {2017}, month = {11/2017}, address = {Long Beach, CA, USA}, abstract = {

We propose a novel Active Learning framework capable to train effectively a convolutional neural network for semantic segmentation of medical imaging, with a limited amount of training labeled data. Our contribution is a practical Cost-Effective Active Learning approach using Dropout at test time as Monte Carlo sampling to model the pixel-wise uncertainty and to analyze the image information to improve the training performance.

Active Deep Learning for Medical Imaging from Xavier Giro-i-Nieto
}, url = {https://arxiv.org/abs/1711.09168}, author = {G{\'o}rriz, Marc and Xavier Gir{\'o}-i-Nieto and Carlier, Axel and Faure, Emmanuel} } @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} } @conference {c, title = {Distributed training strategies for a computer vision deep learning algorithm on a distributed GPU cluster}, booktitle = {International Conference on Computational Science (ICCS)}, year = {2017}, month = {06/2017}, publisher = {Elsevier}, organization = {Elsevier}, address = {Zurich, Switzerland}, abstract = {

Deep learning algorithms base their success on building high learning capacity models with millions of parameters that are tuned in a data-driven fashion. These models are trained by processing millions of examples, so that the development of more accurate algorithms is usually limited by the throughput of the computing devices on which they are trained. In this work, we explore how the training of a state-of-the-art neural network for computer vision can be parallelized on a distributed GPU cluster. The effect of distributing the training process is addressed from two different points of view. First, the scalability of the task and its performance in the distributed setting are analyzed. Second, the impact of distributed training methods on the final accuracy of the models is studied.

[ICCS 2017 website][Related session in ICCS 2017][Paper in UPCommons]

}, keywords = {distributed computing; parallel systems; deep learning; Convolutional Neural Networks}, doi = {https://doi.org/10.1016/j.procs.2017.05.074}, url = {http://www.sciencedirect.com/science/article/pii/S1877050917306129}, author = {V{\'\i}ctor Campos and Sastre, Francesc and Yag{\"u}es, Maurici and M{\'\i}riam Bellver and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @mastersthesis {xCastello17, title = {Extracci{\'o}n de cr{\'a}neo en im{\'a}genes de resonancia magn{\'e}tica del cerebro utilizando una red neuronal convolucional 3D}, year = {2017}, author = {David Rodr{\'\i}guez Castell{\'o}}, editor = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana} } @article {xCampos, title = {From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction}, journal = {Image and Vision Computing}, year = {2017}, abstract = {

Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model.

}, doi = {http://dx.doi.org/10.1016/j.imavis.2017.01.011}, url = {http://arxiv.org/abs/1604.03489}, author = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCampos17, title = {Learning to Skip State Updates in Recurrent Neural Networks}, year = {2017}, abstract = {

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

Grade: A with honours (10.0/10.0)

Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and dificulty in capturing long term dependencies. In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time. We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This network can be encouraged to perform fewer state updates through a novel loss term. We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline models.

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks from Xavier Giro-i-Nieto
}, keywords = {conditional computation, deep learning, machine learning, recurrent neural networks, sequence modeling}, url = {https://imatge-upc.github.io/skiprnn-2017-telecombcn/}, author = {V{\'\i}ctor Campos}, editor = {Jou, Brendan and Chang, Shih-Fu and Xavier Gir{\'o}-i-Nieto} } @article {aPetrone17, title = {Magnetic Resonance Imaging as a valuable tool for Alzheimer{\textquoteright}s disease screening}, journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association}, volume = {13}, year = {2017}, month = {07/2017}, pages = {P1245}, doi = {10.1016/j.jalz.2017.07.457}, url = {https://doi.org/10.1016/j.jalz.2017.07.457}, author = {P. Petrone and Ver{\'o}nica Vilaplana and Adri{\`a} Casamitjana and D. Sanchez-Escobedo and A. Tucholka and R. Cacciaglia and G. Operto and S. Skouras and C. Falcon and J.L. Molinuevo and J.D. Gispert} } @conference {cPetrone17, title = {Magnetic Resonance Imaging as a valuable tool for Alzheimer{\textquoteright}s disease screening}, booktitle = {Alzheimer{\textquoteright}s Association International Conference, London, 2017}, year = {2017}, month = {07/2017}, author = {P. Petrone and Ver{\'o}nica Vilaplana and Adri{\`a} Casamitjana and A. Tucholka and C. Falcon and R. Cacciaglia and G. Operto and S. Skouras and J.L. Molinuevo and J.D. Gispert} } @conference {cCata, title = {Masked V-Net: an approach to brain tumor segmentation}, booktitle = {Multimodal Brain Tumor Segmentation Benchmark, Brain-lesion Workshop, MICCAI 2017 }, year = {2017}, month = {09/2017}, author = {Marcel Cat{\`a} and Adri{\`a} Casamitjana and Irina S{\'a}nchez and Marc Combalia and Ver{\'o}nica Vilaplana} } @conference {cFernandez, title = {More cat than cute? Interpretable Prediction of Adjective-Noun Pairs}, booktitle = {ACM Multimedia 2017 Workshop on Multimodal Understanding of Social, Affective and Subjective Attributes}, year = {2017}, month = {10/2017}, publisher = {ACM SIGMM}, organization = {ACM SIGMM}, address = {Mountain View, CA (USA)}, abstract = {

The increasing availability of affect-rich multimedia resources has bolstered interest in understanding sentiment and emotions in and from visual content. Adjective-noun pairs (ANP) are a popular mid-level semantic construct for capturing affect via visually detectable concepts such as {\textquoteleft}{\textquoteleft}cute dog" or {\textquoteleft}{\textquoteleft}beautiful landscape". Current state-of-the-art methods approach ANP prediction by considering each of these compound concepts as individual tokens, ignoring the underlying relationships in ANPs. This work aims at disentangling the contributions of the {\textquoteleft}adjectives{\textquoteright} and {\textquoteleft}nouns{\textquoteright} in the visual prediction of ANPs. Two specialised classifiers, one trained for detecting adjectives and another for nouns, are fused to predict 553 different ANPs. The resulting ANP prediction model is more interpretable as it allows us to study contributions of the adjective and noun components.

}, doi = {10.1145/3132515.3132520}, author = {Fern{\`a}ndez, D{\`e}lia and Woodward, Alejandro and V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Chang, Shih-Fu} } @mastersthesis {xCompri, title = {Multi-label Remote Sensing Image Retrieval based on Deep Features}, year = {2017}, abstract = {

Student: Michele Compri

Advisors: Beg{\"u}m Demir (University of Trento) and Xavier Giro-i-Nieto (UPC)

Recent advances in satellite technology has led to an increased volume of remote sensing (RS) image archives, from which retrieving useful information is challenging. Thus, one important research area in remote sensing (RS) is the content-based retrieval of RS images (CBIR). The performance of the CBIR systems depends on the capability of the RS image features in modeling the content of the images as well as the considered retrieval algorithm that assesses the similarity among the features. Existing CBIR systems in the RS literature assume that each image is categorized by only a single label in terms of a land-cover class that is associated to the most significant content of the image. However, RS images usually have complex content, i.e., there are usually several regions within each image related to multiple land-cover classes. Thus, available CBIR systems are not capable of accurately characterizing and exploiting the high level semantic content of RS images for retrieval problems.

To overcome these problems and to effectively characterize the high-level semantic content of RS images, we investigate effectiveness of different deep learning architectures in the framework of multi-label remote sensing image retrieval problems. This is achieved based on a two-steps strategy. In the first step, aConvolutional Neural Network (CNN) pre-trained for image classification with the ImageNet dataset is used off-the-shelf as a feature extractor. In particular, three popular architectures are explored: 1) VGG16; 2) Inception V3; and 3) ResNet50. VGG16 is a CNN characterized by 16 convolutional layers of stacked 3x3 filters, with intermediate max pooling layers and 3 fully connected layers at the end. Inception V3 is an improved version of the former GoogleNet, which contains more layers but less parameters, by removing fully connected layers and using a global average pooling from the last convolutional layer. ResNet50 is even deeper thanks to the introduction of residual layers, that allow data to flow by skipping the convolutional blocks. In he second step of our research, we modify these three off-the-shelf models by fine-tunning their parameters with a subset of RS images and their multi-label information. Experiments carried out on an archive of aerial images show that fine-tuning CNN architectures with annotated images with multi-labels significantly improve the retrieval accuracy with respect to the standard CBIR methods. We find that fine-tunning using with a multi-class approach achieves better results than than considering each label as an independent class. Due to the space constraints, the detailed information on each step of the proposed method will be given in the full version of the paper.\ 

[source code on GitHub]

Multi-label Remote Sensing Image Retrieval based on Deep Features from Xavier Giro
}, author = {Compri, Michele}, editor = {Demir, Beg{\"u}m and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCardoner, title = {Predicting Media Interestingness}, year = {2017}, abstract = {

This thesis explores the application of a deep learning approach for the prediction of media\ interestingness. Two di erent models are investigated, one for the prediction of image and one\ for the prediction of video interestingness.\ For the prediction of image interestingness, the ResNet50 network is ne-tuned to obtain\ best results. First, some layers are added. Next, the model is trained and fine-tuned using data\ augmentation, dropout, class weights, and changing other hyper parameters.\ For the prediction of video interestingness, rst, features are extracted with a 3D convolutional\ network. Next a LSTM network is trained and fine-tuned with the features.\ The nal result is a binary label for each image/video: 1 for interesting, 0 for not interesting.\ Additionally, a con fidence value is provided for each prediction. Finally, the Mean Average\ Precision (MAP) is employed as evaluation metric to estimate the quality of the fi nal results.

Predicting Media Interestingness from Xavier Giro-i-Nieto
}, author = {Cardoner, LLuc}, editor = {Zaharieva, Maia and Xavier Gir{\'o}-i-Nieto} } @conference {cPujol-Miro17, title = {Registration of Images to Unorganized 3D Point Clouds Using Contour Cues}, booktitle = {The 25th European Signal Processing Conference (EUSIPCO 2017)}, year = {2017}, month = {08/2017}, publisher = {Eurasip}, organization = {Eurasip}, address = {Kos island, Greece}, abstract = {

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

}, keywords = {Cameras, Feature extraction, Iterative closest point algorithm, Sensors, Signal processing algorithms, Three-dimensional displays}, doi = {10.23919/EUSIPCO.2017.8081173}, url = {https://www.eusipco2017.org/}, author = {A. Pujol-Mir{\'o} and Ruiz-Hidalgo, J. and Casas, J.} } @conference {cPana, title = {SalGAN: Visual Saliency Prediction with Generative Adversarial Networks}, booktitle = {CVPR 2017 Scene Understanding Workshop (SUNw)}, year = {2017}, address = {Honolulu, Hawaii, USA}, abstract = {

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

}, url = {https://arxiv.org/abs/1701.01081}, author = {Pan, Junting and Cristian Canton-Ferrer and McGuinness, Kevin and O{\textquoteright}Connor, N. and Jordi Torres and Elisa Sayrol and Xavier Gir{\'o}-i-Nieto} } @conference {cTorres, title = {Scaling a Convolutional Neural Network for classification of Adjective Noun Pairs with TensorFlow on GPU Clusters}, booktitle = {17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)}, year = {2017}, month = {05/2017}, publisher = {IEEE}, organization = {IEEE}, address = {Madrid, Spain}, abstract = {

Deep neural networks have gained popularity in recent years, obtaining outstanding results in a wide range of applications such as computer vision in both academia and multiple industry areas. The progress made in recent years cannot be understood without taking into account the technological advancements seen in key domains such as High Performance Computing, more specifically in the Graphic Processing Unit (GPU) domain. These kind of deep neural networks need massive amounts of data to effectively train the millions of parameters they contain, and this training can take up to days or weeks depending on the computer hardware we are using. In this work, we present how the training of a deep neural network can be parallelized on a distributed GPU cluster. The effect of distributing the training process is addressed from two different points of view. First, the scalability of the task and its performance in the distributed setting are analyzed. Second, the impact of distributed training methods on the training times and final accuracy of the models is studied. We used TensorFlow on top of the GPU cluster of servers with 2 K80 GPU cards, at Barcelona Supercomputing Center (BSC). The results show an improvement for both focused areas. On one hand, the experiments show promising results in order to train a neural network faster. The training time is decreased from 106 hours to 16 hours in our experiments. On the other hand we can observe how increasing the numbers of GPUs in one node rises the throughput, images per second, in a near-linear way. Morever an additional distributed speedup of 10.3 is achieved with 16 nodes taking as baseline the speedup of one node.

[CCGRID 2017] [Paper page]

}, url = {http://easychair.org/smart-program/CCGRID2017/2017-05-15.html$\#$session:13550}, author = {Jordi Torres and Sastre, Francesc and Yag{\"u}es, Maurici and V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto} } @conference {cBazazian16, title = {Segmentation-based Multi-Scale Edge Extraction to Measure the Persistence of Features in Unorganized Point Clouds}, booktitle = {International Conference on Computer Vision Theory and Applications}, year = {2017}, month = {02/2017}, address = {Porto, Portugal}, abstract = {

Edge extraction has attracted a lot of attention in computer vision. The accuracy of extracting edges in point clouds can be a significant asset for a variety of engineering scenarios. To address these issues, we propose a segmentation-based multi-scale edge extraction technique.\ 

In this approach, different regions of a point cloud are segmented by a global analysis according to the geodesic distance. Afterwards, a multi-scale operator is defined according to local neighborhoods. Thereupon, by applying this operator at multiple scales of the point cloud, the persistence of features is determined. We illustrate the proposed method by computing a feature weight that measures the likelihood of a point to be an edge, then detects the edge points based on that value at both global and local scales. Moreover, we evaluate quantitatively and qualitatively our method. Experimental results show that the proposed approach achieves a superior accuracy. Furthermore, we demonstrate the robustness of our approach in noisier real-world datasets.

}, doi = {10.5220/0006092503170325}, url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220\%2f0006092503170325}, author = {D. Bazazian and Casas, J. and Ruiz-Hidalgo, J.} } @conference {cCampos, title = {Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks}, booktitle = {NIPS Time Series Workshop 2017}, year = {2017}, month = {08/2017}, address = {Long Beach, CA, USA}, abstract = {

Recurrent Neural Networks (RNNs) continue to show \ outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long term dependencies. In backpropagation through time settings, these issues are tightly coupled with the large, sequential computational graph resulting from unfolding the RNN in time. We introduce the Skip RNN model which extends existing RNN models by learning to skip state updates and shortens the effective size of the computational graph. This model can also be encouraged to perform fewer state updates through a budget constraint. We evaluate the proposed model on various tasks and show how it can reduce the number of required RNN updates while preserving, and sometimes even improving, the performance of the baseline RNN models.

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks from Xavier Giro-i-Nieto
}, url = {https://imatge-upc.github.io/skiprnn-2017-telecombcn/}, author = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Jordi Torres and Chang, Shih-Fu} } @conference {cCasamitjana16, title = {3D Convolutional Neural Networks for Brain Tumor Segmentation}, booktitle = {MICCAI 2016 - Brain Lesion Workshop (BrainLes), Multimodal Brain Tumor Segmentation Challenge (BRATS)}, year = {2016}, month = {11/2016}, author = {Adri{\`a} Casamitjana and Santi Puch and Asier Aduriz and Elisa Sayrol and Ver{\'o}nica Vilaplana} } @conference {cLin16a, title = {3D Point Cloud Segmentation Oriented to The Analysis of Interactions}, booktitle = {The 24th European Signal Processing Conference (EUSIPCO 2016)}, year = {2016}, month = {08/2016}, publisher = {Eurasip}, organization = {Eurasip}, address = {Budapest, Hungary}, abstract = {

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

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

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

}, issn = {978-3-319-49409-8}, doi = {10.1007/978-3-319-49409-8}, url = {http://eecs.oregonstate.edu/IWVS_workshop/}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @article {a, title = {Assessment of Crowdsourcing and Gamification Loss in User-Assisted Object Segmentation}, journal = {Multimedia Tools and Applications}, volume = {23}, year = {2016}, month = {11/2016}, chapter = {15901-15928}, abstract = {

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

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

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

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

}, keywords = {Crowdsourcing, GWAP, Object detection, Object segmentation, Serious games}, issn = {1573-7721}, doi = {10.1007/s11042-015-2897-6}, url = {http://dx.doi.org/10.1007/s11042-015-2897-6}, author = {Carlier, Axel and Amaia Salvador and Cabezas, Ferran and Xavier Gir{\'o}-i-Nieto and Charvillat, Vincent and Marques, Oge} } @mastersthesis {xFernandez, title = {Clustering and Prediction of Adjective-Noun Pairs for Affective Computing}, year = {2016}, abstract = {

Student: D{\`e}lia Fern{\`a}ndez

Advisors: V{\'\i}ctor Campos (UPC), Brendan Jou (Columbia University), Xavier Gir{\'o}-i-Nieto (UPC) and Shih-Fu Chang (Columbia University)

Grade: A+ (10.0/10.0) - Best Master Thesis award (Class 2016)

One of the main problems in visual affective computing is overcoming the affective gap between low-level visual features and the emotional content of the image. One rising method to capture visual affection is through the use of Adjective-Noun Pairs (ANP), a mid-level affect representation. This thesis addresses two challenges related to ANPs: representing ANPs in a structured ontology and improving ANP detectability. The first part develops two techniques to exploit relations between adjectives and nouns for automatic ANP clustering. The second part introduces and analyzes a novel deep neural network for ANP prediction. Based on the hypothesis of a different contribution of the adjective and the noun depending of the ANP, the novel network fuses the feature representations of adjectives and nouns from two independently trained convolutional neural networks.

}, author = {Fern{\`a}ndez, D{\`e}lia}, editor = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Chang, Shih-Fu} } @mastersthesis {xCarne-Herrera16, title = {Detect Snap Points in Egocentric Images with Physiological Signals}, year = {2016}, abstract = {

This project addresses a novel problem that has appears in the last years. The use of egocentric cameras, devices that takes images of what we see are growing and the main problem of this images are big data (because at the end of the day, we can have a thousand of images, some of them similar and sometimes with a bad quality or low information) and image retrieval (due to the big data, find a certain moment are very difficult and if we don{\textquoteright}t avoid that problem, the properties of egocentric images became useless).

This work have two objectives: the first one explore images that have physiological signals associated in order to allow us to add some physiological features for the retrieval instead of only base the retrieval in visual features as the actual state of the art. For this part we associate interesting images to the images that are memorable, so a correlation between memorability and physiological signals will be found. The second objective is to deal with the egocentric paradigm. Some recent works shows that machine learning algorithms that have been trained with human-taken images cannot be extended to egocentric images due to the image construction. Based on MIT (Massachusetts Institute of Technology) previous work I built a visual game that allows me to manually annotate the memorability of the images with a simple user interaction (the user don{\textquoteright}t know that he is annotating images during the game). From this game I have computed memorability score and I have obtain predicted scores from a convolutional neural network that MIT present in his work: MemNet. From both results I have compared the results in order to decide if the application of the algorithms on egocentric images is possible.

}, author = {Carn{\'e}-Herrera, Marc}, editor = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @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.} } @article {xCarne-Herrera, title = {EgoMemNet: Visual Memorability Adaptation to Egocentric Images}, year = {2016}, month = {06/2016}, institution = {4th Workshop on Egocentric (First-Person) Vision, CVPR 2016}, address = {Las Vegas, NV, USA}, abstract = {

This work explores the adaptation of visual memorability\ prediction for photos intentionally captured by handheld\ cameras, to images passively captured from an egocentric\ point of view by wearable cameras. The estimation of a visual\ memorability score for an egocentric images is a valuable\ cue when filtering among the large amount of photos\ generated by wearable cameras. For this purpose, a new\ annotation tool and annotated dataset are presented, used\ to fine-tune a pre-trained convolutional neural network.

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

}, author = {Carn{\'e}-Herrera, Marc and Xavier Gir{\'o}-i-Nieto and Gurrin, Cathal} } @mastersthesis {xCherto, title = {EgoMon Gaze and Video Dataset for Visual Saliency Prediction}, year = {2016}, abstract = {

This project focuses on the creation of a new type of egocentric (first person) vision dataset. For that purpose, the EgoMon Gaze \& Video Dataset is presented. This EgoMon dataset was recorded using the eye gaze tracking technology that studies the movement and position of the eyes. The Tobii glasses (wearable, eye tracker and head-mounted device) were the main tool used to record and extract the gaze data for this dataset. The dataset consists in 7 videos of 34 minutes each one of average, 13428 frames extracted from each video (with a frequency of 1 fps), and 7 files with the gaze data (fixations points of the wearer of the glasses) for each frame and video. The videos were recorded in the city of Dublin (Ireland) both indoor and outdoor. The generated dataset has been used to evaluate the performance of a state of art model for visual saliency prediction on egocentric video.

[Project page]

Grade: B (8.2/10.0)

EgoMon Gaze and Video Dataset for Visual Saliency Prediction from Xavier Giro
}, author = {Chert{\'o}, M{\`o}nica}, editor = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCata16, title = {Feature Selection Methods for Predicting Pre-Clinical Stage in Alzheimer{\textquoteright}s Disease}, year = {2016}, abstract = {

Advisors: Ver{\'o}nica Vilaplana, Adri{\`a} Casamitjana

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

Alzheimer{\textquoteright}s disease is still an incurable disease. Nevertheless, some of its biomarkers suffer changes in the early stages of the disease, long before clinical symptoms appear. In order to determine how biomarkers obtained from magnetic resonance (MRI) techniques affect the disease{\textquoteright}s evolution, machine learning techniques have been used to design and implement a classification system so as to predict the stages in which several patients belong. One of the main objectives of this project is reducing the number of data to manage, since MRI provide a large volume of data for each patient. As a result, we will focus on the stage of reduction and extraction of characteristics of the classifier which may be relevant for the mentioned problem. We will carry out an exhaustive analysis of different methods of selection of features to apply to biomedical data related to Alzheimer{\textquoteright}s disease. Results obtained will also be applicable to other fields. Finally, we will assess these methods with a multimodal data base provided by the collaboration agreement with Pasqual Maragall Foundation (FPM).

}, author = {Marcel Cat{\`a}}, editor = {Ver{\'o}nica Vilaplana and Adri{\`a} Casamitjana} } @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} } @unpublished {xFernandeza, title = {Is a {\textquotedblleft}happy dog{\textquotedblright} more {\textquotedblleft}happy{\textquotedblright} than {\textquotedblleft}dog{\textquotedblright}? - Adjective and Noun Contributions for Adjective-Noun Pair prediction}, journal = {NIPS Women in Machine Learning Workshop}, year = {2016}, month = {12/2016}, address = {Barcelona}, abstract = {

Computers are acquiring increasing ability to detect high level visual content such as objects in images, but often lack an affective comprehension of this content. Affective computing is useful for behavioral sciences, with applications in brand monitoring or advertisement effect. The main problem of the visual task of mapping affect or emotions to images is overcoming the affective gap between low-level features and the image emotional content.

One rising method to capture visual affections is through the use of Adjective-Noun Pair (ANP). ANPs were introduced as a mid-level affect representation to overcome the affective gap by combining nouns, which define the object content, and adjectives, which add a strong emotional bias, yielding concepts such as {\textquotedblleft}happy dog{\textquotedblright} or {\textquotedblleft}misty morning{\textquotedblright}.

Current state of the art methods approach ANP prediction by training visual classifiers on these pairs. In this work, we hypothesize that the visual contribution between nouns and adjectives differ between ANPs. We propose a feature-based intermediate representation for ANP prediction using specialized convolutional networks for adjectives and nouns separately. By fusing a representation from nouns and adjectives, the network learns how much the nouns and adjectives contribute to each ANP, which a single tower network does not allow.

The specialized noun and adjective networks follow an AlexNet-styled architecture. These networks are fused into an intermediate feature representation, and ANPs are then learned from it using a fully-connected network. We investigate noun and adjective contributions with two kinds of fusions. First fusion uses the output of the softmax layer: these are class-probability features, so all dimensions have class-correspondence to adjectives and nouns. Second fusion uses the fc7 layer output: these features contain visual information, allowing interpretation of adjective and noun visual relevance. For the feature contributions of each ANP, we compute a deep Taylor decomposition [1].

For experiments, we use a subset of 1,200 ANPs from the tag-based English-MVSO [2] dataset. ANPs are composed by the combination of 350 adjective and 617 nouns. With identical settings to the adjective and noun networks, an ANP classification network is trained end-to-end as the baseline. Using the fc7 features, we improve over the baseline in both top-1 and top-5 accuracy. Also, we observe adjective and nouns contribute differently between ANPs; e.g. for the ANP {\textquotedblleft}pregnant woman{\textquotedblright}, the adjective contributes the most, while for {\textquotedblleft}cute cat{\textquotedblright} the predominant contribution is in the noun. Using the probability features we find other insights, as nouns or adjectives co-occurring together, e.g. for {\textquotedblleft}happy halloween{\textquotedblright} the contribution is also high of the nouns {\textquotedblleft}blood{\textquotedblright} and {\textquotedblleft}cat{\textquotedblright}, and of the adjectives {\textquotedblleft}haunted{\textquotedblright} and {\textquotedblleft}dark{\textquotedblright}.\ 

Based on experiment results, we confirm our hypothesis of adjective and nouns contributing differently to ANP concepts. Furthermore, our architecture proves to outperform traditional methods by giving insights on the role of adjectives and nouns on the prediction.

[1] Montavon, Gr{\'e}goire, et al. "Deep Taylor Decomposition of Neural Networks." ICML Workshop on Visualization for Deep Learning, 2016.

[2] Jou, Brendan, et al. "Visual affect around the world: A large-scale multilingual visual sentiment ontology." ACMM, 2015.

}, author = {Fern{\`a}ndez, D{\`e}lia and V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Chang, Shih-Fu} } @conference {cCasanova16, title = {Interactive Registration Method for 3D data Fusion}, booktitle = {IC3D}, year = {2016}, month = {12/2016}, publisher = {IEEE}, organization = {IEEE}, address = {Li{\`e}ge, Belgium}, abstract = {

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

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

}, url = {http://www.3dstereomedia.eu/ic3d}, author = {A. Casanova and A. Pujol-Mir{\'o} and Ruiz-Hidalgo, J. and Casas, J.} } @conference {cdeOliveira-Barra, title = {LEMoRe: A Lifelog Engine for Moments Retrieval at the NTCIR-Lifelog LSAT Task}, booktitle = {The 12th NTCIR Conference, Evaluation of Information Access Technologies}, year = {2016}, month = {06/2016}, publisher = {National Institute of Informatics (NII)}, organization = {National Institute of Informatics (NII)}, address = {Tokyo, Japan}, abstract = {

Semantic image retrieval from large amounts of egocentric visual data requires to leverage powerful techniques for filling in the semantic gap. This paper introduces LEMoRe, a Lifelog Engine for Moments Retrieval, developed in the context of the Lifelog Semantic Access Task (LSAT) of the the NTCIR-12 challenge and discusses its performance variation on different trials. \ LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing. \ Although this is just a first attempt towards interactive image retrieval from large egocentric datasets and there is a large room for improvement of the system components and the user interface, the structure of the system itself and the way the single components cooperate are very promising.

LEMoRe - A Lifelog Engine for Moments Retrieval at NTCIR-12 from University of Barcelona
}, url = {http://research.nii.ac.jp/ntcir/workshop/OnlineProceedings12/NTCIR/toc_ntcir.html$\#$Lifelog}, author = {Gabriel de Oliveira-Barra and Xavier Gir{\'o}-i-Nieto and Cartas-Ayala, Alejandro and Radeva, Petia} } @conference {cIndia, title = {UPC System for the 2016 MediaEval Multimodal Person Discovery in Broadcast TV task}, booktitle = {MediaEval 2016 Workshop}, year = {2016}, month = {10/2016}, address = {Hilversum, The Netherlands}, abstract = {

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

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

Student: Andrea Calafell

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

Program: Master in Computer Vision (Class of 2016)

Grade: A (9.0/10.0)

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

Video Retrieval of Specific Persons in Specific Locations from Xavier Giro
}, author = {Calafell, Andrea}, editor = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCarne-Herreraa, title = {Visual Memorability for Egocentric Cameras}, year = {2016}, abstract = {

This project explores visual memorability of egocentric in different ways having three main\ contributions. The first and the main contribution of the project is a new tool visual\ memorability in egocentric images. This tool that consists in a web application that allows\ the annotation of the visual memorability associated to still images with an online game.\ The second contribution of this work is a convolutional neural network model for visual\ memorability prediction that adapts an off-the-shelf model to egocentric images. Moreover,\ a visualization study has been pursued to localize the regions of the images that are more\ memorable than others. With this maps a comparison with saliency maps and is explored.\ This part of the research opens a new branch in visual memorability that consists in use\ memorability maps for saliency prediction. Also the memorability of the images is related\ with a sentiment analysis applying a model that predicts that feature.\ The final contribution is related to join visual memorability of images with human\ behaviour and physical state, finding a relation between memory and some physiological\ signals as: heart rate, galvanic skin response and electroencephalographic signals.

Grade: A with honors (9.8/10.)

Visual Memorability for Egocentric Cameras from Xavier Giro
}, author = {Carn{\'e}-Herrera, Marc}, editor = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @conference {cPuch, title = {Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling}, booktitle = {NIPS 2016 Workshop on Machine Learning for Health}, year = {2016}, month = {12/2016}, author = {Santi Puch and Asier Aduriz and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @mastersthesis {xCabezas, title = {Co-filtering human interaction and object segmentation}, year = {2015}, abstract = {

Advisors: Axel Carlier and Vincent Charvillat (ENSEEIHT-Universit{\'e} de Toulouse) / Amaia Salvador and\ Xavier Gir{\'o}-i-Nieto\ (UPC)\ 

Degree: Audiovisual Engineering (4 years) at\ Telecom BCN-ETSETB\ (UPC)

Grade: A with honors (9.6/10)

For so many years the problem of object segmentation has been present in image processing field. Click{\textquoteright}n{\textquoteright}Cut, an already existing web tool for interactive object segmentation, helps us to obtain segmentations of the objects by clicking in green (foreground clicks) inside the object to segment, and in red(background clicks) outside the object to segment. However, the behaviour of all human in front of this web tool is not equal. For this reason, it can be possible that these human interactions do not help us to obtain a good object segmentation as would be the result of a bad human interaction. The main aim of this project is to implement some techniques that allow us to treat with these bad human interactions in order to obtain the best object segmentation possible.

Co-filtering human interaction and object segmentation from Xavi Gir{\'o}
}, url = {http://hdl.handle.net/2099.1/25821}, author = {Cabezas, Ferran}, editor = {Carlier, Axel and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Charvillat, Vincent} } @conference {cSalvadora, title = {Cultural Event Recognition with Visual ConvNets and Temporal Models}, booktitle = {CVPR ChaLearn Looking at People Workshop 2015}, year = {2015}, month = {06/2015}, abstract = {

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

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

Cultural Event Recognition with Visual ConvNets and Temporal Models from Xavier Giro
}, url = {http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W09/papers/Salvador_Cultural_Event_Recognition_2015_CVPR_paper.pdf}, author = {Amaia Salvador and Zeppelzauer, Matthias and Manchon-Vizuete, Daniel and Calafell, Andrea and Xavier Gir{\'o}-i-Nieto} } @conference {cCampos-Camunez, title = {Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction}, booktitle = {1st International Workshop on Affect and Sentiment in Multimedia}, year = {2015}, month = {10/2015}, publisher = {ACM}, organization = {ACM}, address = {Brisbane, Australia}, abstract = {

Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose. In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting. Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.

Acceptance rate at the ASM workshop{\textquoteright}15: 56\% (9/16) [source]

Diving deep into sentiment: Understanding fine-tuned CNNs for visual sentiment prediction from Xavier Giro

Visual Sentiment Detection by Deep CNNs $\#$acmmm15 @DocXavi @mad_astronaut @brendanjou - great paper/analysis! pic.twitter.com/RcGchcPA9f

{\textemdash} Damian Borth (@damianborth) October 30, 2015
}, doi = {10.1145/2813524.2813530}, url = {http://arxiv.org/abs/1508.05056}, author = {V{\'\i}ctor Campos and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Jou, Brendan} } @conference {cBazazian15, title = {Fast and Robust Edge Extraction in Unorganized Point Clouds}, booktitle = {International Conference on Digital Image Computing: Techniques and Applications}, year = {2015}, month = {11/2015}, publisher = {DICTA/IEEE}, organization = {DICTA/IEEE}, address = {Adelaide, Australia}, abstract = {

Edges provide important visual information in scene surfaces. The need for fast and robust feature extraction from 3D data is nowadays fostered by the widespread availability of cheap commercial depth sensors and multi-camera setups. This article investigates the challenge of detecting edges in surfaces represented by unorganized point clouds. Generally, edge recognition requires the extraction of geometric features such as normal vectors and curvatures. Since the normals alone do not provide enough information about the geometry of the cloud, further analysis of extracted normals is needed for edge extraction, such as a clustering method. Edge extraction through these techniques consists of several steps with parameters which depend on the density and the scale of the point cloud. In this paper we propose a fast and precise method to detect sharp edge features by analysing the eigenvalues of the covariance matrix that are defined by each point{\textquoteright}s k-nearest neighbors. Moreover, we evaluate quantitatively, and qualitatively the proposed methods for sharp edge extraction using several dihedral angles and well known examples of unorganized point clouds. Furthermore, we demonstrate the robustness of our approach in the noisier real-world datasets.

}, doi = {10.1109/DICTA.2015.7371262}, url = {http://dictaconference.org/dicta2015/}, author = {D. Bazazian and Casas, J. and Ruiz-Hidalgo, J.} } @mastersthesis {xCalafell-Oros, title = {Fine-tuning a Convolutional Network for Cultural Event Recognition}, year = {2015}, abstract = {

Advisors: Amaia Salvador (UPC), Matthias Zeppelzauer (FH St P{\"o}lten), Xavier Gir{\'o}-i-Nieto (UPC)

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

Grade: A with honors (10/10)

This thesis explores good practices for improving the performance of an existing convnet trained with a dataset of clean data when an additional dataset of noisy data is available. We develop techniques to clean the noisy data with the help of the clean one, a family of solutions that we will refer to as denoising, and then we explore the best sorting of the clean and noisy datasets during the fine-tuning of a convnet. Then we study strategies to select the subset of images of the clean data that will improve the classification performance, a practice we will efer to as fracking. Next, we determine how many layers are actually better to fine-tune in our convnet, given our amount of data. And finally, we compare the classic convnet architecture where a single network is fine-tuned to solve a multi-class problem with the case of fine-tuning a convnet for binary classification for each considered class.

Fine tuning a convolutional network for cultural event recognition from Xavier Giro

2015-TFG-AndreaCalafell-FineTuningConvolutionalNetworkForCulturalEventRecognition from Image Processing Group on Vimeo.

See https://imatge.upc.edu/web/publications/cultural-event-recognition-visual-convnets-and-temporal-models

}, url = {http://hdl.handle.net/2117/78391}, author = {Calafell, Andrea}, editor = {Amaia Salvador and Zeppelzauer, Matthias and Xavier Gir{\'o}-i-Nieto} } @article {xCanton-Ferrer15, title = {From Catalonia to America: notes on how to achieve a successful post-PhD career}, year = {2015}, abstract = {

Presented at the 2nd Annual Catalan Meeting on Computer Vision - ACMCV (September 18, 2015)

Talk summary

Reaching the green prairies where the big fat tech companies graze has been historically regarded as a hard and painful journey for young freshly graduated PhDs. The amount of contenders is large and the competition may be overwhelming but, despite all, you often see researchers related to Catalan universities that have found a prominent role in companies like Microsoft, Google, Facebook, etc. By talking to them you start to detect some common factors that contributed to the success of their careers and some common views on the pros/cons related to their experience in big corporations. In this talk, I will present some hints/advice/directions on how to bridge the gap between the end of a PhD and the start of a professional career in the software industry. What is needed? What is the posture of big companies w.r.t. research? Which are our strong assets coming out from a Catalan university (and which are not!)?

Short bio about the speaker

Cristian Canton (Barcelona, 1980) got his MSc in Telecommunications Engineering by the Technical University of Catalonia (UPC) in 2003, carrying out his MSc Thesis at the Swiss Federal Institute of Technology of Lausanne (EPFL), covering topics in image processing and coding. After that, he pursued his PhD at UPC on computer vision and machine learning topics applied to human motion capture in multi-view scenarios. During this period, he was visiting researcher at Technion (Haifa, Israel) and Koc University (Istanbul, Turkey) and took active roles in several EU founded projects. In 2008, he was a Postdoc researcher at NXP in Paris where he developed gesture-based HCIs for mobile devices. In 2009, he joined VICON Ltd., a leading company in the field of vision-based motion capture systems for entertainment and biomedical industries. During 2010-2012, he was EU Project reviewer. He joined Microsoft in 2012 as a senior researcher and developer where transfers technology into real life products such as Surface Hub, Hololens and Skype.

}, keywords = {america, catalonia, computer Vision, job, microsoft}, author = {Cristian Canton-Ferrer} } @mastersthesis {xCampos-Camunez, title = {Layer-wise CNN Surgery for Visual Sentiment Prediction}, year = {2015}, abstract = {

Advisors: Amaia Salvador (UPC), Brendan Jou (Columbia University) and Xavier Gir{\'o}-i-Nieto (UPC)

Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose. In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting. Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.

Layer-wise CNN Surgery for Visual Sentiment Prediction from Xavier Giro

}, author = {V{\'\i}ctor Campos}, editor = {Amaia Salvador and Jou, Brendan and Xavier Gir{\'o}-i-Nieto} } @article {aValero15, title = {Object recognition in hyperspectral images using Binary Partition Tree representation}, journal = {Pattern Recognition Letters}, volume = {56}, year = {2015}, month = {04/2015}, pages = {45-51}, abstract = {

In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure, which succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Hence, the BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region-based perspective. For each region represented in the BPT, spatial and spectral descriptors are computed and the likelihood that they correspond to an instantiation of the object of interest is evaluated. Experimental results demonstrate the good performances of this BPT-based approach.

}, issn = {0167-8655}, author = {Valero, S. and Salembier, P. and Chanussot, J.} } @conference {cCabezas, title = {Quality Control in Crowdsourced Object Segmentation}, booktitle = {IEEE International Conference on Image Processing (ICIP), 2015}, year = {2015}, month = {09/2015}, abstract = {

This paper explores processing techniques to deal with noisy data in crowdsourced object segmentation tasks. We use the data collected with "Click{\textquoteright}n{\textquoteright}Cut", an online interactive segmentation tool, and we perform several experiments towards improving the segmentation results. First, we introduce different superpixel-based techniques to filter users{\textquoteright} traces, and assess their impact on the segmentation result. Second, we present different criteria to detect and discard the traces from potential bad users, resulting in a remarkable increase in performance. Finally, we show a novel superpixel-based segmentation algorithm which does not require any prior filtering and is based on weighting each user{\textquoteright}s contribution according to his/her level of expertise.

Selected among Top 10\% papers in ICIP 2015 based on the reviewer scores and recommendations.

[Preprint on arXiv]

[Related BSc thesis by Ferran Cabezas]

[IEEE ICIP 2015 conference website]

Co-filtering human interaction and object segmentation from Xavier Giro

}, url = {http://arxiv.org/abs/1505.00145}, author = {Cabezas, Ferran and Carlier, Axel and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Charvillat, Vincent} } @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} } @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} } @article {aTochon15, title = {On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images}, journal = {Remote Sensing of Environment}, volume = {159}, year = {2015}, month = {03/2015}, pages = {318-331}, abstract = {

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

}, issn = {0034-4257}, author = {G. Tochon and J.B. F{\'e}eret and Valero, S. and R.E. Martin and D.E. Knapp and Salembier, P. and Chanussot, J. and G. Asner} } @conference {cMartinez14, title = {3D Shape Reconstruction from a Humanoid Generated Video Sequence}, booktitle = {IEEE International Conference on Humanoid Robots}, year = {2014}, address = {Madrid}, abstract = {

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

}, author = {Mart{\'\i}nez, P. A. and David Varas and Castel{\'a}n, M and Camacho, M and Marqu{\'e}s, F. and Arechavaleta, G.} } @conference {cCarlier, title = {Click{\textquoteright}n{\textquoteright}Cut: Crowdsourced Interactive Segmentation with Object Candidates}, booktitle = {3rd International ACM Workshop on Crowdsourcing for Multimedia (CrowdMM)}, year = {2014}, month = {11/2014}, address = {Orlando, Florida (USA)}, abstract = {

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

[Related master thesis by Amaia Salvador]

[Related Phd thesis by Axel Carlier]

[CrowdMM website]

}, keywords = {Crowdsourcing, figure-ground segmentation, human computing, object candidates}, doi = {10.1145/2660114.2660125}, url = {http://dx.doi.org/10.1145/2660114.2660125}, author = {Carlier, Axel and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Marques, Oge and Charvillat, Vincent} } @conference {cMorros14, title = {Materials transversals per a l{\textquoteright}aprenentatge actiu de les mat{\`e}ries de processat d{\textquoteright}imatge i v{\'\i}deo}, booktitle = {Congr{\'e}s Internacional de Doc{\`e}ncia Universit{\`a}ria i Innovaci{\'o} (CIDUI)}, year = {2014}, month = {07/2014}, address = {Tarragona, Spain}, abstract = {

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

}, keywords = {image/video processing, Matlab demonstrators, question Banks, specific skills, teaching material}, author = {Morros, J.R. and Ver{\'o}nica Vilaplana and Ruiz-Hidalgo, J. and Casas, J. and Gasull, A. and Marqu{\'e}s, F. and M. Pard{\`a}s and Salembier, P.} } @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.} } @phdthesis {dAlcoverro14, title = {Stochastic optimization and interactive machine learning for human motion analysis}, year = {2014}, month = {12/2014}, school = {UPC}, address = {Barcelona}, abstract = {

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

}, url = {http://hdl.handle.net/10803/285337}, author = {Alcoverro, M.}, editor = {M. Pard{\`a}s and Casas, J.} } @conference {cSalvador13 , title = {Crowdsourced Object Segmentation with a Game}, booktitle = {ACM Workshop on Crowdsourcing for Multimedia (CrowdMM)}, year = {2013}, month = {10/2013}, address = {Barcelona}, abstract = {

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

Amaia Salvador, "Crowdsourced Object Segmentation with a Game" from Xavi Gir{\'o}
}, isbn = {978-1-4503-2396-3}, doi = {http://dx.doi.org/10.1145/2506364.2506367}, url = {http://dx.doi.org/10.1145/2506364.2506367}, author = {Amaia Salvador and Carlier, Axel and Xavier Gir{\'o}-i-Nieto and Marques, Oge and Charvillat, Vincent} } @mastersthesis {xSalvador13, title = {Crowdsourced Object Segmentation with a Game}, year = {2013}, abstract = {

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

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

}, author = {Amaia Salvador}, editor = {Xavier Gir{\'o}-i-Nieto and Carlier, Axel and Charvillat, Vincent and Marques, Oge} } @article {aSuau13, title = {Detecting End-Effectors on 2.5D data using Geometric Deformable Models: Application to Human Pose Estimation}, journal = {Computer Vision and Image Understanding (CVIU)}, volume = {117}, year = {2013}, chapter = {281{\textendash}288}, abstract = {

End-effectors are usually related to the location of limbs, and their reliable detection enables robust body tracking as well as accurate pose estimation. Recent innovation in depth cameras has re-stated the pose estimation problem. We focus on the information provided by these sensors, for which we borrow the name 2.5D data from the Graphics community. In this paper we propose a human pose estimation algorithm based on topological propagation. Geometric Deformable Models are used to carry out such propagation, implemented according to the Narrow Band Level Set approach. A variant of the latter method is proposed, including a density restriction which helps preserving the topological properties of the object under analysis. Principal end-effectors are extracted from a directed graph weighted with geodesic distances, also providing a skeletal-like structure describing human pose. An evaluation against reference methods is performed with promising results. The proposed solution allows a frame-wise end-effector detection, with no temporal tracking involved, which may be generalized to the tracking of other objects beyond human body.

}, doi = {10.1016/j.cviu.2012.11.006}, url = {http://www.sciencedirect.com/science/article/pii/S1077314212001907}, author = {Suau, X. and Ruiz-Hidalgo, J. and Casas, J.} } @article {aAlcoverro13, title = {Gesture Control Interface for immersive panoramic displays}, journal = {Multimedia Tools and Applications}, year = {2013}, month = {07/2013}, pages = {1-27}, abstract = {

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

}, issn = {1380-7501}, doi = {10.1007/s11042-013-1605-7}, author = {Alcoverro, M. and Suau, X. and Morros, J.R. and L{\'o}pez-M{\'e}ndez, A. and A. Gil-Moreno and Ruiz-Hidalgo, J. and Casas, J.} } @phdthesis {dSuau13, title = {Human body analysis using depth data}, year = {2013}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

Human body analysis is one of the broadest areas within the computer vision field. Researchers have put a strong effort in the human body analysis area, specially over the last decade, due to the technological improvements in both video cameras and processing power. Human body analysis covers topics such as person detection and segmentation, human motion tracking or action and behavior recognition. Even if human beings perform all these tasks naturally, they build-up a challenging problem from a computer vision point of view. Adverse situations such as viewing perspective, clutter and occlusions, lighting conditions or variability of behavior amongst persons may turn human body analysis into an arduous task.

In the computer vision field, the evolution of research works is usually tightly related to the technological progress of camera sensors and computer processing power. Traditional human body analysis methods are based on color cameras. Thus, the information is extracted from the raw color data, strongly limiting the proposals. An interesting quality leap was achieved by introducing the \emph{multiview} concept. That is to say, having multiple color cameras recording a single scene at the same time. With multiview approaches, 3D information is available by means of stereo matching algorithms. The fact of having 3D information is a key aspect in human motion analysis, since the human body moves in a three-dimensional space. Thus, problems such as occlusion and clutter may be overcome with 3D information.

The appearance of commercial depth cameras has supposed a second leap in the human body analysis field. While traditional multiview approaches required a cumbersome and expensive setup, as well as a fine camera calibration; novel depth cameras directly provide 3D information with a single camera sensor. Furthermore, depth cameras may be rapidly installed in a wide range of situations, enlarging the range of applications with respect to multiview approaches. Moreover, since depth cameras are based on infra-red light, they do not suffer from illumination variations.

In this thesis, we focus on the study of depth data applied to the human body analysis problem. We propose novel ways of describing depth data through specific descriptors, so that they emphasize helpful characteristics of the scene for further body analysis. These descriptors exploit the special 3D structure of depth data to outperform generalist 3D descriptors or color based ones. We also study the problem of person detection, proposing a highly robust and fast method to detect heads. Such method is extended to a hand tracker, which is used throughout the thesis as a helpful tool to enable further research. In the remainder of this dissertation, we focus on the hand analysis problem as a subarea of human body analysis. Given the recent appearance of depth cameras, there is a lack of public datasets. We contribute with a dataset for hand gesture recognition and fingertip localization using depth data. This dataset acts as a starting point of two proposals for hand gesture recognition and fingertip localization based on classification techniques. In these methods, we also exploit the above mentioned descriptor proposals to finely adapt to the nature of depth data.

}, url = {http://hdl.handle.net/10803/134801}, author = {Suau, X.}, editor = {Casas, J. and Ruiz-Hidalgo, J.} } @article {aValero13, title = {Hyperspectral image representation and processing with Binary Partition Trees}, journal = {IEEE Transactions on Image Processing}, volume = {22}, year = {2013}, month = {April 2013}, pages = {1430 - 1443}, abstract = {

The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image-processing tools. This paper proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation relying on the binary partition tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the BPT succeeds in presenting: 1) the decomposition of the image in terms of coherent regions, and 2) the inclusion relations of the regions in the scene. Based on region-merging techniques, the BPT construction is investigated by studying the hyperspectral region models and the associated similarity metrics. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The applicationdependent processing of BPT is generally implemented through a specific pruning of the tree. In this paper, a pruning strategy is proposed and discussed in a classification context. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representation.

}, doi = {10.1109/TIP.2012.2231687}, author = {Valero, S. and Salembier, P. and Chanussot, J.} } @article {aSalvador13, title = {Multi-View Video Representation Based on Fast Monte Carlo Surface Reconstruction}, journal = {IEEE Transactions on Image Processing}, volume = {22}, year = {2013}, month = {09/2013}, pages = {3342 - 3352}, abstract = {

This article provides an alternative solution for the costly representation of multi-view video data, which can be used for both rendering and scene analysis. First, a new, efficient Monte Carlo discrete surface reconstruction method for foreground objects with static background is presented, which outperforms volumetric techniques and is suitable for GPU environments. Some extensions are also presented, which allow speeding up the reconstruction by exploiting multi-resolution and temporal correlation. Then, a fast meshing algorithm is applied, which allows interpolating a continuous surface from the discrete reconstructed points. As shown by the experimental results, the original video frames can be approximated with high accuracy by projecting the reconstructed foreground objects onto the original viewpoints. Furthermore, the reconstructed scene can be easily projected onto any desired virtual viewpoint, simplifying thus the design of Free-Viewpoint Video applications. In our experimental results, we show that our techniques for reconstruction and meshing compare favorably to the state-of-the-art, and we also introduce a rule-of-thumb for effective application of the method with a good quality vs. representation cost trade-off.

Note: ranked 53 among the 100 most downloaded IEEE Xplore articles in July 2013 (Gormish, M., "Top Downloads in IEEE Xplore [Reader{\textquoteright}s Choice]," IEEE Signal Processing Magazine, vol.31, no.1, pp.8,9, Jan. 2014. doi: 10.1109/MSP.2013.2282791)

}, doi = {10.1109/TIP.2013.2264818}, author = {Salvador, J. and Casas, J.} } @conference {cValero13, title = {Object recognition in urban hyperspectral images using binary partition tree representation}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS{\textquoteright}2013}, year = {2013}, publisher = {IEEE}, organization = {IEEE}, address = {Melbourne, Australia}, author = {Valero, S. and Salembier, P. and Chanussot, J.} } @article {aAlonso-Gonzalez13, title = {Processing Multidimensional SAR and Hyperspectral Images With Binary Partition Tree}, journal = {Proceedings of the IEEE}, volume = {101}, year = {2013}, month = {March, 2013}, pages = {723 - 747}, chapter = {723 - 747}, abstract = {

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

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

}, doi = {10.1109/JPROC.2012.2205209}, author = {Alonso-Gonz{\'a}lez, A. and Valero, S. and Chanussot, J. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @article {aTochon13, title = {Segmentation hyperspectrales de forets tropicales par arbres de partition binaires}, journal = {Revue fran{\c c}aise de photogramm{\'e}trie et de t{\'e}l{\'e}d{\'e}tection}, volume = {202}, year = {2013}, month = {May 2013}, pages = {55-65}, author = {G. Tochon and J.B. Feret and Valero, S. and R.E. Martin and R. Tupayachi and Chanussot, J. and Salembier, P. and G. Asner} } @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 {aCuadras12, title = {Distance-based measures of association with applications in relating hyperspectral images}, journal = {Communications in Statistics - Theory and Method}, volume = {41}, year = {2012}, pages = {2342{\textendash}2355}, abstract = {

We propose a distance-based method to relate two data sets. We define and study some measures of multivariate association based on distances between observations. The proposed approach can be used to deal with general data sets (e.g., observations on continuous, categorical or mixed variables). An application, using Hellinger distance, provides the relationships between two regions of hyperspectral images.

}, issn = {0361-0918}, doi = {10.1080/03610926.2012.654880}, author = {Cuadras, C. and Valero, S. and Cuadras, D. and Salembier, P. and Chanussot, J.} } @inbook {bCalderero12 , title = {Image Analysis and Understanding Based on Information Theoretical Region Merging Approaches for Segmentation and Cooperative Fusion}, booktitle = {Handbook of Research on Computational Intelligence for Engineering, Science, and Business}, year = {2012}, pages = {75-121}, publisher = {IGI Global}, organization = {IGI Global}, chapter = {4}, abstract = {

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

}, isbn = {978-1-466-62518-1}, doi = {10.4018/978-1-4666-2518-1.ch004}, author = {Calderero, F. and Marqu{\'e}s, F.} } @inbook {bSuau12, title = {INTAIRACT: Joint Hand Gesture and Fingertip Classification for Touchless Interaction}, booktitle = {Computer Vision {\textendash} ECCV 2012}, volume = {7585}, year = {2012}, pages = {602-606}, publisher = {Springer}, organization = {Springer}, chapter = {3}, address = {Heidelberg}, abstract = {

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

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

}, isbn = {978-3-642-33885-4}, issn = {978-3-642-33884-7}, doi = {10.1007/978-3-642-33885-4_62}, author = {Suau, X. and Alcoverro, M. and L{\'o}pez-M{\'e}ndez, A. and Ruiz-Hidalgo, J. and Casas, J.} } @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.} } @article {aCalderero12, title = {Multispectral Cooperative Partition Sequence Fusion for Joint Classification and Hierarchical Segmentation}, journal = {Geoscience and Remote Sensing Letters, IEEE}, volume = {9}, year = {2012}, pages = {1012-1016}, abstract = {

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

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

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

}, isbn = {978-3-642-31566-4}, doi = {10.1007/978-3-642-31567-1_8}, author = {Navarro, Sergio and L{\'o}pez-M{\'e}ndez, A. and Alcoverro, M. and Casas, J.}, editor = {Perales, Francisco and Fisher, Robert and Moeslund, Thomas} } @conference {cNavarro12 , title = {Multi-view Body Tracking with a Detector-Driven Hierarchical Particle Filter}, booktitle = {7th International Conference AMDO 2012}, year = {2012}, month = {07/2012}, publisher = {Springer}, organization = {Springer}, address = {Port d{\textquoteright}Andratx, Mallorca}, abstract = {

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

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

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

}, keywords = {3DTV, Depth map, multiview video coding, virtual view}, issn = {1047-3203}, doi = {10.1016/j.jvcir.2011.08.001}, url = {http://www.sciencedirect.com/science/article/pii/S1047320311001040}, author = {Ruiz-Hidalgo, J. and Morros, J.R. and Aflaki, P. and Calderero, F. and Marqu{\'e}s, F.} } @conference {cSuau12, title = {Oriented radial distribution on depth data: Application to the detection of end-effectors}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, year = {2012}, month = {03/2012}, address = {Kyoto, Japan}, abstract = {

End-effectors are considered to be the main topological extremities of a given 3D body. Even if the nature of such body is not restricted, this paper focuses on the human body case. Detection of human extremities is a key issue in the human motion capture domain, being needed to initialize and update the tracker. Therefore, the effectiveness of human motion capture systems usually depends on the reliability of the obtained end-effectors. The increasing accuracy, low cost and easy installation of depth cameras has opened the door to new strategies to overcome the body pose estimation problem. With the objective of detecting the head, hands and feet of a human body, we propose a new local feature computed from depth data, which gives an idea of its curvature and prominence. Such feature is weighted depending on recent detections, providing also a temporal dimension. Based on this feature, some end-effector candidate blobs are obtained and classified into head, hands and feet according to three probabilistic descriptors.

}, doi = {10.1109/ICASSP.2012.6288002}, author = {Suau, X. and Ruiz-Hidalgo, J. and Casas, J.} } @phdthesis {dGiro-i-Nieto12, title = {Part-Based Object Retrieval With Binary Partition Trees}, volume = {Phd}, year = {2012}, month = {05/2012}, pages = {215}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, address = {Barcelona, Catalonia}, abstract = {

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

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

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

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

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

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

Xavier Gir{\'o}-i-Nieto, "Part-based Object Retrieval with Binary Partition Trees" from Xavi Gir{\'o}
}, url = {http://hdl.handle.net/10803/108909}, author = {Xavier Gir{\'o}-i-Nieto}, editor = {Marqu{\'e}s, F. and Chang, Shih-Fu} } @article {aSuau12, title = {Real-time head and hand tracking based on 2.5D data}, journal = {IEEE Transactions on Multimedia }, volume = {14}, year = {2012}, month = {06/2012}, pages = {575-585 }, abstract = {

A novel real-time algorithm for head and hand tracking is proposed in this paper. This approach is based on data from a range camera, which is exploited to resolve ambiguities and overlaps. The position of the head is estimated with a depth-based template matching, its robustness being reinforced with an adaptive search zone. Hands are detected in a bounding box attached to the head estimate, so that the user may move freely in the scene. A simple method to decide whether the hands are open or closed is also included in the proposal. Experimental results show high robustness against partial occlusions and fast movements. Accurate hand trajectories may be extracted from the estimated hand positions, and may be used for interactive applications as well as for gesture classification purposes.

}, issn = {1520-9210}, doi = {http://dx.doi.org/10.1109/TMM.2012.2189853}, author = {Suau, X. and Ruiz-Hidalgo, J. and Casas, J.} } @inbook {bCarcel12, title = {Rich Internet Application for Semi-automatic Annotation of Semantic Shots on Keyframes}, booktitle = {Computational Intelligence for Multimedia Understanding}, volume = {7242}, number = {Lecture Notes in Computer Science}, year = {2012}, pages = {172-182}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, address = {Pisa, Italy}, abstract = {

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

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Rich Internet Application for Semi-Automatic Annotation of Semantic Shots on Keyframes from Xavi Gir{\'o}
}, keywords = {annotation, classification, MPEG-7 visual descriptors, RIA, semantic shot}, isbn = {978-3-642-32435-2}, doi = {10.1007/978-3-642-32436-9_15}, url = {http://www.springerlink.com/content/x34632125j381045/}, author = {Carcel, Elisabet and Martos, Manel and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @article {aButko11, title = {Acoustic event detection based on feature-level fusion of audio and video modalities}, journal = {Eurasip journal on advances in signal processing}, volume = {2011}, year = {2011}, pages = {1{\textendash}11}, abstract = {

Acoustic event detection (AED) aims at determining the identity of sounds and their temporal position in audio signals. When applied to spontaneously generated acoustic events, AED based only on audio information shows a large amount of errors, which are mostly due to temporal overlaps. Actually, temporal overlaps accounted for more than 70\% of errors in the real-world interactive seminar recordings used in CLEAR 2007 evaluations. In this paper, we improve the recognition rate of acoustic events using information from both audio and video modalities. First, the acoustic data are processed to obtain both a set of spectrotemporal features and the 3D localization coordinates of the sound source. Second, a number of features are extracted from video recordings by means of object detection, motion analysis, and multicamera person tracking to represent the visual counterpart of several acoustic events. A feature-level fusion strategy is used, and a parallel structure of binary HMM-based detectors is employed in our work. The experimental results show that information from both the microphone array and video cameras is useful to improve the detection rate of isolated as well as spontaneously generated acoustic events.

}, issn = {1687-6172}, doi = {10.1155/2011/485738}, url = {http://www.hindawi.com/journals/asp/2011/485738/}, author = {Butko, T. and Cristian Canton-Ferrer and Segura, C. and Xavier Gir{\'o}-i-Nieto and Nadeu, C. and Hernando, J. and Casas, J.} } @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 {cValero11a, title = {Arbre de partition binaire: un nouvel outil pour la repr{\'e}sentation hi{\'e}rarchique et l{\textquoteright}analyse des images hyperspectrales}, booktitle = {XXIII{\`e} Colloque sur le Traitement du Signal et des Images, GRETSI 2011}, year = {2011}, pages = {120{\textendash}125}, address = {Bordeaux, France}, url = {http://hdl.handle.net/2117/14621}, author = {Valero, S. and Salembier, P. and Chanussot, J.} } @conference {cSalvador11, title = {A compact 3D representation for multi-view video}, booktitle = {2011 International Conference on 3D Imaging}, year = {2011}, pages = {1{\textendash}8}, abstract = {

This paper presents a methodology for obtaining a 3D reconstruction of a dynamic scene in multi-camera settings. Our target is to derive a compact representation of the 3D scene which is effective and accurate, whatever the number of cameras and even for very-wide baseline settings. Easing realtime 3D scene capture has outstanding applications in 2D and 3D content production, free viewpoint video of natural scenes and interactive video applications.\ 

The method proposed here has several original contributions on how to accelerate the process: it exploits spatial and temporal consistency for speeding up reconstruction, dividing the problem in two parts. First, 3D surfaces are efficiently sampled to obtain a silhouette-consistent set of colored surface points and normals, using a novel algorithm presented in this paper. Then, a fast, greedy meshing algorithm retrieves topologically correct continuous surfaces from the dense sets of oriented points, providing a suitable representation for multi-view video.\ 

Compared to other techniques in the literature, the presented approach is capable of retrieving 3D surfaces of foreground objects in real-time by exploiting the computing capabilities of GPUs. This is feasible due to the parallelized design of the surface sampling algorithm. The reconstructed surfaces can effectively be used for interactive representations.\ 

The presented methodology also offers good scalability to large multi-view video settings.

}, isbn = {978-1-61284-349-0}, doi = {10.1109/IC3D.2011.6584371}, url = {http://www.3dstereomedia.eu}, author = {Salvador, J. and Casas, J.} } @conference {cAlcoverro11a, title = {Connected Operators on 3D data for human body analysis}, booktitle = {2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}, year = {2011}, pages = {9{\textendash}14}, abstract = {

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

}, isbn = {978-1-4577-0529-8}, doi = {10.1109/CVPRW.2011.5981772}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5981772}, author = {Alcoverro, M. and L{\'o}pez-M{\'e}ndez, A. and M. Pard{\`a}s and Casas, J.} } @conference {cBenediktsson11, title = {Hierarchical analysis of remote sensing data: morphological attribute profiles and binary partition trees}, booktitle = {International Symposium on Mathematical Morphology 2011}, year = {2011}, pages = {306{\textendash}319}, address = {Intra, Lake Maggiore, Italy}, isbn = {3642215688}, doi = {10.1007/978-3-642-21569-8_27}, url = {http://hdl.handle.net/2117/14624}, author = {Benediktsson, J. and Bruzzone, L. and Chanussot, J. and Dalla Mura, M. and Salembier, P. and Valero, S.} } @article {aCanton-Ferrer11, title = {Human motion capture using scalable body models}, journal = {Computer vision and image understanding}, volume = {115}, number = {10}, year = {2011}, pages = {1363{\textendash}1374}, abstract = {

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

}, issn = {1077-3142}, doi = {10.1016/j.cviu.2011.06.001}, url = {http://hdl.handle.net/2117/13393}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cValero11b, title = {Hyperspectral image segmentation using binary partition trees}, booktitle = {IEEE International Conference on Image Processing, ICIP 2011}, year = {2011}, pages = {1273{\textendash}1276}, address = {Brussels, Belgium}, isbn = {0-7803-5470-2}, doi = {10.1109/ICIP.2011.6115666}, url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6094293}, author = {Valero, S. and Salembier, P. and Chanussot, J.} } @conference {cBragos11a, title = {Implementation and first results of the Introduction to Engineering course in the ETSETB-UPC new degrees}, booktitle = {II Conferencia Internacional en Fomento e Innovaci{\'o}n con Nuevas Tecnolog{\'\i}as en la Docencia de la Ingenier{\'\i}a}, year = {2011}, pages = {1{\textendash}4}, isbn = {978-1-4577-0559-5}, doi = {10.1109/FINTDI.2011.5945971}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5945971}, author = {Bragos, R. and Pegueroles, J. and Alarc{\'o}n, E. and Camps, A. and Sarda, J. and Consolacion, C. and Mussons, J. and Pons, O. and Albert Oliveras and Garc{\'\i}a, M. and Onrubia, R. and Elisa Sayrol} } @conference {cValero11, title = {Improved binary partition tree construction for hyperspectral images: application to object detection}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2011}, year = {2011}, pages = {2515{\textendash}2518}, address = {Vancouver, Canada}, doi = {10.1109/IGARSS.2011.6049723}, url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6034618}, author = {Valero, S. and Salembier, P. and Chanussot, J. and Cuadres, C.} } @conference {cGallego11, title = {Joint multi-view foreground segmentation and 3D reconstruction with tolerance loop}, booktitle = {IEEE International Conference on Image Processing}, year = {2011}, pages = {997{\textendash}1000}, abstract = {

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

}, isbn = {978-1-4577-1304-0}, doi = {10.1109/ICIP.2011.6116731}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6116731}, author = {Gallego, J. and Salvador, J. and Casas, J. and M. Pard{\`a}s} } @phdthesis {dObrador11, title = {Media Aesthetics Based Multimedia Storytelling}, year = {2011}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

Since the earliest of times, humans have been interested in recording their life experiences, for future reference and for storytelling purposes. This task of recording experiences {\textendash}i.e., both image and video capture{\textendash} has never before in history been as easy as it is today. This is creating a digital information overload that is becoming a great concern for the people that are trying to preserve their life experiences. As high-resolution digital still and video cameras become increasingly pervasive, unprecedented amounts of multimedia, are being downloaded to personal hard drives, and also uploaded to online social networks on a daily basis. The work presented in this dissertation is a contribution in the area of multimedia organization, as well as automatic selection of media for storytelling purposes, which eases the human task of summarizing a collection of images or videos in order to be shared with other people. As opposed to some prior art in this area, we have taken an approach in which neither user generated tags nor comments {\textendash}that describe the photographs, either in their local or on-line repositories{\textendash} are taken into account, and also no user interaction with the algorithms is expected. We take an image analysis approach where both the context images {\textendash}e.g. images from online social networks to which the image stories are going to be uploaded{\textendash}, and the collection images {\textendash}i.e., the collection of images or videos that needs to be summarized into a story{\textendash}, are analyzed using image processing algorithms. This allows us to extract relevant metadata that can be used in the summarization process. Multimedia-storytellers usually follow three main steps when preparing their stories: first they choose the main story characters, the main events to describe, and finally from these media sub-groups, they choose the media based on their relevance to the story as well as based on their aesthetic value. Therefore, one of the main contributions of our work has been the design of computational models {\textendash}both regression based, as well as classification based{\textendash} that correlate well with human perception of the aesthetic value of images and videos. These computational aesthetics models have been integrated into automatic selection algorithms for multimedia storytelling, which are another important contribution of our work. A human centric approach has been used in all experiments where it was feasible, and also in order to assess the final summarization results, i.e., humans are always the final judges of our algorithms, either by inspecting the aesthetic quality of the media, or by inspecting the final story generated by our algorithms. We are aware that a perfect automatically generated story summary is very hard to obtain, given the many subjective factors that play a role in such a creative process; rather, the presented approach should be seen as a first step in the storytelling creative process which removes some of the ground work that would be tedious and time consuming for the user. Overall, the main contributions of this work can be capitalized in three: (1) new media aesthetics models for both images and videos that correlate with human perception, (2) new scalable multimedia collection structures that ease the process of media summarization, and finally, (3) new media selection algorithms that are optimized for multimedia storytelling purposes

}, url = {http://hdl.handle.net/10803/33293}, author = {Obrador, P.}, editor = {Casas, J.} } @article {aCanton-Ferrer11a, title = {Multi-camera multi-object voxel-based Monte Carlo 3D tracking strategies}, journal = {Eurasip journal on advances in signal processing}, volume = {2011}, number = {114}, year = {2011}, pages = {1{\textendash}15}, abstract = {

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

}, issn = {1687-6172}, doi = {10.1186/1687-6180-2011-114}, url = {http://asp.eurasipjournals.com/content/2011/1/114}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s and Monte, E.} } @conference {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 {cSuau11, title = {Real-time head and hand tracking based on 2.5D data}, booktitle = {ICME - 2011 IEEE International Conference on Multimedia and Expo}, year = {2011}, pages = {1{\textendash}6}, abstract = {

A novel real-time algorithm for head and hand tracking is proposed in this paper. This approach is based on 2.5D data from a range camera, which is exploited to resolve ambiguities and overlaps. Experimental results show high robustness against partial occlusions and fast movements. The estimated positions are fairly stable, allowing the extraction of accurate trajectories which may be used for gesture classification purposes.

}, isbn = {975001880X}, doi = {10.1109/ICME.2011.6011869}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6011869\&tag=1}, author = {Suau, X. and Casas, J. and Ruiz-Hidalgo, J.} } @conference {cLopez-Mendez11a, title = {Real-time upper body tracking with online initialization using a range sensor}, booktitle = {2011 IEEE International Conference on Computer VIsion Workshops (ICCV Workshops)}, year = {2011}, pages = {391{\textendash}398}, abstract = {

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

}, isbn = {978-1-4673-0063-6/11}, doi = {10.1109/ICCVW.2011.6130268}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6130268}, author = {L{\'o}pez-M{\'e}ndez, A. and Alcoverro, M. and M. Pard{\`a}s and Casas, J.} } @mastersthesis {x11, title = {Rich Internet Application for the Semi-Automatic Annotation of Semantic Shots on Keyframes}, year = {2011}, abstract = {

This thesis describes the graphical user interface developed for semi-automatic keyframebased semantic shot annotation and the semantic shot classifiers built. The graphical user interface aims to optimize the current indexation process by substituting manual annotation for automatic annotation and validation. The system is based on supervised learning binary classifiers and web services. The graphical user interface provides the necessary tools to fix and validate the automatic detections and to learn from the user feedback to retrain the system and improve it. Results of the classifiers evaluation, performed using cross-validation methods, show a good performance in terms of precision and recall. The graphical user interface has been described as complete and easy to use by a professional documentalist at a broadcast company.

Interfície web per l{\textquoteright}annotació semi-automàtica de plans semàntics from Xavi Gir{\'o}


}, url = {http://hdl.handle.net/2099.1/13539}, author = {Carcel, Elisabet}, editor = {Xavier Gir{\'o}-i-Nieto and Vives, X.} } @phdthesis {dSalvador11, title = {Surface Reconstruction for Multi-View Video}, year = {2011}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

This thesis introduces a methodology for obtaining an alternative representation of video sequences captured by calibrated multi-camera systems in controlled environments with known scene background. This representation consists in a 3D description of the surfaces of foreground objects, which allows for the recovering of part of the 3D information of the original scene lost in the projection process in each camera.

The choice of the type of representation and the design of the reconstruction techniques are driven by three requirements that appear in smart rooms or recording studios. In these scenarios, video sequences captured by a multi-camera rig are used both for analysis applications and interactive visualization methods. The requirements are: the reconstruction method must be fast in order to be usable in interactive applications, the surface representation must provide a compression of the multi-view data redundancies and this representation must also provide all the relevant information to be used for analysis applications as well as for free-viewpoint video.

Once foreground and background are segregated for each view, the reconstruction process is divided in two stages. The first one obtains a sampling of the foreground surfaces (including orientation and texture), whereas the second provides closed, continuous surfaces from the samples, through interpolation.

The sampling process is interpreted as a search for 3D positions that result in feature matchings between different views. This search process can be driven by different mechanisms: an image-based approach, another one based on the deformation of a surface from frame to frame or a statistical sampling approach where samples are searched around the positions of other detected samples, which is the fastest and easiest to parallelize of the three approaches.

A meshing algorithm is also presented, which allows for the interpolation of surfaces between samples. Starting by an initial triangle, which connects three points coherently oriented, an iterative expansion of the surface over the complete set of samples takes place. The proposed method presents a very accurate reconstruction and results in a correct topology. Furthermore, it is fast enough to be used interactively.

The presented methodology for surface reconstruction permits obtaining a fast, compressed and complete representation of foreground elements in multi-view video, as reflected by the experimental results.

}, url = {http://hdl.handle.net/10803/108907}, author = {Salvador, J.}, editor = {Casas, J.} } @conference {cValero10a, title = {Comparison of merging orders and pruning strategies for binary partition tree in hyperspectral data}, booktitle = {IEEE International Conference on Image Processing, ICIP 2010}, year = {2010}, pages = {2565{\textendash}2568}, address = {Hong Kong, China}, doi = {10.1109/ICIP.2010.5652595}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5652595}, author = {Valero, S. and Salembier, P. and Chanussot, 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 {cSalvador10b, title = {From silhouettes to 3D points to mesh: towards free viewpoint video}, booktitle = {ACM Workshop on 3D Video Processing (3DVP)}, year = {2010}, pages = {19{\textendash}24}, isbn = {1522-4880}, doi = {10.1145/1877791.1877797}, url = {http://delivery.acm.org/10.1145/1880000/1877797/p19-salvador.pdf?ip=147.83.95.37\&CFID=24773176\&CFTOKEN=26981598\&__acm__=1306838718_5a07df5f539bac704e3bc98a25b9a68c}, author = {Salvador, J. and Suau, X. and Casas, J.} } @article {aGiro-i-Nieto10, title = {GAT, a Graphical Annotation Tool for semantic regions}, journal = {Multimedia tools and applications}, volume = {46}, number = {2/3 (2010)}, year = {2010}, pages = {155{\textendash}174}, abstract = {

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

}, issn = {1380-7501}, doi = {10.1007/s11042-009-0389-2}, url = {http://www.springerlink.com/content/j78782k762617352/}, author = {Xavier Gir{\'o}-i-Nieto and Camps, N. and Marqu{\'e}s, F.} } @article {xCortes10, title = {GOS: b{\'u}squeda visual de im{\'a}genes}, number = {25}, year = {2010}, pages = {36{\textendash}44}, keywords = {i3media}, issn = {1698-7047}, url = {http://upcommons.upc.edu/e-prints/urlFiles?idDrac=2251008}, author = {Cort{\'e}s, S.}, editor = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @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.} } @phdthesis {dCalderero10, title = {Information Theoretical Region Merging Approaches and Fusion of Hierarchical Image Segmentation Results}, year = {2010}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {This PhD. thesis addresses the unsupervised hierarchical segmentation of images and the automatic extraction of the image partitions providing the most semantically relevant explanations of the image at different levels of analysis. From a semantic and practical perspective, image segmentation is a first and key step for image analysis and pattern recognition since region-based image representations provide a first level of abstraction and a reduction of the number of primitives, leading to a more robust estimation of parameters and descriptors. The proposal of this dissertation is based on an important class of hierarchical bottomup segmentation approaches, known as region merging techniques. These approaches naturally provide a bottom-up hierarchy, more suitable when no a priori information about the image is available, and an excellent compromise between efficiency of computation and representation. The first part of the dissertation is devoted to the proposal, under a statistical framework, of a family of unsupervised region merging techniques. These techniques are characterized by general and non-parametric region models, with neither color nor texture homogeneity assumptions, and a set of innovative merging criteria, based on information theory statistical measures. The scale consistency of the partitions is assured through (i) a size regularization term into the merging criteria and a classical merging order, or (ii) using a novel scale-based merging order to avoid the region size homogeneity imposed by the use of a size regularization term. Moreover, a partition significance index is defined to automatically determine the subset of most representative partitions from the created hierarchy. Most significant automatically extracted partitions show the ability to represent the semantic content of the image. Results are promising, outperforming in most indicators both object-oriented and texture state-of-the-art segmentation techniques. The second part of the thesis is focused on the fusion of hierarchical segmentation results, obtained by different segmentation techniques or from different information channels of the same image, with the purpose of increasing the robustness and accuracy of the final solution. In this case, a generic and scalable segmentation scheme based on a cooperative principle, named cooperative region merging, is designed to combine in an unsupervised manner a set of hierarchical region-based representations. The intuition behind this cooperative approach is to iteratively establish a basic or conservative consensus between the independent techniques that can be used as the starting point from which further consensus may be built, similar to a negotiation process for decision making. In addition to the new fused hierarchy of partitions, the proposed scheme automatically provides a subset of partitions considered most relevant from the fusion viewpoint. The combination of hierarchical segmentation results applying different segmentation techniques over the same color image leads to a global improvement of the accuracy and the stability of the segmentation results. Moreover, the use of the cooperative approach for the fusion of segmentation results from heterogenous information channels is presented. Application examples demonstrate the high flexibility and potentiality of the cooperative region merging scheme into a wide range of applications and fusion problems (for instance, in multiview processing and remote sensing). The proposed fusion strategies are able to naturally incorporate a priori available knowledge on the types of information to combine, or on the specificities of the particular fusion problem, and to improve the efficiency and reduce the computational load of the fusion process without compromising the accuracy of the segmentation results.}, author = {Calderero, F.}, editor = {Marqu{\'e}s, F.} } @conference {cSalvador10, title = {Joint estimation of shape and motion from silhouettes}, booktitle = {2010 IEEE International Conference on Image Processing}, year = {2010}, pages = {4069{\textendash}4072}, isbn = {3-540-66082-8}, doi = {10.1109/ICIP.2010.5654022}, url = {http://www.cic.unb.br/~mylene/PI_2010_2/ICIP10/pdfs/0004069.pdf}, author = {Salvador, J. and Casas, J.} } @article {aCanton-Ferrer10, title = {Marker-based human motion capture in multi-view sequences}, journal = {Eurasip journal on advances in signal processing}, volume = {2010}, number = {Article ID 105476}, year = {2010}, pages = {1{\textendash}11}, abstract = {

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

}, keywords = {CHIL, PROVEC}, issn = {1687-6172}, doi = {10.1155/2010/105476}, url = {http://www.hindawi.com/journals/asp/2010/105476.html}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @article {cSayrol10, title = {Mixed integration of CDIO skills into telecommunication engineering curricula}, journal = {Elektronika ir elektrotechnika}, volume = {102}, number = {102}, year = {2010}, pages = {127{\textendash}130}, issn = {1392-1215}, url = {http://www.ktu.lt/lt/mokslas/zurnalai/elektros_z/z102/32__ISSN_1392-1215_Mixed\%20Integration\%20of\%20CDIO\%20skills\%20into\%20Telecommunication\%20Engineering\%20Curricula.pdf}, author = {Elisa Sayrol and Bragos, R. and Alarc{\'o}n, E. and Cabrera, M. and Calveras, A. and Comellas, J. and O{\textquoteright}Callaghan, J. and Pegueroles, J. and Pla, E. and Prat, L. and Saez, G. and Sarda, J. and Tallon, C.} } @conference {cValero10, title = {New hyperspectral data representation using binary partition tree}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010}, year = {2010}, pages = {80{\textendash}83}, address = {Honolulu, USA}, isbn = {978-1-4244-4761-9}, doi = {10.1109/IGARSS.2010.5649780}, url = {http://hdl.handle.net/2117/11005}, author = {Valero, S. and Salembier, P. and Chanussot, J.} } @conference {cSalvador10a, title = {Photo-consistent surfaces from a sparse set of viewpoints}, booktitle = {2010 IEEE International Conference on Image Processing}, year = {2010}, pages = {4045{\textendash}4048}, isbn = {978-1-4244-5655-6}, doi = {10.1109/ICIP.2010.5649006}, url = {http://www.cic.unb.br/~mylene/PI_2010_2/ICIP10/pdfs/0004045.pdf}, author = {Salvador, J. and Casas, J.} } @conference {cBragos10, title = {Proceso de inversi{\'o}n de competencias gen{\'e}ricas en los nuevos planes de estudios de grado de la ETSETB de acuerdo con el modelo CDIO}, booktitle = {IX Congreso de Tecnolog{\'\i}as Aplicadas a la Ense{\~n}anza de la Electr{\'o}nica}, year = {2010}, pages = {1{\textendash}9}, isbn = {978-84-96737-68-6}, url = {http://upcommons.upc.edu/e-prints/urlFiles?idDrac=4451103}, author = {Bragos, R. and Alarc{\'o}n, E. and Cabrera, M. and Calveras, A. and Comellas, J. and O{\textquoteright}Callaghan, J. and Pegueroles, J. and Prat, L. and Saez, G. and Sarda, J. and Elisa Sayrol} } @conference {cCanton-Ferrer10a, title = {Real-time 3D multi-person tracking using Monte Carlo surface sampling}, booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}, year = {2010}, pages = {40{\textendash}46}, isbn = {978-1-4244-7030-3/10}, doi = {10.1109/CVPRW.2010.5543734}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5543734}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cCalderero10, title = {Region merging parameter dependency as information diversity to create sparse hierarchies of partitions}, booktitle = {2010 IEEE International Conference on Image Processing}, year = {2010}, pages = {2237{\textendash}2240}, isbn = {1522-4880}, doi = {10.1109/ICIP.2010.5651720}, url = {http://ieeexplore.ieee.org/search/srchabstract.jsp?tp=\&arnumber=5651720\&queryText\%3DRegion+merging+parameter+dependency+as+information+diversity+to+create+sparse+hierarchies+of+partitions\%26openedRefinements\%3D*\%26searchField\%3DSearch+All}, author = {Calderero, F. and Marqu{\'e}s, F.} } @article {aCalderero10, title = {Region merging techniques using information theory statistical measures}, journal = {IEEE transactions on image processing}, volume = {19}, number = {6}, year = {2010}, pages = {1567{\textendash}1586}, abstract = {

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

}, issn = {1057-7149}, doi = {10.1109/TIP.2010.2043008}, url = {http://www.dfmf.uned.es/~daniel/www-imagen-dhp/biblio/region-merging-information.pdf}, author = {Calderero, F. and Marqu{\'e}s, F.} } @conference {cAlcoverro10, title = {Skeleton and shape adjustment and tracking in multicamera environments}, booktitle = {6th International Conference AMDO 2010}, year = {2010}, pages = {88{\textendash}97}, isbn = {978-3-642-14060-0}, doi = {10.1007/978-3-642-14061-7_9}, author = {Alcoverro, M. and Casas, J. and M. Pard{\`a}s} } @inbook {bAlcoverro10, title = {Skeleton and shape adjustment and tracking in multicamera environments}, booktitle = {Lecture notes in computer science}, volume = {6169/2010}, year = {2010}, pages = {88{\textendash}97}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-642-14060-0}, issn = {0302-9743}, doi = {10.1007/978-3-642-14061-7_9}, url = {http://www.springerlink.com/content/9662h2hwq81041g2/fulltext.pdf}, author = {Alcoverro, M. and Casas, J. and M. Pard{\`a}s} } @conference {cCuadras10, title = {Some measures of multivariate association relating two spectral data sets}, booktitle = {19th International Conference on Computational Statistics, COMSTAT 2010}, year = {2010}, address = {Paris, France}, author = {Cuadras, C. and Valero, S. and Salembier, P. and Chanussot, J.} } @conference {cCanton-Ferrer10, title = {Spatio-temporal alignment and hyperspherical radon transform for 3D gait recognition in multi-view environments}, booktitle = {2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops}, year = {2010}, pages = {116{\textendash}121}, isbn = {978-1-4244-7030-3/10}, doi = {10.1109/CVPRW.2010.5544615}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=5544615}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cSuau10, title = {Surface reconstruction by restricted and oriented propagation}, booktitle = {2010 IEEE International Conference on Image Processing}, year = {2010}, pages = {813{\textendash}816}, isbn = {952-15-1364-0}, doi = {10.1109/ICIP.2010.5652707}, author = {Suau, X. and Casas, J. and Ruiz-Hidalgo, J.} } @conference {cGiro10a, title = {System architecture of a web service for Content-Based Image Retrieval}, booktitle = {ACM International Conference On Image And Video Retrieval 2010}, year = {2010}, pages = {358{\textendash}365}, abstract = {

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

}, isbn = {978-1-4503-0117-6}, doi = {10.1145/1816041.1816093}, url = {http://doi.acm.org/10.1145/1816041.1816093}, author = {Xavier Gir{\'o}-i-Nieto and Ventura, C. and Jordi Pont-Tuset and Cort{\'e}s, S. and Marqu{\'e}s, F.} } @mastersthesis {xSancho10, title = {Tweet@TV: Televisi{\'o} social en 140 car{\`a}cters}, year = {2010}, abstract = {

Aquest Projecte de Final de Carrera se centra en aquesta vessant dels serveis interactius de la televisi{\'o}, la televisi{\'o} social. Durant la seva realitzaci{\'o} s{\textquoteright}ha desenvolupat una aplicaci{\'o} per accedir a una xarxa social d{\textquoteright}una forma integrada i sincronitzada amb el consum de televisi{\'o}. Seguint la l{\'\i}nia de recerca del PFC d{\textquoteright}en Manel Martos, Adaptaci{\'o} i distribuci{\'o} de continguts web per IPTV, aquest projecte s{\textquoteright}ha realitzat en l{\textquoteright}empresa Activa Multim{\`e}dia Digital de la Corporaci{\'o} Catalana de Mitjans Audiovisuals entre els mesos de febrer i maig de 2010 en el marc del projecte CREA-IPTV.

Guardonat amb el segon premi de la convocat{\`o}ria Premios Liberalizaci{\'o}n de las Telecomunicaciones 2010 del Colegio de Ingenieros T{\'e}cnicos de Telecomunicaci{\'o}n (COITT), Espanya.

Cristina Ruiz Sancho, "Tweet@TV, la Televisi{\'o} Social en 140 car{\`a}cters" from Xavi Gir{\'o}
}, keywords = {interactive, microblogging, television, twitter}, url = {http://hdl.handle.net/2099.1/13523}, author = {Ruiz-Sancho, Cristina}, editor = {Xavier Gir{\'o}-i-Nieto and Cucurella, Eduard} } @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 {cCanton-Ferrer09, title = {Audiovisual Event Detection Towards Scene Understanding}, booktitle = {2009 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition Workshops}, year = {2009}, pages = {840{\textendash}847}, abstract = {

Acoustic events produced in meeting environments may contain useful information for perceptually aware interfaces and multimodal behavior analysis. In this paper, a system to detect and recognize these events from a multimodal perspective is presented combining information from multiple cameras and microphones. First, spectral and temporal features are extracted from a single audio channel and spatial localization is achieved by exploiting cross-correlation among microphone arrays. Second, several video cues obtained from multi-person tracking, motion analysis, face recognition, and object detection provide the visual counterpart of the acoustic events to be detected. A multimodal data fusion at score level is carried out using two approaches: weighted mean average and fuzzy integral. Finally, a multimodal database containing a rich variety of acoustic events has been recorded including manual annotations of the data. A set of metrics allow assessing the performance of the presented algorithms. This dataset is made publicly available for research purposes.

}, doi = {10.1109/CVPRW.2009.5204264}, author = {Cristian Canton-Ferrer and Butko, T. and Segura, C. and Xavier Gir{\'o}-i-Nieto and Nadeu, C. and Hernando, J. and Casas, J.} } @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.} } @article {Caselles200916, title = {Flux-gradient and source-term balancing for certain high resolution shock-capturing schemes}, journal = {Computers \& Fluids}, volume = {38}, year = {2009}, month = {0/2009}, pages = {16 - 36}, abstract = {

We present an extension of Marquina{\textquoteright}s flux formula, as introduced in Fedkiw et al. [Fedkiw RP, Merriman B, Donat R, Osher S. The penultimate scheme for systems of conservation laws: finite difference \{ENO\} with Marquina{\textquoteright}s flux splitting. In: Hafez M, editor. Progress in numerical solutions of partial differential equations, Arcachon, France; July 1998], for the shallow water system. We show that the use of two different Jacobians at cell interfaces prevents the scheme from satisfying the exact C-property [Berm{\'u}dez A, V{\'a}zquez ME. Upwind methods for hyperbolic conservation laws with source terms. Comput Fluids 1994;23(8):1049{\textendash}71] while the approximate C-property is satisfied for higher order versions of the scheme. The use of a single Jacobian in Marquina{\textquoteright}s flux splitting formula leads to a numerical scheme satisfying the exact C-property, hence we propose a combined technique that uses Marquina{\textquoteright}s two sided decomposition when the two adjacent states are not close and a single decomposition otherwise. Finally, we propose a special treatment at wet/dry fronts and situations of dry bed generation.

}, issn = {0045-7930}, doi = {10.1016/j.compfluid.2007.07.023}, author = {Caselles, Vicent and Donat, Rosa and Haro, G.} } @conference {cCalderero09a, title = {Hierarchical fusion of color and depth information at partition level by cooperative region merging}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing 2009}, year = {2009}, pages = {973{\textendash}976}, doi = {10.1109/ICASSP.2009.4959748}, url = {http://hdl.handle.net/2117/9491}, author = {Calderero, F. and Marqu{\'e}s, F.} } @conference {cCalderero09, title = {Hierarchical segmentation of vegetation areas in high spatial resolution images by fusion of multispectral information}, booktitle = {2009 IEEE International Geoscience and Remote Sensing Symposium}, year = {2009}, pages = {232{\textendash}235}, isbn = {978-1-4244-3394-0}, doi = {10.1109/IGARSS.2009.5417329}, url = {http://hdl.handle.net/2117/9494}, author = {Calderero, F. and Marqu{\'e}s, F. and Marcello, J. and F. Eugenio} } @phdthesis {dCanton-Ferrer09, title = {Human Motion Capture with Scalable Body Models}, year = {2009}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

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

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

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

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

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

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

}, author = {Cristian Canton-Ferrer}, editor = {Casas, J. and M. Pard{\`a}s} } @inbook {bCanton-Ferrer09, title = {Image and video processing tools for HCI}, booktitle = {Multimodal signal processing: theory and applications for human-computer interaction}, year = {2009}, pages = {93{\textendash}118}, isbn = {9780123748256}, author = {Cristian Canton-Ferrer and M. Pard{\`a}s and Ver{\'o}nica Vilaplana} } @conference {cButko09, title = {Improving Detection of Acoustic Events Using Audiovisual Data and Feature Level Fusion}, booktitle = {10th Annual Conference of the International Speech Communication Association}, year = {2009}, pages = {1147{\textendash}1150}, isbn = {978-1-61567-692-7}, url = {http://gps-tsc.upc.es/imatge/_Xgiro/research/publications/2009/interspeech.pdf}, author = {Butko, T. and Cristian Canton-Ferrer and Segura, C. and Xavier Gir{\'o}-i-Nieto and Nadeu, C. and Hernando, J. and Casas, J.} } @conference {cSanchez-Riera09, title = {Indoor PTZ camera calibration with concurrent PT axes}, booktitle = {Fourth International Conference on Computer Vision Theory and Applications}, year = {2009}, pages = {45{\textendash}50}, abstract = {

The introduction of active (pan-tilt-zoom or PTZ) cameras in Smart Rooms in addition to fixed static cameras allows to improve resolution in volumetric reconstruction, adding the capability to track smaller objects with higher precision in actual 3D world coordinates. To accomplish this goal, precise camera calibration data should be available for any pan, tilt, and zoom settings of each PTZ camera. The PTZ calibration method proposed in this paper introduces a novel solution to the problem of computing extrinsic and intrinsic parameters for active cameras. We first determine the rotation center of the camera expressed under an arbitrary world coordinate origin. Then, we obtain an equation relating any rotation of the camera with the movement of the principal point to define extrinsic parameters for any value of pan and tilt. Once this position is determined, we compute how intrinsic parameters change as a function of zoom. We validate our method by evaluating the re-projection error and its stability for points inside and outside the calibration set.

}, isbn = {0-8186-7310-9}, doi = {10.5220/0001754900100015}, url = {http://visapp.visigrapp.org/Abstracts/2009/VISAPP_2009_Abstracts.htm}, author = {Sanchez-Riera, J. and Salvador, J. and Casas, J.} } @article {aNeumann09, title = {Integration of audiovisual sensors and technologies in a smart room}, journal = {Personal and ubiquitous computing}, volume = {13}, number = {1}, year = {2009}, pages = {15{\textendash}23}, abstract = {

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

}, keywords = {CHIL, PROVEC}, issn = {1617-4909}, doi = {10.1007/s00779-007-0172-1}, url = {http://hdl.handle.net/2117/9468}, author = {Neumann, J. and Casas, J. and Macho, D. and Ruiz-Hidalgo, J.} } @mastersthesis {dCortes09, title = {Interfaz gr{\'a}fica de usuario para la b{\'u}squeda de im{\'a}genes basada en im{\'a}genes}, year = {2009}, month = {07/2009}, type = {BSc}, abstract = {

El Proyecto de Final de Carrera (PFC) responde a esa necesidad de creaci{\'o}n de herramientas de acceso a contenido multimedia, nuevas herramientas que faciliten la recuperaci{\'o}n de toda esa informaci{\'o}n audiovisual almacenada. El Graphic Object Searcher (GOS) es una interfaz gr{\'a}fica para realizar b{\'u}squedas de im{\'a}genes alojadas en grandes bases de datos a partir de una imagen ejemplo y de unos criterios de b{\'u}squeda establecidos por el usuario. La realizaci{\'o}n de PFC permite trabajar en las dos {\'a}reas tecnol{\'o}gicas con m{\'a}s auge actualmente: el sector multimedia (gesti{\'o}n de contenido audiovisual) y las tecnolog{\'\i}as de la informaci{\'o}n (TIC) (inform{\'a}tica al servicio de la comunicaci{\'o}n). Estas dos {\'a}reas tienden a aunar esfuerzos en una sociedad abocada al uso y consumo de contenido audiovisual a trav{\'e}s de m{\'u}ltiples plataformas y dispositivos en cualquier sector econ{\'o}mico y social (ocio, formaci{\'o}n, servicios, etc.). Cualquier profesional del sector audiovisual ha de adquirir conocimiento y experiencia en ambas {\'a}reas para cimentar su carrera.

Interfaz gr{\'a}fica de usuario para la b{\'u}squeda de im{\'a}genes basada en im{\'a}genes from Xavi Gir{\'o}
}, url = {http://hdl.handle.net/2099.1/8588}, author = {Cort{\'e}s, S.}, editor = {Xavier Gir{\'o}-i-Nieto} } @conference {cCabrera09, title = {LAVICAD: LAboratori VIrtual de Comunicacions Anal{\`o}giques i Digitals}, booktitle = {Jornada d{\textquoteright}Innovaci{\'o} Docent - RIMA (JID-RIMA)}, year = {2009}, month = {02/2009}, publisher = {UPCommons}, organization = {UPCommons}, address = {Barcelona, Catalonia}, abstract = {

Mitjan{\c c}ant el present ajut s{\textquoteright}ha ampliat l{\textquoteright}aplicaci{\'o} en xarxa LAVICAD (LAboratori VIrtual de COmunicacions Anal{\`o}giques i Digitals) que s{\textquoteright}ofereix de forma integrada dins de la plataforma d{\textquoteright}e-learning COM@WEB. LAVICAD {\'e}s una eina programada en Java i Matlab i est{\`a} formada per un conjunt de simuladors de la capa f{\'\i}sica de sistemes de comunicacions. Tots els simuladors es presenten en xarxa i es poden utilitzar pels estudiants des de qualsevol ordinador sense necessitat d{\textquoteright}instal{\textperiodcentered}laci{\'o} de cap tipus de software especial. Durant el curs 2007 2008 s{\textquoteright}han desenvolupat entre d{\textquoteright}altres dos l{\'\i}nies de treball. D{\textquoteright}una banda s{\textquoteright}ha programat l{\textquoteright}applet que emula la capa f{\'\i}sica de la televisi{\'o} digital terrestre, com a referent per a l{\textquoteright}ensenyament de sistemes de comunicacions avan{\c c}ats. D{\textquoteright}altra banda s{\textquoteright}ha treballat en la programaci{\'o} de noves funcionalitats de l{\textquoteright}eina LAVICAD, que permeten ajudar als professors en el seguiment i avaluaci{\'o} del treball continuat dels estudiants. En particular s{\textquoteright}ha programat la generaci{\'o} d{\textquoteright}una base de dades que cont{\'e} la informaci{\'o} dels usuaris que s{\textquoteright}han connectat i els resultats obtinguts a l{\textquoteright}executar un determinat simulador. Les dues l{\'\i}nies desenvolupades han de permetre durant l{\textquoteright}actual curs, consolidar l{\textquoteright}{\'u}s dels diferents simuladors per a la doc{\`e}ncia de les assignatures implicades al projecte.

}, url = {http://hdl.handle.net/2099/7235}, author = {Cabrera, M. and Xavier Gir{\'o}-i-Nieto and Rey, F. and Gasull, A. and Casas, J. and Villares, J. and Fernandez, J. and Sala {\'A}lvarez, josep and Espinosa Fricke, Pedro and Fern{\'a}ndez, Carlos Marcos and Cort{\'e}s, S. and Farr{\'e}, Miquel {\`A}ngel} } @inbook {bDimakis09, title = {The Memory Jog Service}, booktitle = {Computers in the Human Interaction Loop}, year = {2009}, pages = {207{\textendash}234}, publisher = {Springer}, organization = {Springer}, address = {London}, abstract = {

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

}, isbn = {978-1-84882-053-1}, doi = {10.1007/978-1-84882-054-8}, url = {http://www.springerlink.com/content/tg220751v5m1h356/}, author = {Dimakis, N. and Soldatos, J. and Polymenakos, L. and Sturm, J. and Neumann, J. and Casas, J.} } @conference {cSuau09, title = {Multi-resolution illumination compensation for foreground extraction}, booktitle = {16th International Conference on Image Processing}, year = {2009}, pages = {3225{\textendash}3228}, abstract = {

Illumination changes may lead to false foreground (FG) segmentation and tracking results. Most of the existing FG extraction algorithms obtain a background (BG) estimation from temporal statistical parameters. Such algorithms consider a quasi-static BG which does not change but slowly. Therefore, fast illumination changes are not taken into account by the BG estimator and they are considered as FG. The aim of the proposed algorithm is to reduce illumination effects in video sequences in order to improve foreground segmentation performances.

}, doi = {http://dx.doi.org/10.1109/ICIP.2009.5414358}, url = {http://www.icip2009.org}, author = {Suau, X. and Casas, J. and Ruiz-Hidalgo, J.} } @conference {cCalderero09b, title = {Performance evaluation of probability density estimators for unsupervised information theoretical region merging}, booktitle = {16th International Conference on Image Processing}, year = {2009}, pages = {4397{\textendash}4400}, isbn = {978-1-4244-5655-6}, doi = {10.1109/ICIP.2009.5413621}, url = {http://hdl.handle.net/2117/9510}, author = {Calderero, F. and Marqu{\'e}s, F. and Ortega, A.} } @inbook {bBernardin09, title = {Person Tracking}, booktitle = {Computers in the human interaction loop}, year = {2009}, pages = {11{\textendash}22}, publisher = {Springer}, organization = {Springer}, address = {London}, abstract = {

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

}, isbn = {978-1-84882-053-1}, doi = {10.1007/978-1-84882-054-8}, url = {http://www.springerlink.com/content/tj2x827107563616/}, author = {Bernardin, K. and Stiefelhagen, R. and Pnevmatikakis, A. and Lanz, O. and Brutti, A. and Casas, J. and Potamianos, G.} } @conference {cCanton-Ferrer09a, title = {Towards a low cost multi-camera marker based human motion capture system}, booktitle = {16th International Conference on Image Processing}, year = {2009}, pages = {2581{\textendash}2584}, isbn = {978-1-4244-5655-6}, doi = {10.1109/ICIP.2009.5413915}, url = {http://dx.doi.org/10.1109/ICIP.2009.5413915}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @article {aChang09, title = {Trajectory tree as an object-oriented hierarchical representation for video}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {19}, number = {4}, year = {2009}, pages = {547{\textendash}560}, issn = {1051-8215}, author = {Chang, C. and M. Pard{\`a}s and Marqu{\'e}s, F.} } @conference {cCanton-Ferrer09b, title = {Voxel based annealed particle filtering for markerless 3D articulated motion capture}, booktitle = {3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video, 2009}, year = {2009}, pages = {1{\textendash}4}, doi = {10.1109/3DTV.2009.5069645}, url = {http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5069609\&isYear=2009}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @article {aCorrea08, title = {3D posture estimation using geodesic distance maps}, journal = {Multimedia tools and applications}, volume = {38}, number = {3}, year = {2008}, pages = {365{\textendash}384}, issn = {1380-7501}, author = {Correa, P. and Marqu{\'e}s, F. and Marichal, X. and Macq, B.} } @article {aCanton-Ferrer08, title = {Audiovisual head orientation estimation with particle filtering in multisensor scenarios}, journal = {EURASIP Journal on Advances in Signal Processing}, volume = {2008}, year = {2008}, month = {06/2007}, pages = {1{\textendash}13}, chapter = {276846}, abstract = {

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

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

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

}, isbn = {978-3-540-70516-1}, doi = {10.1007/978-3-540-70517-8_9}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer08, title = {Exploiting Structural Hierarchy in Articulated Objects Towards Robust Motion Capture}, booktitle = {V Conference on Articulated Motion and Deformable Objects (AMDO)}, year = {2008}, pages = {82{\textendash}91}, isbn = {0302-9743}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cButko08, title = {Fusion of Audio and Video Modalities for Detection of Acoustic Events}, booktitle = {Interspeech 2008, 9th Annual Conference of the International Speech Communication Association}, year = {2008}, month = {09/2008}, pages = {123{\textendash}126}, publisher = {ISCA}, organization = {ISCA}, address = {Brisbane, Australia}, isbn = {978-1-61567-378-0}, url = {http://www.isca-speech.org/archive/interspeech_2008/i08_0123.html}, author = {Butko, T. and Temko, A. and Nadeu, C. and Cristian Canton-Ferrer} } @conference {cCalderero08a, title = {General region merging approaches based on information theory statistical measures}, booktitle = {IEEE International Conference on Image Processing}, year = {2008}, pages = {3016{\textendash}3019}, isbn = {1-4244-1764-3}, author = {Calderero, F. and Marqu{\'e}s, F.} } @conference {cCalderero08, title = {General Region Merging Based on First Order Markov Information Theory Statistical Measures}, booktitle = {16th European Signal Processing Conference}, year = {2008}, author = {Calderero, F. and Marqu{\'e}s, F.} } @inbook {bCanton-Ferrer08, title = {Head Orientation Estimation Using Particle Filtering in Multiview Scenarios}, booktitle = {Multimodal Technologies for Perception of Humans}, volume = {4625}, year = {2008}, pages = {317{\textendash}327}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-540-68584-5}, doi = {10.1007/978-3-540-68585-2_30}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cButko08a, title = {Inclusion of video information for detection of acoustic events using the fuzzy integral}, booktitle = {Machine Learning for Multimodal Interaction: 5th International Workshop}, year = {2008}, pages = {74{\textendash}85}, isbn = {978-3540858522}, author = {Butko, T. and Temko, A. and Nadeu, C. and Cristian Canton-Ferrer} } @conference {cCabrera08, title = {Lavicad: laboratorio virtual de comunicaciones anal{\'o}gicas y digitales}, booktitle = {XXIII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {2008}, month = {09/2008}, pages = {1{\textendash}4}, address = {Madrid, Spain}, abstract = {

The presented experience consists on the {\textquotedblleft}design of{\textquotedblright} and {\textquotedblleft}experimentation with{\textquotedblright} a virtual laboratory of analog and digital communications: LAVICAD. It has been result a useful tool to verify the performance of different communication systems and signal processing techniques, topics typically integrated in undergraduated courses of the curriculum of telecommunications engineering. The communication systems have been implemented and designed as Java applets and are free access. They can be run at the e-learning platform: comweb.upc.edu. The different communication systems present different levels of user interactivity and when students execute a system integrated in a comweb course, the obtained results can be supervised by the professor as an evaluation and assessment tool. From a pedagogical point of view, the main advantages of using a virtual laboratory supposes, can leads to facilitate the learning of certain matters, acting as a connection between the model of knowledge based on concepts and theories, and their practical understanding and experimentation.\ 

[URSI 2008 website][Program for ED track]

}, isbn = {978-84-612-6291-5}, author = {Cabrera, M. and Xavier Gir{\'o}-i-Nieto and Rey, F.} } @conference {cCanton-Ferrer08b, title = {Multimodal Real-Time Focus of Attention Estimation in SmartRooms}, booktitle = {CVPR 2008 Workshop on Human Communicative Behavior Analysis}, year = {2008}, pages = {1{\textendash}4}, isbn = {978-1-4244-2340}, author = {Cristian Canton-Ferrer and Segura, C. and M. Pard{\`a}s and Casas, J. and Hernando, J.} } @inbook {bCanton-Ferrer08b, title = {Multi-Person Tracking Strategies Based on Voxel Analysis}, booktitle = {Multimodal Technologies for Perception of Humans}, volume = {4625}, year = {2008}, pages = {91{\textendash}103}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-540-68584-5}, doi = {10.1007/978-3-540-68585-2_7}, url = {http://www.springerlink.com/content/k08w4000844220gr/}, author = {Cristian Canton-Ferrer and Salvador, J. and Casas, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer08a, title = {Particle Filtering and Sparse Sampling for Multi-Person 3D Tracking}, booktitle = {IEEE International Conference on Image Processing}, year = {2008}, pages = {2644{\textendash}2647}, isbn = {1-4244-1764-3}, doi = {10.1109/ICIP.2008.4712337}, author = {Cristian Canton-Ferrer and Sblendido, R. and Casas, J. and M. Pard{\`a}s} } @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 {cSalvador08, title = {Shape from Probability Maps with Image-Adapted Voxelization}, booktitle = {ECCV Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications (M2SFA2)}, year = {2008}, pages = {1{\textendash}12}, isbn = {0-7803-9076-8}, author = {Salvador, J. and Casas, J.} } @conference {cGudmundsson08, title = {TOF Imaging in Smart Room Environments towards Improved People Tracking}, booktitle = {Computer Vision and Pattern Recognition: Workshop on Time of Flight based Computer Vision (CVPR/TOF-CV)}, year = {2008}, pages = {1{\textendash}6}, author = {Gudmundsson, S. and M. Pard{\`a}s and Casas, J. and Aanaes, H. and Larsen, R.} } @article {aCraene08, title = {Unbiased group-wise alignment by iterative central tendency estimations}, journal = {Mathematical modeling of natural phenomena}, volume = {3}, number = {6}, year = {2008}, pages = {2{\textendash}32}, issn = {0973-5348}, url = {http://hdl.handle.net/2117/8140}, author = {de Craene, M. and Macq, B. and Marqu{\'e}s, F. and Salembier, P. and Warfield, S.} } @article {aCorrea07, title = {Bayesian approach for morphology based 2D human motion capture}, journal = {IEEE transactions on multimedia}, volume = {9}, number = {4}, year = {2007}, pages = {754{\textendash}765}, issn = {1520-9210}, author = {Correa, P. and Czyz, J. and Marqu{\'e}s, F. and Umeda, T. and Marichal, X. and Macq, B.} } @article {aMostefa07, title = {The CHIL Audiovisual Corpus for Lecture and Meeting Analysis inside Smart Rooms}, journal = {Language resources and evaluation}, volume = {41}, number = {3}, year = {2007}, month = {01/2008}, pages = {389{\textendash}407}, abstract = {

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

}, issn = {1574-020X}, doi = {10.1007/s10579-007-9054-4}, author = {Mostefa, D. and Moreau, N. and Choukri, K. and Potamianos, G. and Chu, S. and Tyagi, A. and Casas, J. and Turmo, J. and Cristoforetti, L. and Tobia, F. and Pnevmatikakis, A. and Mylonakis, V. and Talantzis, F. and Burger, S. and Stiefelhagen, R. and Bernardin, K. and Rochet, C.} } @inbook {bCasas07, title = {Context Awareness triggered by Multiple Perceptual Analyzers}, booktitle = {Emerging Artificial Intelligence Applications in Computer Engineering}, volume = {160}, year = {2007}, pages = {371{\textendash}383}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam}, abstract = {

A multitude of technologies from computer vision, acoustic signal analysis and natural language processing are used to implement multi-modal perceptual components. The output of this analysis is used to gain context awareness {\textendash} a necessity when designing a computer-based service that interacts reactively and proactively with humans. This article describes the integration process and our experience in implementing one such information service, the {\textquotedblleft}Memory Jog{\textquotedblright}, in a particular scenario where the computer system supports a group of journalists in their daily work.

}, isbn = {978-1-58603-780-2}, url = {http://www.booksonline.iospress.nl/Content/View.aspx?piid=6976}, author = {Casas, J. and Neumann, 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 Dios, V. and Duxans, H. and Esparza, O. and Esquerra, I. and Garcia, D. and Garcias, P. and Gomez, C. and Gorricho, J. and Guinjoan, F. and Hesselbach, X. and Liria, A. and Lopez, J. and Madrenas, J. and Madue{\~n}o, M. and Mestre, F. and Monte, E. and Morros, J.R. and Mu{\~n}oz, J. and Pallar{\'e}s, E. and Pons, J. and Recolons, J. and Rincon, D. and Riu, P. and Pradell, L. and Pascual-Iserte, A. and Prat, L. and Rey, F. and Villares, J.} } @conference {cPetras07, title = {Flexible test-bed for unusual behavior detection}, booktitle = {6th ACM International Conference on Image and Video Retrieval}, year = {2007}, pages = {105{\textendash}108}, isbn = {0 646 34170 7}, author = {Petras, I. and Beleznai, C. and Dedeoglu, Y. and M. Pard{\`a}s and Kov{\'a}cs, L. and Szl{\'a}vik, Z. and Havasi, L. and Szir{\'a}nyi, T. and T{\"o}reyin, B. and G{\"u}d{\"u}kbay, U. and Cetin, E. and Cristian Canton-Ferrer} } @conference {cCanton-Ferrer07a, title = {Head Orientation Estimation using Particle Filtering in Multiview Scenarios}, booktitle = {CLEAR{\textquoteright}07 Second International Evaluation Workshop on Classification of Events, Activities and Relationships}, year = {2007}, pages = {1{\textendash}11}, isbn = {978-3-540-68584-5}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @inbook {bCanton-Ferrer07, title = {Head Pose Detection based on Fusion of Multiple Viewpoint Information}, booktitle = {Multimodal Technologies for Perception of Humans}, volume = {4122}, year = {2007}, pages = {305{\textendash}310}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-540-69567-7}, doi = {10.1007/978-3-540-69568-4_28}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @phdthesis {dChang07, title = {Hierarchical Partition-Based Representations of Motion-Coherent Regions For Video Object Segmentation}, year = {2007}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, author = {Chang, C.}, editor = {M. Pard{\`a}s and Marqu{\'e}s, F.} } @conference {cTurkan07, title = {Human eye localization using edge projections}, booktitle = {International Conference on Computer Vision Theory and Applications, VISAPP 2007}, year = {2007}, pages = {410{\textendash}415}, isbn = {-}, author = {Turkan, M. and M. Pard{\`a}s and Cetin, E.} } @conference {cSegura07, title = {Multimodal Head Orientation towards Attention Tracking in Smart Rooms}, booktitle = {2007 International Conference on Acoustics, Speech and Signal Processing}, year = {2007}, author = {Segura, C. and Abad, A. and Casas, J. and Hernando, J.} } @conference {cLopez-Mendez07, title = {Multi-person 3D Tracking with Particle Filters on Voxels}, booktitle = {2007 International Conference on Acoustics, Speech and Signal Processing}, year = {2007}, author = {L{\'o}pez-M{\'e}ndez, A. and Cristian Canton-Ferrer and Casas, J.} } @conference {cCanton-Ferrer07, title = {Multi-Person Tracking Strategies Based on Voxel Analysis}, booktitle = {CLEAR{\textquoteright}07 Second International Evaluation Workshop on Classification of Events, Activities and Relationships}, year = {2007}, pages = {1{\textendash}12}, isbn = {978-3-540-68584-5}, author = {Cristian Canton-Ferrer and Salvador, J. and Casas, J. and M. Pard{\`a}s} } @conference {cCalderero07, title = {Multiple view region matching using as a Lagrangian optimization problem}, booktitle = {2007 International Conference on Acoustics, Speech and Signal Processing}, year = {2007}, author = {Calderero, F. and Marqu{\'e}s, F. and Ortega, A.} } @conference {cCabrera07, title = {Proyecto de Innovaci{\'o}n Docente COM@WEB (COMunicaciones en la WEB)}, booktitle = {VIII Simposio Nacional de Tecnolog{\'\i}as de la Informaci{\'o}n y las Comunicaciones en la Educaci{\'o}n. SINTICE{\textquoteright}07}, year = {2007}, month = {09/2007}, pages = {113{\textendash}120}, abstract = {

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

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

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

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

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

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

}, isbn = {978-84-9732-597-4}, author = {Cabrera, M. and Closas, P. and Alfredo, L. and Xavier Gir{\'o}-i-Nieto and Rey, F.} } @conference {cGiro-i-Nieto07a, title = {Region-based annotation tool using partition trees}, booktitle = {International Conference on Semantic and Digital Media Technologies}, year = {2007}, month = {12/2007}, pages = {3{\textendash}4}, address = {Genova, Italy}, abstract = {

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

}, url = {http://hdl.handle.net/2117/10890}, author = {Xavier Gir{\'o}-i-Nieto and Camps, N. and Marqu{\'e}s, F.} } @mastersthesis {xGil-Moreno07, title = {Sistema de gesti{\'o} de v{\'\i}deo off-line per una smart-room}, year = {2007}, keywords = {qt, recording, smartroom}, author = {A. Gil-Moreno}, editor = {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.} } @conference {cCanton-Ferrer06, title = {3D Human Action Recognition In Multiple View Scenarios}, booktitle = {2ones Jornades UPC de Investigaci{\'o}n en Autom{\'a}tica, Visi{\'o}n y Rob{\'o}tica}, year = {2006}, pages = {1{\textendash}5}, isbn = {84-7653-885-5}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s and Sargin, M. and Tekalp, M.} } @conference {cFerran06, title = {BPT Enhancement based on Syntactic and Semantic criteria}, booktitle = {1st International Conference on Semantic and Digital Media Technologies}, year = {2006}, pages = {184{\textendash}198}, isbn = {3-540-49335-2}, author = {Ferran, C. and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Casas, J.} } @inbook {bFerran06, title = {BPT Enhancement based on Syntactic and Semantic criteria}, booktitle = {Semantic Multimedia}, volume = {4306}, year = {2006}, pages = {184{\textendash}198}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-540-49335-8}, doi = {10.1007/11930334_15}, url = {http://www.springerlink.com/content/u7201mw06545w057/}, author = {Ferran, C. and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Casas, J.} } @phdthesis {dCorrea06, title = {Dual morphology-based and Bayesian approach for markerless human motion capture in natural interaction environments}, year = {2006}, school = {Universit{\'e} Catholique de Louvain (UCL)}, type = {phd}, url = {http://hdl.handle.net/2078.1/5033}, author = {Correa, P.}, editor = {Marqu{\'e}s, F. and Macq, B.} } @conference {cGiro-i-Nieto06, title = {The edition of the Wikipedia as an academic activity}, booktitle = {4rt. Congr{\'e}s Internacional de Doc{\`e}ncia Unversit{\`a}ria i Innovaci{\'o}}, year = {2006}, month = {07/2006}, pages = {{\textendash}}, address = {Barcelona, Catalonia}, abstract = {

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

[CIDUI 2006 website]

}, keywords = {wikipedia, ~cooperative, ~online}, isbn = {84-8458-240-X}, url = {http://hdl.handle.net/2117/13157}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Casas, J.} } @conference {cCanton-Ferrer06b, title = {Head Pose Detection based on Fusion of Multiple Viewpoint Information}, booktitle = {CLEAR{\textquoteright}06 Evaluation Campaign and Workshop - Classification of Events, Activities and Relationships}, year = {2006}, pages = {305{\textendash}310}, isbn = {978-3-540-69567-7}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer06a, title = {Human Model and Motion Based 3D Action Recognition in Multiple View Scenarios}, booktitle = {14th European Signal Processing Conference}, year = {2006}, pages = {1{\textendash}1}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cSalvador06, title = {Image-adapted voxelization in multicamera settings}, booktitle = {International Workshop on Multimedia Signal Processing}, year = {2006}, pages = {161{\textendash}165}, isbn = {0-7803-3258-X}, author = {Salvador, J. and Casas, J.} } @conference {cCasas06, title = {Image-based Multi-view Scene Analysis using {\textquoteright}Conexels{\textquoteright}}, booktitle = {HCSNet Workshop on the Use of Vision in Human-Computer Interaction (VisHCI 2006)}, year = {2006}, pages = {203{\textendash}212}, abstract = {

Multi-camera environments allow constructing volumetric models of the scene to improve the analysis performance of computer vision algorithms (e.g. disambiguating occlusion). When representing volumetric results of image-based multi-camera analysis, a direct approach is to scan the 3D space with regular voxels. Regular voxelization is good at high spatial resolutions for applications such as volume visualization and rendering of synthetic scenes generated by geometric models, or to represent data resulting from direct 3D data capture (e.g. MRI). However, regular voxelization shows a number of drawbacks for visual scene analysis, where direct measurements on 3D voxels are not usually available. In this case, voxel values are computed rather as a result of the analysis on ?projected? image data.

In this paper, we first provide some statistics to show how voxels project to ?unbalanced? sets of image data in common multi-view analysis settings. Then, we propose a 3D geometry for multi-view scene analysis providing a better balance in terms of the number of pixels used to analyse each elementary volumetric unit. The proposed geometry is non-regular in 3D space, but becomes regular once projected onto camera images, adapting the sampling to the images. The aim is to better exploit multiview image data by balancing its usage across multiple cameras instead of focusing in regular sampling of 3D space, from which we do not have direct measurements. An efficient recursive algorithm using the proposed geometry is outlined. Experimental results reflect better balance and higher accuracy for multi-view analysis than regular voxelization with equivalent restrictions.

}, keywords = {epipolar geometry, Multi-view analysis, volume voxelization}, isbn = {1-920-68238-4}, url = {http://crpit.com/Vol56.html}, author = {Casas, J. and Salvador, J.} } @conference {cNeumann06, title = {Multimodal Integration of Sensor Network}, booktitle = {Proceedings of 3rd IFIP Conference on Artificial Intelligence Applications \& Innovations}, year = {2006}, month = {09/2006}, publisher = {Springer}, organization = {Springer}, address = {Athens, Greece}, isbn = {978-0-387-34223}, author = {Neumann, J. and Casas, J. and Macho, D. and Ruiz-Hidalgo, J.} } @inbook {bNeumann06, title = {Multimodal Integration of Sensor Network}, booktitle = {Artificial Intelligence Applications and Innovations}, volume = {204}, year = {2006}, pages = {312{\textendash}323}, publisher = {Springer}, organization = {Springer}, address = {Boston}, abstract = {

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

}, isbn = {978-0-387-34223-8}, doi = {10.1007/0-387-34224-9_36}, author = {Neumann, J. and Casas, J. and Macho, D. and Ruiz-Hidalgo, J.} } @inbook {bCanton-Ferrer06, title = {Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding}, booktitle = {Machine Learning for Multimodal Interaction}, volume = {3869}, year = {2006}, pages = {250{\textendash}261}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-540-32549-9}, doi = {10.1007/11677482_22}, author = {Cristian Canton-Ferrer and Casas, J. and Tekalp, M. and M. Pard{\`a}s} } @conference {cLandabaso06a, title = {Reconstruction of 3D shapes considering inconsistent 2D silhouettes}, booktitle = {International Conference on Image Processing}, year = {2006}, pages = {1{\textendash}4}, author = {Landabaso, J. and M. Pard{\`a}s and Casas, J.} } @conference {cGiro-i-Nieto06c, title = {Solucions de programari lliure en un projecte d{\textquoteright}adaptaci{\'o} de dades a XML}, booktitle = {V Jornades de Programari Lliure de la UPC}, year = {2006}, author = {Xavier Gir{\'o}-i-Nieto and Camps, N.} } @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 {cSalvador06a, title = {Voxelitzaci{\'o} Adaptada a les Imatges en Entorns Multic{\`a}mera}, booktitle = {2ones Jornades UPC de Investigaci{\'o}n en Autom{\'a}tica, Visi{\'o}n y Rob{\'o}tica}, year = {2006}, pages = {1{\textendash}6}, isbn = {978-3-540-26042-4}, author = {Salvador, J. and Casas, J.} } @conference {cSayrol05, title = {Development of a platform offering video copyright protection and security against illegal distribution}, booktitle = {Security, Steganography, and Watermarking of Multimedia Contents}, year = {2005}, pages = {76{\textendash}83}, isbn = {9963-607-06-3}, author = {Elisa Sayrol and Soriano, M. and Fernandez, M. and Casanelles, J. and Tom{\`a}s, J.} } @conference {cGarcia05, title = {Functionalities for mapping 2D images and 3D world objects in a Multicamera Environment}, booktitle = {6th International Workshop on Image Analysis for Multimedia Interactive Services}, year = {2005}, abstract = {

We present four functionalities intended to improve the ability of image detection and tracking algorithms to understand a scene in a multicamera system. The redundancy of several available projections of any 3D object onto different cameras might ease video analysis tasks. When some prior information about the 3D object or any of its projections is known, geometric constraints can help to restrict search areas in the images under analysis. The functionalities presented also tackle the problem of selecting the best camera at any time, or computing projected areas of 3D objects in images.

}, author = {Garcia, O. and Casas, J.} } @conference {cCanton-Ferrer05a, title = {Fusion of multiple viewpoint information towards 3d face robust orientation detection}, booktitle = {IEEE International Conference on Image Processing}, year = {2005}, isbn = {0-7803-9135-7}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @article {jFigueras05, title = {Las Mancomunidades en Espa{\~n}a}, journal = {Bolet{\'\i} de la Asociaci{\'o}n de Ge{\'o}grafos Espa{\~n}oles}, number = {39}, year = {2005}, pages = {151{\textendash}176}, issn = {0212-9426}, author = {Figueras, P. and Haas, C. and Capdevila, C. and Ver{\'o}nica Vilaplana} } @conference {cChang05, title = {A Motion-based Binary Partition Tree approach to Video Object Segmentation}, booktitle = {IEEE International Conference on Image Processing}, year = {2005}, pages = {430{\textendash}433}, isbn = {0-7803-9135-7}, author = {Chang, C. and M. Pard{\`a}s and Marqu{\'e}s, F.} } @conference {cSoriano05, title = {Multimedia copyright protection platform demonstrator}, booktitle = {Third International Conference on Trust Management (iTrust{\textquoteright}05)}, year = {2005}, pages = {411{\textendash}414}, doi = {10.1007/11429760_32}, url = {http://dx.doi.org/10.1007/11429760_32}, author = {Soriano, M. and Fernandez, M. and Elisa Sayrol and Buliart, J. and Casanelles, J. and Pegueroles, J. and Juan Hern{\'a}ndez Serrano} } @inbook {bSoriano05, title = {Multimedia Copyright Protection Platform Demonstrator}, booktitle = {Lecture notes in computer science}, volume = {3477}, year = {2005}, pages = {76{\textendash}83}, issn = {0302-9743}, url = {http://www.springerlink.com/(n0yw1g55rs45pg55g5zcinrm)/app/home/contribution.asp?referrer=parent\&backto=issue,32,34;journal,197,2212;linkingpublicationresults,1:105633,1}, author = {Soriano, M. and Fernandez, M. and Elisa Sayrol and Tom{\`a}s, J. and Casanelles, J. and Pegueroles, J. and Juan Hern{\'a}ndez Serrano} } @article {aCasas05, title = {Mutual feedback scheme for face detection and tracking aimed at density estimation in demonstrations}, journal = {IEE Proceedings-Vision, Image and Signal Processing}, volume = {152}, number = {3}, year = {2005}, month = {05/2005}, pages = {334{\textendash}346}, abstract = {

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

}, issn = {1350-245X}, doi = {10.1049/ip-vis:20045071}, url = {http://dx.doi.org/10.1049/ip-vis:20045071}, author = {Casas, J. and Puig-Sitjes, A. and Puig-Folch, P.} } @conference {cFerran05, title = {Object representation using colour, shape and structure criteria in a Binary Partition Tree}, booktitle = {IEEE International Conference on Image Processing}, year = {2005}, isbn = {0-7803-9135-7}, author = {Ferran, C. and Casas, J.} } @conference {cCanton-Ferrer05b, title = {Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding}, booktitle = {2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms}, year = {2005}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and Tekalp, M. and M. Pard{\`a}s} } @conference {cCorrea05, title = {Silhouette-based Probabilistic 2D Human Motion Estimation for Real-Time Applications}, booktitle = {IEEE International Conference on Image Processing}, year = {2005}, pages = {836{\textendash}839}, isbn = {0-7803-9135-7}, author = {Correa, P. and Czyz, J. and Umeda, T. and Marqu{\'e}s, F. and Marichal, X. and Macq, B.} } @inbook {bCanton-Ferrer05, title = {Towards a Bayesian Approach to Robust Finding Correspondances in Multiple View Geometry Environments}, booktitle = {Computational Science {\textendash} ICCS 2005}, volume = {3515}, year = {2005}, pages = {281{\textendash}289}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-540-26043-1}, doi = {10.1007/11428848_35 }, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer05, title = {Towards a bayesian approach to robust finding correspondances in multiple view geometry environments}, booktitle = {Workshop on Computer Graphics and Geometric Modelling. Intrernational Conference on Computational Science}, year = {2005}, pages = {281{\textendash}289}, isbn = {3-540-26043-9}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cFerran04, title = {Binary-Partition Tree creation using a quasi-inclusion criterion}, booktitle = {8th International Conference on Information Visualization (IV04)}, year = {2004}, pages = {259{\textendash}264}, isbn = {0-7695-2177-0}, author = {Ferran, C. and Casas, J.} } @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 {cCabrera04, title = {LaViCAD: LABORATORIO VIRTUAL DE COMUNICACIONES ANAL{\'O}GICAS Y DIGITALES}, booktitle = {4rt. Congr{\'e}s Internacional de Doc{\`e}ncia Unversit{\`a}ria i Innovaci{\'o}}, year = {2004}, pages = {1{\textendash}20}, isbn = {84-8458-240-X}, author = {Cabrera, M. and Fernandez, J. and Berzosa, C. and Francisco, V. and Gasull, A.} } @conference {cCasas04, title = {Spatial-temporal Video Analysis for Improved Pedestrian Detection: Application to Density Estimation and Tracking in Demonstrations}, booktitle = {Image Analysis for Multimedia Interactive Services}, year = {2004}, pages = {334{\textendash}346}, isbn = {972-98115-7-1}, author = {Casas, J. and Puig-Sitjes, A. and Puig-Folch, P.} } @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}, author = {Casas, J. and Kompatsiaris, I. and Strintzis, M.} } @conference {cO{\textquoteright}Connor03, title = {Region and object segmentation algorithms in the QIMERA segmentation platform}, booktitle = {Third International Workshop on Content-Based Multimedia Indexing}, year = {2003}, pages = {95{\textendash}103}, isbn = {978-84-612-2373-2}, author = {O{\textquoteright}Connor, N. and Sav, S. and Adamek, T. and Mezaris, V. and Kompatsiaris, I. and Lui, T. and Izquierdo, E. and Ferran, C. and Casas, J.} } @article {aComas03, title = {Unbalanced multiple description video coding based on a rate-distortion optimization}, journal = {Eurasip Journal on Applied Signal Processing}, volume = {2003}, number = {1}, year = {2003}, pages = {81{\textendash}90}, issn = {1110-8657}, author = {Comas, D. and Singh, R. and Ortega, A. and Marqu{\'e}s, F.} } @article {aSchelkens03, title = {Wavelet coding of volumetric medical datasets}, journal = {Medical Imaging, IEEE Transactions on}, volume = {22}, year = {2003}, month = {march}, pages = {441 -458}, abstract = {

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

}, keywords = {Algorithms, Computer-Assisted, Data Compression, discrete cosine transforms, embedded coding, embedded data streams, functionality constraints, image coding, Image Enhancement, Image Interpretation, Imaging, JPEG2000, layered zero coding, lossless compression, medical diagnostic imaging, medical image compression, medical image processing, Numerical Analysis, objective quality evaluation, progressive image transmission, quadtree coding, reviews, Signal Processing, subjective quality evaluation, Three-Dimensional, volumetric coding, wavelet transforms}, issn = {0278-0062}, doi = {10.1109/TMI.2003.809582}, author = {Schelkens, P. and Munteanu, A. and Barbarien, J. and Galca, M. and Xavier Gir{\'o}-i-Nieto and Cornelis, J.} } @conference {cBescos01, title = {An open integrated system for video indexing}, booktitle = {International Workshop on Content-Based Multimedia Indexing}, year = {2001}, pages = {322{\textendash}333}, author = {Bescos, J. and Torres, L. and Menendez, J. and Vil{\`a}, J. and Cisneros, G. and Marqu{\'e}s, F. and Cabrera, J. and Mart{\'\i}nez, J.} } @conference {cSchelkens00, title = {3D compression of medical data based on cube-splitting and embedded block coding}, booktitle = {ProRISC/IEEE Workshop}, year = {2000}, month = {12/2000}, address = {Veldhoven, The Netherlands}, author = {Schelkens, P. and Xavier Gir{\'o}-i-Nieto and Barbarien, J. and Cornelis, J.} } @article {aSalembier00a, title = {Description Schemes for Video Programs, Users and Devices}, journal = {Signal processing: image communication}, volume = {16}, number = {1}, year = {2000}, pages = {211{\textendash}234}, issn = {0923-5965}, author = {Salembier, P. and Richard, Q. and O{\textquoteright}Connor, N. and Correia, P. and Sezan, I and van Beek, P} } @conference {cMaziere00, title = {Segmentation and tracking of video objects for content-based video indexing}, booktitle = {IEEE International Conference on Multimedia and Expo, ICME{\textquoteright}2000}, year = {2000}, pages = {305{\textendash}309}, address = {New York City, NY, USA}, author = {Maziere, M. and Chassaing, F. and Garrido, L. and Salembier, P.} } @conference {cSalembier99, title = {The DICEMAN description schemes for still images and video sequences}, booktitle = {Workshop on Image Analysis for Multimedia Application Services, WIAMIS{\textquoteright}99}, year = {1999}, pages = {25{\textendash}34}, address = {Berlin, Germany}, isbn = {84-7653-885-5}, author = {Salembier, P. and O{\textquoteright}Connor, N. and Correa, P. and Ward, L} } @conference {cSalembier99a, title = {Hierarchical visual description schemes for still images and video sequences}, booktitle = {1999 IEEE International Conference on Image Processing, ICIP 1999}, year = {1999}, address = {Kobe, Japan}, author = {Salembier, P. and O{\textquoteright}Connor, N. and Correia, P. and Pereira, F.} } @conference {cVidal99, title = {Non-Noticeable Information Embedding in Color Images : Marking and Detection}, booktitle = {33rd ANNUAL - 1999 International Carnahan Conference on Security Technology}, year = {1999}, pages = {293{\textendash}297}, isbn = {0-7803-5247-5}, author = {Vidal, J. and Elisa Sayrol and Cabanillas, S. and Santamaria, S.} } @conference {cSayrol99, title = {Optimum Watermark Detection in Color Images}, booktitle = {1999 IEEE International Conference on Image Processing}, year = {1999}, pages = {1{\textendash}5}, author = {Elisa Sayrol and Vidal, J. and Cabanillas, S. and Santamaria, S.} } @conference {cGarrido99, title = {Representing and retrieving regions using binary partition trees}, booktitle = {1999 IEEE International Conference on Image Processing, ICIP 1999}, year = {1999}, address = {Kobe, Japan}, isbn = {0-7803-5470-2}, author = {Garrido, L. and Salembier, P. and Casas, J.} } @conference {cMarcotegui99, title = {A video generation tool allowing friendly user interaction}, booktitle = {1999 IEEE INternational Conference on Image Processing}, year = {1999}, isbn = {0-7803-5470-2}, author = {Marcotegui, B. and Correia, P. and Marqu{\'e}s, F. and Mech, R. and Rosa, R. and Wollborn, M. and Zanoguera, F.} } @conference {cRuiz-Hidalgo98, title = {The segmentation of images via scale-space trees}, booktitle = {British Machine Vision Conference}, year = {1998}, month = {09/1998}, pages = {33{\textendash}43}, address = {Southampton, UK}, abstract = {

A useful representation of an image would be an object tree in which nodes represent objects, or parts of objects, and which includes at least one node that, together with its children, represents each object: a grandmother node. It is shown that scale-trees, obtained from greyscale images, approximate such a tree. It is then shown how they may be modified using other attributes to more closely become object trees. The result is a data structure that provides {\textquotedblleft}handles{\textquotedblright} for every element of the image that can be used for manipulating the image. This segmentation has potential for object recognition.

}, author = {Bangham, J. and Ruiz-Hidalgo, J. and Harvey, R. and Cawley, G.} } @article {aCasas97, title = {A region-based subband coding scheme}, journal = {Signal Processing: Image Communication}, volume = {10}, number = {1-2}, year = {1997}, month = {10/1997}, pages = {173{\textendash}200}, abstract = {

This paper describes a region-based subband coding scheme intended for efficient representation of the visual information contained in image regions of arbitrary shape. QMF filters are separately applied inside each region for the analysis and synthesis stages, using a signal-adaptive symmetric extension technique at region borders. The frequency coefficients corresponding to each region are identified over the various subbands of the decomposition, so that the coding steps {\textemdash} namely, bit-allocation, quantization and entropy coding {\textemdash} can be performed independently for each region. Region-based subband coding exploits the possible homogeneity of the region contents by distributing the available bitrate not only in the frequency domain but also in the spatial domain, i.e. among the considered regions. The number of bits assigned to the subbands is optimized region by region for the whole image, by means of a rate-distortion optimization algorithm. Improved compression efficiency is obtained thanks to the local adaptativity of the bit allocation to the spectral contents of the different regions. This compensates for the overhead data spent in the coding of contour information. As the subband coefficients obtained for each region are coded as separate data units, the content-based functionalities required for the future MPEG4 video coding standard can be readily handled. For instance, content-based scalability is possible by simply imposing user-defined constraints to the bit-assignment in some regions.

}, keywords = {SCHEMA}, issn = {0923-5965}, doi = {10.1016/S0923-5965(97)00024-6}, author = {Casas, J. and Torres, L.} } @article {aSalembier97, title = {Segmentation-based video coding system allowing the manipulation of objects}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {7}, number = {1}, year = {1997}, pages = {60{\textendash}74}, issn = {1051-8215}, doi = {10.1109/76.554418}, author = {Salembier, P. and Marqu{\'e}s, F. and M. Pard{\`a}s and Morros, J.R. and Corset, I. and Jeannin, S. and Bouchard, L. and Meyer, F. and Marcotegui, B.} } @article {aTorres97, title = {Stochastic vector quantization of images}, journal = {Signal Processing}, volume = {62}, number = {3}, year = {1997}, month = {11/1997}, pages = {291{\textendash}301}, abstract = {

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

}, issn = {0165-1684}, doi = {10.1016/S0165-1684(97)00130-8}, author = {Torres, L. and Casas, J. and Arias, E.} } @conference {cCasas96, title = {A feature-based subband coding schene}, booktitle = {THE 1996 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH \& SIGNAL PROCESSING}, year = {1996}, pages = {2357{\textendash}2360}, isbn = {0-7803-3192-3}, author = {Casas, J. and Torres, L.} } @phdthesis {dCasas96, title = {Image compression based on perceptual coding techniques}, volume = {PhD}, year = {1996}, month = {03/1996}, pages = {178}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, address = {download link}, abstract = {

This thesis studies image and video sequence coding methods from the point of view of the way the human visual system perceives and understands visual information. The relevance of such study is due, on the one hand, to the important role that visual signals have in our civilization and, on the other hand, to the problem of representing the large amount of data that image and video processing systems have to deal with. Three different approaches have been investigated for the coding of image textures in an advanced compression scheme relying in aspects of visual perception. The first approach is based on image transitions and the interpolation of smooth areas from such transitions. The second one, considers the extraction, selection and coding of meaningful image details. Finally, the third approach studies the efficient representation of homogeneous fine textures that give a natural appearance to the reconstructed images at high compression levels. In order to apply these techniques for still image and video coding, a three component model of the image, that matches the perceptual properties of the human vision, is put forward. The coding approaches subject of research have leaded to the design of three new image analysis and coding techniques. Using non-linear tools from the framework of Mathematical Morphology, three texture coding techniques are developed. In particular, - A "morphological" image interpolation method aimed at the problem of scattered data interpolation. - An empirical subjective criterion for the ranking and selection of image details according to visual perception. - The application of a conventional image coding technique, subband coding, to the coding of arbitrarily shaped image regions (region-based subband coding). These are new texture coding techniques in the field of object-oriented and Second Generation image and video coding schemes. Furthermore, the model of the image that has been investigated follows the line of the last proposals in the framework of MPEG4, the forthcoming coding standard for low bit-rate visual communications, which considers the possibility of content-based manipulation and coding of visual information.

The PhD thesis report can be downloaded from http://hdl.handle.net/10803/6920

}, url = {http://hdl.handle.net/10803/6920}, author = {Casas, J.}, editor = {Torres, L.} } @conference {cCasas96b, title = {Morphological Interpolation for Image Coding}, booktitle = {ICAOS {\textquoteright}96: 12th International Conference on Analysis and Optimization of Systems Images, Wavelets and PDEs }, year = {1996}, month = {06/1996}, pages = {295{\textendash}304}, address = {Paris}, author = {Casas, J.} } @inbook {bCasas96, title = {Morphological Interpolation for Image Coding}, booktitle = {Images, Wavelets and PDEs}, volume = {219}, number = {Lecture Notes in Control and Information Sciences}, year = {1996}, pages = {295{\textendash}304}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

The aim of this paper is to present a new interpolation technique intended for spatial interpolation from sparse data sets. The proposed implementation, which is based on non-linear morphological operators, overperforms linear interpolation by means of diffusion processes performing iterative space-variant filtering on the initial image. Morphological interpolation is applied to sketch-based image coding. We put forward a perceptually motivated two-component image model that strongly relies on morphological operators. The watershed is used to detect strong edge features in the first component of the model. The smooth areas of the image are recovered from the extracted edge information by morphological interpolation. The residual component, containing fine textures, is separately coded by a subband coding scheme.

}, isbn = {978-3-540-76076-4}, doi = {10.1007/3-540-76076-8_142}, author = {Casas, J.} } @article {aSalembier96, title = {Morphological operators for image and video compression}, journal = {IEEE transactions on image processing}, volume = {5}, number = {6}, year = {1996}, month = {06/1996}, pages = {881{\textendash}898}, abstract = {

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

}, issn = {1057-7149}, doi = {10.1109/83.503906}, author = {Salembier, P. and Brigger, P. and Casas, J. and M. Pard{\`a}s} } @conference {cTorres96, title = {Prediction error image coding using a modified stochastic vector quantization scheme}, booktitle = {IEEE International Conference on Image Processing}, year = {1996}, pages = {451{\textendash}454}, author = {Torres, L. and Casas, J.} } @conference {cMarques96, title = {A segmentation-based coding system allowing manipulation of objects}, booktitle = {IEEE International Conference on Image Processing, ICIP 1996}, year = {1996}, pages = {145{\textendash}175}, address = {Lausanne, Switzerland}, abstract = {

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

}, isbn = {0-7803-3258-X}, doi = {10.1109/ICIP.1996.560741}, author = {Marqu{\'e}s, F. and Salembier, P. and M. Pard{\`a}s and Morros, J.R. and Corset, I. and Jeannin, S. and Marcotegui, B. and Meyer, F.} } @article {pSalembier96a, title = {Segmented Picture Coding Method and System and Corresponding Decoding Method and System}, number = {964009161-}, year = {1996}, type = {Invention patent}, author = {Salembier, P. and Marqu{\'e}s, F. and Corset, I. and Bouchard, L. and Jeannin, S. and M. Pard{\`a}s and Morros, J.R. and Meyer, F. and Marcotegui, B.} } @conference {cCasas96a, title = {Strong edge features for image coding}, booktitle = {International Symposium on Mathematical Morphology and its Applications to Image and Signal Processing III}, year = {1996}, pages = {443{\textendash}450}, isbn = {0-7923-9733-9}, author = {Casas, J. and Torres, L.} } @inbook {bCasas96c, title = {Strong edge features for image coding}, booktitle = {Mathematical Morphology and its Applications to Image and Signal Processing}, volume = {5}, year = {1996}, pages = {443{\textendash}450}, publisher = {Springer}, organization = {Springer}, address = {Boston}, abstract = {

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

}, isbn = {0-793-9733-9}, doi = {10.1007/978-1-4613-0469-2_52}, author = {Casas, J. and Torres, L.}, editor = {Schafer, R. W. and Maragos, P. and Butt, M. A.} } @conference {cTorres95a, title = {An efficient technique of texture representation in segmentation-based image coding schemes}, booktitle = {IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING.}, year = {1995}, pages = {588{\textendash}591}, author = {Torres, L. and Casas, J. and Campins, J.} } @conference {cCasas95, title = {Morphological interpolation for texture coding}, booktitle = {IEEE International Conference on Image Processing, ICIP 1995}, year = {1995}, pages = {903{\textendash}907}, address = {Washington DC, USA}, author = {Casas, J. and Salembier, P. and Torres, L.} } @conference {cTorres95, title = {New approaches to texture coding in segmentation and feature-based image coding schemes}, booktitle = {International Conference on Digital Signal Processing}, year = {1995}, pages = {12{\textendash}17}, isbn = {84-600-7766-7}, author = {Torres, L. and Casas, J.} } @article {aCasas94, title = {Coding of details in very low bit-rate video systems}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {4}, number = {3}, year = {1994}, month = {06/1994}, pages = {317{\textendash}327}, abstract = {

In this paper, the importance of including small image features at the initial levels of a progressive second generation video coding scheme is presented. It is shown that a number of meaningful small features called details should be coded, even at very low data bit-rates, in order to match their perceptual significance to the human visual system. We propose a method for extracting, perceptually selecting and coding of visual details in a video sequence using morphological techniques. Its application in the framework of a multiresolution segmentation-based coding algorithm yields better results than pure segmentation techniques at higher compression ratios, if the selection step fits some main subjective requirements. Details are extracted and coded separately from the region structure and included in the reconstructed images in a later stage. The bet of considering the local background of a given detail for its perceptual selection breaks the concept of {\textquotedblleft}partition{\textquotedblright} in the segmentation scheme. As long as details are not considered as adjacent regions but isolated features spread over the image, {\textquotedblleft}detail coding{\textquotedblright} can be seen as one step towards the so called feature-based video coding techniques

}, keywords = {CHIL}, issn = {1051-8215}, doi = {10.1109/76.305876}, url = {http://hdl.handle.net/2117/97643}, author = {Casas, J. and Torres, L.} } @conference {cCasas94a, title = {Coding of significant features in very low bit-rate video systems}, booktitle = {SPIE{\textquoteright}S Visual Communications{\textquoteright}94}, year = {1994}, pages = {73{\textendash}85}, author = {Casas, J. and Torres, L.} } @conference {cCasas94c, title = {Feature-based video coding using Mathematical Morphology}, booktitle = {VII European Signal Processing Conference (EUSIPCO)}, year = {1994}, publisher = {EURASIP}, organization = {EURASIP}, address = {Edinburgh}, abstract = {

This paper puts forward a new approach to second generation image coding. The concept of "image feature" is introduced in order to deal with those "objects" that cannot be properly described as regions within a segmentation framework. Visual features such as open contours or texture details are extracted from the original images using morphological operators. As malhematical morphology deals with the shapes and structures of the images, the resulting features are very close to what would be obtained from a visual perception point of view. If these features are efficiently coded with suitable techniques, such coding model is able to reach higher compression ratios than purely segmentationbased techniques. Numerical results are given at the end of the paper to prove the goodness of the feature-based coding scheme in a very-low bit-rate video coding application.

}, author = {Casas, J. and Torres, L.} } @conference {cMarques94c, title = {Interleaved segmentation and motion estimation by means of morphological tools}, booktitle = {Workshop on Image Analysis and Synthesis in Image Coding}, year = {1994}, address = {Berlin, Germany}, author = {Marqu{\'e}s, F. and Bouchard, L. and Corset, I. and Jeannin, S. and Morros, J.R. and M. Pard{\`a}s and Salembier, P. and Torres, L.} } @conference {cCasas94b, title = {Morphological filter for lossless image subsampling}, booktitle = {IEEE International Conference on Image Processing{\textquoteright}94}, year = {1994}, pages = {903{\textendash}907}, isbn = {0-8186-6950-0}, author = {Casas, J. and Torres, L.} } @conference {cCasas94d, title = {Morphological scheme for morphometric analysis of epidermal biopsy images}, booktitle = {International Symposium on Mathematical Morphology and its Applications to Image Processing II}, year = {1994}, address = {Fontainebleau}, isbn = {0-7923-3093-5}, author = {Casas, J. and Esteban, P. and Moreno, A. and Carrera, M.} } @inbook {bCasas94, title = {Morphological scheme for morphometric analysis of epidermal biopsy images}, booktitle = {Mathematical Morphology and its Applications to Image Processing}, volume = {2}, number = {Computational Imaging and Vision}, year = {1994}, pages = {325{\textendash}331}, publisher = {Springer}, organization = {Springer}, address = {Dordrecht}, abstract = {

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

}, doi = {10.1007/978-94-011-1040-2_42}, author = {Casas, J. and Esteban, P. and Moreno, A. and Carrera, M.}, editor = {Serra, J. and Soille, P.} } @conference {cGimeno94, title = {A new approach to texture coding using stochastic vector quantization}, booktitle = {IEEE International Conference on Image Processing{\textquoteright}94}, year = {1994}, pages = {119{\textendash}123}, abstract = {

A new method for texture coding which combines 2-D linear prediction and stochastic vector quantization is presented in this paper. To encode a texture, a linear predictor is computed first. Next, a codebook following the prediction error model is generated and the prediction error is encoded with VQ, using an algorithm which takes into account the pixels surrounding the block being encoded. In the decoder, the error image is decoded first and then filtered as a whole, using the prediction filter. Hence, correlation between pixels is not lost from one block to another and a good reproduction quality can be achieved

}, isbn = {0-8186-6950-0}, doi = {10.1109/ICIP.1994.413287}, author = {Gimeno, D. and Torres, L. and Casas, J.} } @conference {cCasas94, title = {Residual image coding using mathematical morphology}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing}, year = {1994}, pages = {597{\textendash}600}, isbn = {0-7803-1775-0}, author = {Casas, J. and Torres, L.} } @conference {cTorres94, title = {Segmentation based coding of textures using stochastic vector quantization}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing}, year = {1994}, pages = {553{\textendash}556}, abstract = {

In second generation image compression techniques the image to be compressed is first segmented. The pixels are divided into mutually exclusive spatial regions based on some criteria. After segmentation, the image consists of regions separated by contours. Then, the information is coded describing the shapes and interiors of the regions. The interiors of the regions are usually encoded using polynomials. The objective of this paper is to encode the interior of the regions by stochastic vector quantization techniques. If the segmentation process has been well defined and the obtained regions are homogeneous, then it is possible to design a specific codebook suited to the statistics of each region. The approach is to design the codebook according to some previously defined model for the regions of the image found in the segmentation process. If the approach is combined with efficient contour coding techniques, good visual results for high compression rates are obtained

}, isbn = {84-600-7766-7}, doi = {10.1109/ICASSP.1994.389380}, author = {Torres, L. and Casas, J. and Diego, D.} } @conference {cGasull93, title = {Character recognition and document analysis by morphological techniq}, booktitle = {MATHEMATICAL MORPHOLOGY}, year = {1993}, pages = {198{\textendash}203}, author = {Gasull, A. and Corbera, L. and Marqu{\'e}s, F.} } @conference {cMontolio93, title = {Character recognition and document analysis by morphological techniques}, booktitle = {WORKSHOP ON MATHEMATICAL MORPHOLOGY AND ITS APPLICATIONS TO SIGNAL PROCESSING}, year = {1993}, pages = {198{\textendash}203}, author = {Montolio, P. and Gasull, A. and Corbera, L. and Marqu{\'e}s, F.} } @conference {cCasas93, title = {Codificacion run-length de imagenes de detalle}, booktitle = {VIII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1993}, pages = {349{\textendash}399}, author = {Casas, J.} } @conference {cCasas93a, title = {Efficient coding of residual images}, booktitle = {SPIE Visual Communications {\textquoteright}93}, year = {1993}, month = {10/1993}, pages = {694{\textendash}705}, publisher = {SPIE}, organization = {SPIE}, address = {Cambridge, MA}, abstract = {

In progressive image coding, the cost in bits of the successive residual images being encoded does not always correspond with the subjective importance that such components may have. The original idea of this paper arose for increasing the efficiency when coding the last Laplacian levels in linear decompositions for pyramidal coding. The same principle has been applied to the non-linear image decomposition presented in which a segmentation-based progressive scheme is used for coding purposes. The {\textquoteleft}post-it{\textquoteright} method for extracting details based on mathematical morphology proposed by Meyer has been modified in order to improve the rendition of the posterior coding, and a suitable technique for coding the extracted details from an extension of run-length coding is then applied. In both linear and non-linear cases the results of this method for {\textquoteleft}detail-coding{\textquoteright} are compared against the conventional progressive coding technique, i.e., pyramidal coding or pure segmentation-based contour/texture coding.

}, doi = {10.1117/12.157987}, author = {Casas, J. and Torres, L. and Jare{\~n}o, M.} } @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 {cCasas92a, title = {Fuzzy classification of Remote Sensing images: a pseudocolor representation of fuzzy partitions}, booktitle = {SPIE Neural and Stochastic Methods in Image and Signal Processing}, year = {1992}, month = {07/1992}, publisher = {SPIE}, organization = {SPIE}, address = {San Diego, CA}, doi = {10.1117/12.130844}, author = {Casas, J. and Hillion, A. and Roux, C. and Torres, L. and Gasull, A.} } @conference {cCasas92, title = {Una interpretaci{\'o}n colorim{\'e}trica en clasificaciones fuzzy de im{\'a}genes de teledetecci{\'o}n}, booktitle = {V Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1992}, pages = {281{\textendash}287}, author = {Casas, J. and Torres, L. and Gasull, A.} } @conference {cCasas91, title = {Dise{\~n}o de filtros de imagen con funciones de transferencia}, booktitle = {VI Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1991}, pages = {965{\textendash}969}, isbn = {84-600-7766-7}, author = {Casas, J. and Torres, L.} } @article {aSanz90, title = {Aspectos Cl{\'\i}nicos-toxicol{\'o}gicos en los trabajadores de una industria productora de cromatos}, journal = {Annual review of pharmacology and toxicology}, volume = {7}, year = {1990}, pages = {1:13{\textendash}1:20}, issn = {0362-1642}, author = {Sanz, P. and Ribas, B. and Cobo, E. and Gadea, E. and Marqu{\'e}s, F. and Sol{\'e}, E. and Corbella, J.} }