@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} } @conference {cBonet23, title = {HyperFast: Instant Classification for Tabular Data}, booktitle = {38th Annual AAAI Conference on Artificial Intelligence (AAAI)}, year = {2024}, month = {12/2024}, abstract = {

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

}, author = {Bonet, David and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cBarrabes, title = {Adversarial Learning for Feature Shift Detection and Correction}, booktitle = {Neural Information Processing Systems (NeurIPS)}, year = {2023}, month = {12/2023}, address = {New Orleans, USA}, abstract = {

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

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

}, author = {Barrab{\'e}s, M{\'\i}riam and Mas-Montserrat, Daniel and Geleta, Margarita and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @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} } @phdthesis {dFernandez23, title = {Knowledge graph population from news streams}, volume = {Doctorate}, year = {2023}, month = {10/2023}, school = {Universitat Polit{\`e}cnica de Catalunya}, type = {Industrial}, address = {Barcelona, Catalonia}, abstract = {

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

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

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

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

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

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

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

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

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

}, author = {Dominguez, Albert and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cLozano23, title = {Optical Phased Array Antenna Apodization for Lidar in Autonomous Vehicles}, booktitle = {XIII Reuni{\'o}n OptoElectr{\'o}nica}, year = {2023}, month = {06/2023}, publisher = {OPTOEL}, organization = {OPTOEL}, address = {Sevilla, Spain}, abstract = {

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

}, url = {https://www.optoel2023.es/94299/section/44205/optoel-2023.html}, author = {Jos{\'e} Lozano and Humberto Jim{\'e}nez and Sergio Torres and Pau Biosca and Bernat Fontanet and Jorge Pinazo and Adolfo Ler{\'\i}n and Federico Dios and Casas, J. and Jos{\'e} Antonio L{\'a}zaro} } @conference {cMorros, title = {Real-time lane classification and accident detection for safer micromobility}, booktitle = {11th Internationa Congress on Transportation Research}, year = {2023}, month = {09/2023}, address = {Heraklion, Crete}, abstract = {

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

}, keywords = {accident detection, computer Vision, deep learning, lane classification, Micromobility, RideSafeUM}, author = {Morros, J.R. and Broquetas, A. and Mateo, A. and Puig, J. and Davins, M.} } @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 {cBonet22, title = {Channel Redundancy and Overlap in Convolutional Neural Networks with Channel-Wise NNK Graphs}, booktitle = {International Conference on Acoustics, Speech and Signal Processing}, year = {2022}, month = {05/2022}, abstract = {

Feature spaces in the deep layers of convolutional neural networks (CNNs) are often very high-dimensional and difficult to interpret. However, convolutional layers consist of multiple channels that are activated by different types of inputs, which suggests that more insights may be gained by studying the channels and how they relate to each other. In this paper, we first analyze theoretically channel-wise non-negative kernel (CW-NNK) regression graphs, which allow us to quantify the overlap between channels and, indirectly, the intrinsic dimension of the data representation manifold. We find that redundancy between channels is significant and varies with the layer depth and the level of regularization during training. Additionally, we observe that there is a correlation between channel overlap in the last convolutional layer and generalization performance. Our experimental results demonstrate that these techniques can lead to a better understanding of deep representations.\ 

}, author = {Bonet, D. and Ortega, A. and Ruiz-Hidalgo, J. and Shekkizhar, S.} } @article {aTemprana-Salvador22, title = {DigiPatICS: Digital Pathology Transformation of the Catalan Health Institute Network of 8 Hospitals - Planification, Implementation and Preliminary Results}, journal = {Diagnostics}, volume = {12}, year = {2022}, month = {03/2022}, chapter = {852}, abstract = {

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

}, keywords = {artificial intelligence, computational pathology, deep learning, digital pathology, implementation, LIS, primary diagnosis, telepathology, workflow}, doi = {10.3390/diagnostics12040852}, url = {https://www.mdpi.com/2075-4418/12/4/852}, author = {Jordi Temprana-Salvador and Pau L{\'o}pez-Garc{\'\i}a and Josep Castellv{\'\i} Vives and Llu{\'\i}s de Haro and Eudald Ballesta and Matias Rojas Abusleme and Miquel Arrufat and Ferran Marques and Casas, J. and Carlos Gallego and Laura Pons and Jos{\'e} Luis Mate and Pedro Luis Fern{\'a}ndez and Eugeni L{\'o}pez-Bonet and Ramon Bosch and Salom{\'e} Mart{\'\i}nez and Santiago Ram{\'o}n y Cajal and Xavier Matias-Guiu} } @conference {cMas-Montserrat, title = {Generative Moment Matching Networks for Genotype Simulation}, booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}22)}, year = {2022}, abstract = {

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

}, author = {Mas-Montserrat, Daniel and Perera, Maria and Barrab{\'e}s, M{\'\i}riam and Geleta, Margarita and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cSchurholt22, title = {Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights}, booktitle = {NeurIPS 2022 - Neural Information Processing Systems}, year = {2022}, abstract = {

Learning representations of neural network weights given a model zoo is an emerging and challenging area with many potential applications from model inspection, to neural architecture search or knowledge distillation. Recently, an autoencoder trained on a model zoo was able to learn a hyper-representation, which captures intrinsic and extrinsic properties of the models in the zoo. In this work, we extend hyper-representations for generative use to sample new model weights as pre-training. We propose layer-wise loss normalization which we demonstrate is key to generate high-performing models and a sampling method based on the empirical density of hyper-representations. The models generated using our methods are diverse, performant and capable to outperform conventional baselines for transfer learning. Our results indicate the potential of knowledge aggregation from model zoos to new models via hyper-representations thereby paving the avenue for novel research directions.

}, author = {Sch{\"u}rholt, Konstantin and Knyazev, Boris and Xavier Gir{\'o}-i-Nieto and Borth, Damian} } @article {xBarrabes22, title = {Machine Learning for Genomic Sequence Processing}, year = {2022}, abstract = {

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

Predicting Dog Phenotypes from Genotypes

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

MergeGenome. A Python-based Toolkit for Merging VCF files

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

}, author = {Barrab{\'e}s, M{\'\i}riam}, editor = {Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cSchurholt22b, title = {Model Zoos: A Dataset of Diverse Populations of Neural Network Models}, booktitle = {NeurIPS 2022 Track Datasets and Benchmarks}, year = {2022}, month = {12/2022}, address = {New Orleans, Louisiana, USA.}, abstract = {

In the last years, neural networks (NN) have evolved from laboratory environments to the state-of-the-art for many real-world problems. It was shown that NN models (i.e., their weights and biases) evolve on unique trajectories in weight space during training. Following, a population of such neural network models (referred to as model zoo) would form structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can reveal latent properties of individual models. With such model zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of NN weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of NNs. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of NN models for further research. In total the proposed model zoo dataset is based on eight image datasets, consists of 27 model zoos trained with varying hyperparameter combinations and includes 50{\textquoteright}360 unique NN models as well as their sparsified twins, resulting in over 3{\textquoteright}844{\textquoteright}360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks.The dataset can be found at www.modelzoos.cc.

}, author = {Sch{\"u}rholt, Konstantin and Taskiran, Diyar and Knyazev, Boris and Xavier Gir{\'o}-i-Nieto and Borth, Damian} } @conference {cSchurholt22a, title = {Model Zoos: A Dataset of Diverse Populations of Neural Network Models}, booktitle = {NeurIPS}, year = {2022}, month = {09/2022}, abstract = {

In the last years, neural networks (NN) have evolved from laboratory environments to the state-of-the-art for many real-world problems. It was shown that NN models (i.e., their weights and biases) evolve on unique trajectories in weight space during training. Following, a population of such neural network models (referred to as model zoo) would form structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can reveal latent properties of individual models. With such model zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of NN weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks significantly increases the friction for further research about populations of NNs. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of NN models for further research. In total the proposed model zoo dataset is based on eight image datasets, consists of 27 model zoos trained with varying hyperparameter combinations and includes 50{\textquoteright}360 unique NN models as well as their sparsified twins, resulting in over 3,844,360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks.

}, author = {Sch{\"u}rholt, Konstantin and Taskiran, Diyar and Knyazev, Boris and Xavier Gir{\'o}-i-Nieto and Borth, Damian} } @article {xBudria22, title = {Multimodal 3D Hand Pose Enhancement for Sign Language}, year = {2022}, abstract = {

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

}, author = {{\'A}lvaro Budria}, editor = {Laia Tarr{\'e}s and Moreno, Francesc and Xavier Gir{\'o}-i-Nieto} } @conference {cBartusiak, title = {Predicting Dog Phenotypes from Genotypes}, booktitle = {44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}22)}, year = {2022}, abstract = {

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

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

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

}, url = {https://www.melba-journal.org/papers/2022:026.html}, author = {Raghav Metha and Angelos Filos and Ujjwal Baid and Laura Mora and Ver{\'o}nica Vilaplana and Christos Davatzikos and Bjoern Menze and Spyridon Bakas and Yarin Gal and Tar Arbel} } @article {cBellver-Bueno20, title = {RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation}, journal = {Multimedia Tools and Applications}, year = {2022}, month = {07/2022}, abstract = {

The task of video object segmentation with referring expressions (language-guided VOS) is to, given a linguistic phrase and a video, generate binary masks for the object to which the phrase refers. Our work argues that existing benchmarks used for this task are mainly composed of trivial cases, in which referents can be identified with simple phrases. Our analysis relies on a new categorization of the phrases in the DAVIS-2017 and Actor-Action datasets into trivial and non-trivial REs, with the non-trivial REs annotated with seven RE semantic categories. We leverage this data to analyze the results of RefVOS, a novel neural network that obtains competitive results for the task of language-guided image segmentation and state of the art results for language-guided VOS. Our study indicates that the major challenges for the task are related to understanding motion and static actions.

}, doi = {https://doi.org/10.1007/s11042-022-13413-x}, author = {Bellver-Bueno, M{\'\i}riam and Ventura, C. and Silberer, Carina and Kazakos, Ioannis and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @article {aBernal22, title = {SALAI-Net: species-agnostic local ancestry inference network}, journal = {Bioinformatics}, volume = {38}, year = {2022}, month = {09/2022}, chapter = {ii27}, abstract = {

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

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

}, doi = {https://doi.org/10.1093/bioinformatics/btac464}, url = {https://academic.oup.com/bioinformatics/article/38/Supplement_2/ii27/6701999?login=false$\#$373158629}, author = {Bernal, Oriol and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @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} } @mastersthesis {xBudria, title = {Topic Detection from Sign Language Videos}, year = {2022}, abstract = {

Significant progress has been made recently on challenging tasks in automatic sign language understanding, such as sign language recognition, translation and production. However, most works have focused on datasets with relatively few samples, short recordings and limited vocabulary and signing space. Moreover, they have neglected the less complex task of sign language video classification, whose analogue in spoken language, namely text classification, has been widely addressed. For this reason, in this work, we introduce the novel task of sign language topic detection. We base our experiments on How2Sign, a large-scale video dataset spanning multiple semantic domains. The contributions of this thesis are twofold. First, we present the first study of sign language topic detection in continuous sign language videos, providing baseline models for this task. Second, we perform a comparison between different visual features and deep learning architectures that are commonly employed in the sign language understanding literature. We implement our modelling pipelines in Fairseq, a PyTorch library that is extensively used in the spoken language community. Modular, extensible code for running our experiments is provided along this thesis.

}, author = {{\'A}lvaro Budria}, editor = {Laia Tarr{\'e}s and Xavier Gir{\'o}-i-Nieto} } @conference {cBudria, title = {Topic Detection in Continuous Sign Language Videos}, booktitle = {Accessibility, Vision, and Autonomy Meet (AVA) CVPR Workshop}, year = {2022}, month = {06/2022}, abstract = {

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

}, author = {{\'A}lvaro Budria and Laia Tarr{\'e}s and Gerard I. G{\'a}llego and Moreno, Francesc and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cMayoral21a, title = {Brain structural alterations in cognitively unimpaired individuals with discordant amyloid-β PET and CSF Aβ42 status: findings using Machine Learning}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Mahnaz Shekari and Gemma Salvad{\'o} and Greg Operto and Raffaele Cacciaglia and Carles Falcon and Aida Ni{\~n}erola Baiz{\'a}n and Andr{\'e}s Perissinotti and Carolina Minguillon and Karine Fauria and Ivonne Suridjan and Gwendlyn Kollmorgen and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cBonet21, title = {Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolation}, booktitle = {Asia Pacific Signal and Information Processing Association Annual Summit, APSIPA}, year = {2021}, month = {12/2021}, address = {Tokyo, Japan}, abstract = {

State-of-the-art neural network architectures continue to scale in size and deliver impressive generalization results, although this comes at the expense of limited interpretability. In particular, a key challenge is to determine when to stop training the model, as this has a significant impact on generalization. Convolutional neural networks (ConvNets) comprise high-dimensional feature spaces formed by the aggregation of multiple channels, where analyzing intermediate data representations and the model{\textquoteright}s evolution can be challenging owing to the curse of dimensionality. We present channel-wise DeepNNK (CW-DeepNNK), a novel channel-wise generalization estimate based on non-negative kernel regression (NNK) graphs with which we perform local polytope interpolation on low-dimensional channels. This method leads to instance-based interpretability of both the learned data representations and the relationship between channels. Motivated by our observations, we use CW-DeepNNK to propose a novel early stopping criterion that (i) does not require a validation set, (ii) is based on a task performance metric, and (iii) allows stopping to be reached at different points for each channel. Our experiments demonstrate that our proposed method has advantages as compared to the standard criterion based on validation set performance.

}, url = {https://arxiv.org/abs/2107.12972}, author = {Bonet, D. and Ortega, A. and Ruiz-Hidalgo, J. and Shekkizhar, S.} } @conference {cGeleta, title = {Deep variational autoencoders for population genetics: applications in classification, imputation, dimensionality reduction, and novel lossless data compression}, booktitle = {American Society of Human Genetics (ASHG)}, year = {2021}, month = {10/2021}, publisher = {ASHG}, organization = {ASHG}, address = {Virtual}, abstract = {

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

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

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

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

}, author = {Geleta, Margarita and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @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} } @phdthesis {dBellver, title = {Image and Video Object Segmentation in Low Supervision Scenarios}, volume = {PhD}, year = {2021}, month = {03/2021}, school = {Universitat Politecnica de Catalunya}, type = {Academic}, address = {Barcelona}, abstract = {

Image and video segmentation are central tasks within the computer vision field. Nevertheless, deep learning solutions for segmentation typically rely on pixel-level annotations, which are very costly to collect. Likewise, some segmentation systems require human interaction at inference time, which involves effort for the end-user. In this thesis, we look into diverse supervision scenarios for image and video object segmentation. We discern between supervision when learning the model, i.e., which type of annotations are used during training, and supervision at inference, namely which kind of human input is required when running the system. Our target are models that require low forms of supervision.

In the first part of the thesis we present a novel recurrent architecture for video object segmentation that is end-to-end trainable in a fully-supervised setup, and that does not require any post-processing step, i.e., the output of the model directly solves the addressed task. The second part of the thesis aims at lowering the annotation cost, in terms of labeling time, needed to train image segmentation models. We explore semi-supervised pipelines and show results when a very limited budget is available. The third part of the dissertation attempts to alleviate the supervision required by semi-automatic systems at inference time. Particularly, we focus on semi-supervised video object segmentation, which typically requires generating a binary mask for each instance to be tracked. In contrast, we present a model for language-guided video object segmentation, which identifies the object to segment with a natural language expression. We study current benchmarks, propose a novel categorization of referring expressions for video, and identify the main challenges posed by the video task.

Evaluation committee: Zeynep Akata (University of T{\"u}bingen), Francesc Moreno-Noguer (UPC IRI-CSIC) and Yannis Kalantidis (Naver Labs Europ).

M{\'\i}riam Bellver Phd defense UPC 2021
}, author = {M{\'\i}riam Bellver}, editor = {Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cHernandez, title = {Implementation of personalized medicine in cutaneous melanoma patients aided by artificial intelligence}, booktitle = {10th World Congress of2 Melanoma / 17th EADO Congress}, year = {2021}, month = {04/2021}, author = {Carlos Hernandez and Anil Kiroglu and Sergio Garc{\'\i}a and Joan Ficapal and Julio Burgos and Sebastian Podlipnik and Neus Calbet and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana and Marc Combalia} } @unpublished {xBonet21, title = {Improved Neural Network Generalization using Channel-Wise NNK Graph Constructions}, year = {2021}, publisher = {Final Year Project, UPC}, abstract = {

State-of-the-art neural network architectures continue to scale in size and deliver impressive results on unseen data points at the expense of poor interpretability. In the deep layers of these models we often encounter very high dimensional feature spaces, where constructing graphs from intermediate data representations can lead to the well-known curse of dimensionality. We propose a channel-wise graph construction method that works on lower dimensional subspaces and provides a new channel-based perspective that leads to better interpretability of the data and relationship between channels. In addition, we introduce a novel generalization estimate based on the proposed graph construction method with which we perform local polytope interpolation. We show its potential to replace the standard generalization estimate based on validation set performance to perform progressive channel-wise early stopping without requiring a validation set.

}, author = {Bonet, David}, editor = {Ortega, Antonio and Ruiz-Hidalgo, J. and Shekkizhar, Sarath} } @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 {cDominguez, title = {Neural ADMIXTURE: rapid population clustering with autoencoders}, booktitle = {2021 Society for Molecular Biology \& Evolution meeting (SMBEv2021)}, year = {2021}, month = {06/2021}, address = {Virtual}, abstract = {

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

}, author = {Dominguez, Albert and Mas-Montserrat, Daniel and Bustamante, Carlos and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @conference {cPodlipnik21, title = {Personalized medicine in melanoma patients aided by artificial intelligence}, booktitle = {Clinical Translation of Medical Image Computing and Computer Assisted Interventions (CLINICCAI) Workshop at MICCAI}, year = {2021}, month = {09/2021}, abstract = {

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

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

}, author = {Sebastian Podlipnik and Carlos Hernandez and Anil Kiroglu and Sergio Garc{\'\i}a and Joan Ficapal and Julio Burgos and Neus Calbet and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana and Marc Combalia} } @conference {cMayoral21b, title = {Prediction of amyloid pathology in cognitively unimpaired individuals using structural MRI}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Silvia Ingala and Luigi Lorenzini and Alle Meije Wink and Sven Haller and Jose Luis Molinuevo and Robin Wolz and Alessandro Palombit and Adam J Schwarz and Ga{\"e}l Chetelat and Pierre Payoux and Pablo Martinez-Lage and Giovanni Frisoni and Nick C Fox and Craig W Ritchie and Joanna M Wardlaw and Adam Waldman and Frederik Barkhof and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cKazakos, title = {SynthRef: Generation of Synthetic Referring Expressions for Object Segmentation}, booktitle = {NAACL Visually Grounded Interaction and Language (ViGIL) Workshop}, year = {2021}, month = {06/2021}, address = {Virtual}, abstract = {

Recent advances in deep learning have brought significant progress in visual grounding tasks such as language-guided video object segmentation. However, collecting large datasets for these tasks is expensive in terms of annotation time, which represents a bottleneck. To this end, in our work we propose a novel method, namely SynthRef, for generating synthetic referring expressions for an image (or video frame), and we also present and disseminate the first large-scale dataset with synthetic referring expressions for video object segmentation. Our experiments demonstrate that by training with our synthetic dataset one can improve the ability of a model to generalize across different datasets, without any additional annotation cost. Moreover, our formulation allows its application to any object detection or segmentation dataset.

}, author = {Kazakos, Ioannis and Bellver-Bueno, M{\'\i}riam and Ventura, C. and Silberer, Carina 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} } @article {9103248, title = {Grounded Sequence to Sequence Transduction}, journal = {IEEE Journal of Selected Topics in Signal Processing}, volume = {14}, year = {2020}, month = {05/2020}, pages = {577-591}, abstract = {

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

}, author = {L. Specia and L. Barrault and O. Caglayan and Amanda Duarte and D. Elliott and S. Gella and N. Holzenberger and C. Lala and S. J. Lee and J. Libovicky and P. Madhyastha and F. Metze and K. Mulligan and A. Ostapenko and S. Palaskar and R. Sanabria and J. Wang and R. Arora} } @article {aBellver, title = {Mask-guided sample selection for Semi-Supervised Instance Segmentation}, journal = {Multimedia Tools and Applications}, year = {2020}, month = {07/2020}, abstract = {

Image segmentation methods are usually trained with pixel-level annotations, which require significant human effort to collect. The most common solution to address this constraint is to implement weakly-supervised pipelines trained with lower forms of supervision, such as bounding boxes or scribbles. Another option are semi-supervised methods, which leverage a large amount of unlabeled data and a limited number of strongly-labeled samples. In this second setup, samples to be strongly-annotated can be selected randomly or with an active learning mechanism that chooses the ones that will maximize the model performance.\ In this work, we propose a sample selection approach to decide which samples to annotate for semi-supervised instance segmentation. Our method consists in first predicting pseudo-masks for the unlabeled pool of samples, together with a score predicting the quality of the mask. This score is an estimate of the Intersection Over Union (IoU) of the segment with the ground truth mask. We study which samples are better to annotate given the quality score, and show how our approach outperforms a random selection, leading to improved performance for semi-supervised instance segmentation with low annotation budgets.

}, doi = {10.1007/s11042-020-09235-4}, url = {http://link.springer.com/article/10.1007/s11042-020-09235-4}, author = {M{\'\i}riam Bellver and Amaia Salvador and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cPuig-Sitjes20, title = {Strategy for the real-time detection of thermal events on the plasma facing components of Wendelstein 7-X}, booktitle = {31st Symposium on Fusion Technology (SOFT2020)}, year = {2020}, month = {09/2020}, address = {Dubrovnik, Croatia}, abstract = {

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

}, url = {http://hdl.handle.net/21.11116/0000-0007-7FF5-7}, author = {Puig-Sitjes, A. and Jakubowski, M. and Fellinger, J. and Drewelow, P. and Gao, Y. and Niemann, H. and Sunn-Pedersen, T. and K{\"o}nig, R. and Naujoks, D. and Winter, A. and Laqua, H. and Dumke, S. and Moncada, V. and Belafdil, C. and Mitteau, R. and Aumeunier, M.-H. and Pisano, F. and Aymerich, E. and Cannas, B. and Kocsis, G. and Szepesi, T. and Cseh, G. and Szabolics, T. and Casas, J. and Morros, J.R. and Salembier, P. and Clemente, R. and Cobos, M. and I. Caminal and Palacios Corral, A. and Moreno Punzano, A. and Quiceno Lopera, S.} } @conference {cCombaliac, title = {BCN20000: Dermoscopic Lesions in the Wild}, booktitle = {International Skin Imaging Collaboration (ISIC) Challenge on Dermoscopic Skin Lesion Analysis 2019}, year = {2019}, month = {10/2019}, abstract = {

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

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

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

}, doi = {10.1109/TMI.2019.2901712}, author = {Li Wang and Dong Nie and Guannan Li and Elodie Puybareau and Jose Dolz and Qian Zhang and Fan Wang and Jing Xia and Zhengwang Wu and Jiawei Chen and Kim-HanThung and Toan Duc Bui and Jitae Shin and Guodong Zeng and Guoyan Zheng and Vladimir S. Fonov and Andrew Doyle and Yongchao Xu and Pim Moeskops and Josien Pluim and Christian Desrosiers and Ismail Ben Ayed and Gerard Sanroma and Oualid Benkarim and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Weili Lin and Gang Li and Dinggang Shen} } @conference {cBellverb, title = {Budget-aware Semi-Supervised Semantic and Instance Segmentation}, booktitle = {CVPR 2019 DeepVision Workshop}, year = {2019}, month = {05/2019}, publisher = {OpenCVF}, organization = {OpenCVF}, address = {Long Beach, CA, USA}, abstract = {

Methods that move towards less supervised scenarios are key for image segmentation, as dense labels demand significant human intervention. Generally, the annotation burden is mitigated by labeling datasets with weaker forms of supervision, e.g. image-level labels or bounding boxes. Another option are semi-supervised settings, that commonly leverage a few strong annotations and a huge number of unlabeled/weakly-labeled data. In this paper, we revisit semi-supervised segmentation schemes and narrow down significantly the annotation budget (in terms of total labeling time of the training set) compared to previous approaches. With a very simple pipeline, we demonstrate that at low annotation budgets, semi-supervised methods outperform by a wide margin weakly-supervised ones for both semantic and instance segmentation. Our approach also outperforms previous semi-supervised works at a much reduced labeling cost. We present results for the Pascal VOC benchmark and unify weakly and semi-supervised approaches by considering the total annotation budget, thus allowing a fairer comparison between methods.

Winners of the best paper award at the $\#$CVPR2019 DeepVision workshop

Budget-aware Semi-Supervised Semantic and Instance Segmentation from Universitat Polit{\`e}cnica de Catalunya
}, url = {https://arxiv.org/abs/1905.05880}, author = {M{\'\i}riam Bellver and Amaia Salvador and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xBalibrea19, title = {Deep learning for semantic segmentation of airplane hyperspectral imaging}, year = {2019}, abstract = {

Given their success, both qualitative and quantitative, Deep Neural Networks have been used to approach classification and segmentation problems for images, especially during these last few years where it has been possible to design computers with sufficient capacity to make quick and efficient experiments.

In this work, we will study the use of two Convolutional Neural Networks (CNNs) to segment the ground of a land section of Maspalomas{\textquoteright} Park using an image taken by the flight of an airplane.

The comparison will be made in terms of computational cost, complexity and results that will be obtained while testing different algorithms, loss functions or optimizers and also while tuning some other parameters. The results will also be compared with a past work done with the same dataset but another methodology (SVM).

}, author = {Mar Balibrea}, editor = {Luis Salgueiro and Ver{\'o}nica Vilaplana} } @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 {cRamon, title = {Hyperparameter-Free Losses for Model-Based Monocular Reconstruction}, booktitle = {ICCV 2019 Workshop on Geometry Meets Deep Learning}, year = {2019}, month = {11/2019}, publisher = {IEEE / Computer Vision Foundation}, organization = {IEEE / Computer Vision Foundation}, address = {Seoul, South Corea}, abstract = {

This work proposes novel hyperparameter-free losses for single view 3D reconstruction with morphable models (3DMM). We dispense with the hyperparameters used in other works by exploiting geometry, so that the shape of the object and the camera pose are jointly optimized in a sole term expression. This simplification reduces the optimization time and its complexity. Moreover, we propose a novel implicit regularization technique based on random virtual projections that does not require additional 2D or 3D annotations. Our experiments suggest that minimizing a shape reprojection error together with the proposed implicit regularization is especially suitable for applications that require precise alignment between geometry and image spaces, such as augmented reality. We evaluate our losses on a large scale dataset with 3D ground truth and publish our implementations to facilitate reproducibility and public benchmarking in this field.

}, author = {Ramon, Eduard and Ruiz, Guillermo and Batard, Thomas and Xavier Gir{\'o}-i-Nieto} } @article {xLLados19, title = {The pillars of the Computer Vision Catalan Alliance}, year = {2019}, abstract = {

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

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

{\textemdash} CVC_UAB (@CVC_UAB) September 17, 2019
}, author = {LLad{\'o}s, Josep and Bou, Elisenda and Bressan, Marco and Sala, Oscar and Xavier Gir{\'o}-i-Nieto} } @unpublished {cRamon19, title = {Plug-and-Train Loss for Model-Based Single View 3D Reconstruction}, journal = {BMVA Technical Meeting: 3D vision with Deep Learning}, year = {2019}, month = {02/2019}, publisher = {UPC}, address = {London, UK}, abstract = {

Obtaining 3D geometry from images is a well studied problem by the computer vision community. In the concrete case of a single image, a considerable amount of prior knowledge is often required to obtain plausible reconstructions. Recently, deep neural networks in combination with 3D morphable models (3DMM) have been used in order to address the lack of scene information, leading to more accurate results. Nevertheless, the losses employed during the training process are usually a linear combination of terms where the coefficients, also called hyperparameters, must be carefully tuned for each dataset to obtain satisfactory results. In this work we propose a hyperparameters-free loss that exploits the geometry of the problem for learning 3D reconstruction from a single image. The proposed formulation is not dataset dependent, is robust against very large camera poses and jointly optimizes the shape of the object and the camera pose.

Plug and-train Loss for Single View 3D Reconstruction from Universitat Polit{\`e}cnica de Catalunya
}, author = {Ramon, Eduard and Villar, Jordi and Ruiz, Guillermo and Batard, Thomas and Xavier Gir{\'o}-i-Nieto} } @conference {cHerrera-Palacioa, title = {Recurrent Instance Segmentation using Sequences of Referring Expressions}, booktitle = {NeurIPS workshop on Visually Grounded Interaction and Language (ViGIL)}, year = {2019}, month = {09/2019}, address = {Vancouver, Canada}, abstract = {

The goal of this work is segmenting the objects in an image which are referred to by a sequence of linguistic descriptions (referring expressions). We propose a deep neural network with recurrent layers that output a sequence of binary masks, one for each referring expression provided by the user. The recurrent layers in the architecture allow the model to condition each predicted mask on the previous ones, from a spatial perspective within the same image. Our multimodal approach uses off-the-shelf architectures to encode both the image and the referring expressions. The visual branch provides a tensor of pixel embeddings that are concatenated with the phrase embeddings produced by a language encoder. Our experiments on the RefCOCO dataset for still images indicate how the proposed architecture successfully exploits the sequences of referring expressions to solve a pixel-wise task of instance segmentation.

Recurrent Instance Segmentation with Linguistic Referring Expressions from Universitat Polit{\`e}cnica de Catalunya
}, author = {Herrera-Palacio, Alba and Ventura, C. and Silberer, Carina and Sorodoc, Ionut-Teodor and Boleda, Gemma and Xavier Gir{\'o}-i-Nieto} } @conference {cVenturaa, title = {RVOS: End-to-End Recurrent Network for Video Object Segmentation}, booktitle = {CVPR}, year = {2019}, month = {06/2019}, publisher = {OpenCVF / IEEE}, organization = {OpenCVF / IEEE}, address = {Long Beach, CA, USA}, abstract = {

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

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



}, url = {http://openaccess.thecvf.com/content_CVPR_2019/html/Ventura_RVOS_End-To-End_Recurrent_Network_for_Video_Object_Segmentation_CVPR_2019_paper.html}, author = {Ventura, C. and M{\'\i}riam Bellver and Girbau, A. and Amaia Salvador and Marqu{\'e}s, F. and Xavier Gir{\'o}-i-Nieto} } @article {aKuijf, title = {Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge}, journal = {IEEE Transactions on Medical Imaging}, year = {2019}, month = {03/2019}, abstract = {

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

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

Affective computing, leveraged by machine learning techniques, is advancing rapidly in the task of affect recognition in videos. However, there is a need for more annotated data. Several studies have built huge video datasets with emotions annotations. Others have collected music videos or film scenes datasets with physiological signals. However, none of them approached a solution with both physiological signals and user-generated videos. The work introduced here presents GALLUS, a novel database of user-generated videos with affective physiological responses. The database is composed of 775 videos that have been previously annotated through an online crowdsourcing platform. Physiological responses such as electroencephalography, electrocardiography, galvanic skin response, facial emotion recognition, and eye-gaze have been collected from 30 participants while they watched the stimuli. Our dataset will be made public to foster research in affect recognition.

}, author = {Granero, Marcel}, editor = {Borth, Damian and Weber, Barbara and Xavier Gir{\'o}-i-Nieto} } @conference {cFernandeze, title = {VLX-Stories: a Semantically Linked Event platform for media publishers}, booktitle = {Proceedings of the ISWC 2019 Satellite Tracks (Posters \& Demonstrations, Industry, and Outrageous Ideas) co-located with 18th International Semantic Web Conference (ISWC 2019)}, year = {2019}, month = {10/2019}, publisher = {CEUR Workshop Proceeedings}, organization = {CEUR Workshop Proceeedings}, address = {Auckland, New Zealand}, abstract = {

In this article we present a web platform used by media producers to monitor word events, detected by VLX-Stories.\ The event detector system retrieves multi-regional articles from news sites, aggregates them by topic, and summarizes them by extracting and structuring its most relevant entities in order to answer the journalism W{\textquoteright}s: who, what, when and where.\ The dashboard displays online detected events in a semantically linked space which allows navigation among trending news stories on distinct countries, categories and time.\ Moreover, detected events are linked to costumer contents, helping editorial process by providing real time access to breaking news related to their contents.\ (Demo paper)

}, url = {http://ceur-ws.org/Vol-2456/paper61.pdf}, author = {Fern{\`a}ndez, D{\`e}lia and Bou, Elisenda and Xavier Gir{\'o}-i-Nieto} } @conference {cFernandezd, title = {VLX-Stories: building an online Event Knowledge Base with Emerging Entity detection}, booktitle = {The Semantic Web {\textendash} ISWC 2019}, year = {2019}, month = {10/2019}, pages = {382-399}, publisher = {Springer, Cham}, organization = {Springer, Cham}, chapter = {24}, address = {Auckland, New Zealand}, abstract = {

We present an online multilingual system for event detection and comprehension from media feeds. The system retrieves information from news sites and social networks, aggregates them into events (event detection), and summarizes them by extracting semantic labels of its most relevant entities (event representation) in order to answer the journalism W{\textquoteright}s: who, what, when and where. The generated events populate VLX-Stories -an event Knowledge Base (KB)- transforming unstructured text data to a structured knowledge base representation.\ Our system exploits an external entity Knowledge Base (VLX-KG) to help populate VLX-Stories. At the same time, this external knowledge base can also be extended with a Dynamic Entity Linking (DEL) module, which detects Emerging Entities (EE) on unstructured data and adds them to VLX-KG.\ The system is currently used in production, detecting over 6000 monthly events from over 3500 news feeds from seven different countries and in three different languages.

}, keywords = {emerging entities, Entity Linking, event encoding, knowledge base population. knowledge graph, topic detection}, issn = {978-3-030-30796-7}, doi = {10.1007/978-3-030-30796-7_24}, url = {https://link.springer.com/chapter/10.1007/978-3-030-30796-7_24}, author = {Fern{\`a}ndez, D{\`e}lia and Bou, Elisenda and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xRos18, title = {Generative Adversarial Networks for Anomaly Detection in Images}, year = {2018}, abstract = {

Anomaly detection is used to identify abnormal observations that don{\textquoteright}t follow a normal pattern. In

this work, we use the power of Generative Adversarial Networks in sampling from image distributions

to perform anomaly detection with images and to identify local anomalous segments within this

images. Also, we explore potential application of this method to support pathological analysis of

biological tissues.

}, author = {Guillem Batiste}, editor = {Ver{\'o}nica Vilaplana} } @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.} } @conference {cFernandezc, title = {Linking Media: adopting Semantic Technologies for multimodal media connection}, booktitle = {International Semantic Web Conference - ISWC (Industry Track)}, year = {2018}, month = {08/2018}, address = {Monterey, CA, USA}, abstract = {

Today{\textquoteright}s media and news organizations are constantly generating large amounts of multimedia content, majorly delivered online. As the online media market grows, the management and delivery of contents is becoming a challenge.\ Computational approaches can help to overcome this challenge by governing different applications such as content creation, production, search, and its promotion and distribution to different audiences.\ In this abstract we present a success story of the adoption of semantic technologies on the aforementioned applications, which\ \ are built on top of a semantic tagging framework, based on a Knowledge Graph (KG).\ The presented pipeline combines multimodal inputs into a contextual entity linking module, which indexes documents and links them to trends and stories developing on the news.\ \ We will describe how documents are linked and provided to media producers through Vilynx{\textquoteright}s platform, which is currently indexing over 20k media documents a day.

}, keywords = {Knowledge graph; Linked data; Multimedia; Semantic web Linked data; Computational approach; Content creation; Knowledge graphs; Multimedia; Multimedia contents; Multimodal inputs; Semantic tagging; Semantic technologies; Semantic Web}, url = {http://ceur-ws.org/Vol-2180/}, author = {Fern{\`a}ndez, D{\`e}lia and Bou-Balust, Elisenda and Xavier Gir{\'o}-i-Nieto} } @unpublished {xFernandezb, title = {Multimodal Knowledge Base Population from News Streams for Media Applications}, year = {2018}, month = {07/2018}, type = {Phd thesis proposal}, abstract = {

Media producers publish large amounts of multimedia content online - both text, audio\ and video. As the online media market grows, the management and delivery of contents\ is becoming a challenge. Semantic and Linking technologies can be used to organize and\ exploit this contents. This dissertation addresses the problem of integrating Semantic Web\ technologies and linking data technologies into Vilynx{\textquoteright}s platform, a system used by media\ producers to manage and explode its contents. For that purpose, Knowledge Graphs (KG)\ and its maintenance through multimodal Knowledge Base Population (KBP) from online\ data extracted from the Web is studied. The Web is a very large unstructured data source\ with millions of text, images, videos and audio. This thesis is willing to generate solutions\ to facilitate automatic learning from these multimodal data and use it in real product applications\ for media.

This thesis is going to be structured in three parts. The first part of the thesis will cover\ the construction of a multimodal KG, which will be the core of the system for knowledge\ extraction, standardization and contextualization.

The\ second part will consist on the construction of the tools that will be used for KBP. For\ that we will construct a multimodal semantic tagging framework, based on the previously\ mentioned KG. This block addresses some typical challenges of KBP and data mining, like:\ name entity recognition (NER), entity linking (EL), context set construction (CSC), structured\ data creation, standardization, entity matching and data fusion.

The third part will focus on the extraction of knowledge from the Web to populate the knowledge\ base. As the KG domain is media, we will populate the KG using events detected from\ news streams using a multilmodal perspective. To detect events we will construct a news\ aggregator system. This part will deal with the problems of Topic Detection and Tracking\ (TDT), Topic Modeling (TM) and multi-document summarization. From these data we will\ learn relations between world entities, that will populate our KG, dealing with the automatic\ detection and update of concepts and relations. Also social media information will be\ analyzed to understand trendiness and world interests.

}, keywords = {Entity Detection, Entity Linking, Knowledge Base Population, Knowledge Graph, Linked Technologies, Multi-document Summarization, multimedia, Multimodal Systems, Natural Language Processing, Semantic Web, Topic Detection and Tracking, Topic Modeling}, author = {Fern{\`a}ndez, D{\`e}lia and Bou-Balust, Elisenda 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} } @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 {cFernandezb, title = {What is going on in the world? A display platform for media understanding}, booktitle = {IEEE Multimedia Information Processing and Retrieval (MIPR) Conference}, year = {2018}, month = {04/2018}, publisher = {IEEE}, organization = {IEEE}, address = {Miami, FL (USA)}, abstract = {

News broadcasters and on-line publishers daily generate a large amount of articles and videos describing events currently happening in the world. In this, work we present a system that automatically indexes videos from a library and links them to stories developing in the news. The user interface displays in an intuitive manner the links between\  videos and stories and allows navigation through related content by using associated tags. This interface is a powerful industrial tool for publishers to index, retrieve and visualize their video content. It helps them identify which topics require more attention or retrieve related content that{\textquoteright}s already been published about the stories.

}, doi = {https://doi.org/10.1109/MIPR.2018.00045}, url = {https://www.youtube.com/watch?v=eaXcB2X-5xY}, author = {Fern{\`a}ndez, D{\`e}lia and David Varas and Bou, Elisenda and Xavier Gir{\'o}-i-Nieto} } @conference {cBellvera, title = {Detection-aided liver lesion segmentation using deep learning}, booktitle = {ML4H: Machine Learning for Health Workshop at NIPS 2017}, year = {2017}, month = {11/2017}, abstract = {

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

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

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

Grade: A with honours (10.0/10.0)

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

Detection-aided liver lesion segmentation using deep learning from Xavier Giro-i-Nieto
}, author = {M{\'\i}riam Bellver}, editor = {Kevis-Kokitsi Maninis and Jordi Pont-Tuset and Luc van Gool and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @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} } @inbook {bBellot17, title = {Efficient Combination of Pairwise Feature Networks}, booktitle = {Neural Connectomics Challenge}, year = {2017}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, chapter = {7}, issn = {978-3-319-53069-7}, doi = {10.1007/978-3-319-53070-3}, author = {P Bellot and Patrick Meyer}, editor = {Battaglia, D and Guyon, I. and Lemaire, V. and Orlandi, J. and Ray, B. and Soriano, J.} } @inbook {bBellver17, title = {Hierarchical Object Detection with Deep Reinforcement Learning}, booktitle = {Deep Learning for Image Processing Applications}, volume = {31}, year = {2017}, publisher = {IOS Press}, organization = {IOS Press}, address = {Amsterdam, The Netherlands}, abstract = {

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

}, keywords = {computer Vision, Object detection, reinforcement learning}, issn = {978-1-61499-822-8}, doi = {10.3233/978-1-61499-822-8-164}, url = {http://ebooks.iospress.nl/volumearticle/48029}, author = {M{\'\i}riam Bellver and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Jordi Torres} } @conference {cRomero-Lopeza, title = {The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier based on Skin Lesion Images}, booktitle = {Annual Meeting of the Society of Imaging Informatics in Medicine (SIIM)}, year = {2017}, month = {06/2017}, publisher = {Society of Imaging Informatics for Medicine}, organization = {Society of Imaging Informatics for Medicine}, address = {Pittsburgh, PA, USA}, abstract = {

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

[SIIM 2017 Annual Meeting website]

[SIIM 2017 Session where our work is presented]

The Impact of Segmentation on the Accuracy and Sensitivity of a Melanoma Classifier Based on Skin Lesion Images from Oge Marques
}, url = {http://hdl.handle.net/2117/105582}, author = {Romero-Lopez, Adria and Burdick, Jack and Xavier Gir{\'o}-i-Nieto and Marques, Oge} } @article {aPont-Tuset2015, title = {Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation}, journal = { IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, volume = {38}, year = {2017}, pages = {128-140}, abstract = {

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

}, url = {http://arxiv.org/abs/1503.00848v1}, author = {Jordi Pont-Tuset and Pablo Arbelaez and Jonathan T. Barron and Marqu{\'e}s, F. and Jitendra Malik} } @mastersthesis {xBozal, title = {Personalized Image Classi cation from EEG Signals using Deep Learning}, year = {2017}, abstract = {

This thesis explores the semantic classi cation of images based processing of electroencephalogram\ (EEG) signals generated by the viewer{\textquoteright}s brain. The work extends an existing solution by\ exploring the gains obtained when the parameters of the classi er are adapted to the user. Firstly,\ we developed an universal end-to-end model based on deep learning that extracts features from\ the EEG raw signals predicts the semantic content of the image between 40 possible classes from\ the ImageNet dataset. Our main contribution aims at adapting this universal model to new users,\ in order to build a personalized model based on the minimum feedback from the new user. We\ explored di fferent deep learning architectures and hyperparameters to obtain a better accuracy\ than the baseline by Spampinato et al (CVPR 2017). We achieve a result of 89.03 \% and 90.34\ \% of the universal and personalized model respectively.\ 

Personalized Image classification of EEG Signals using Deep Learning from Xavier Giro-i-Nieto
}, author = {Bozal, Alberto}, editor = {Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xBernal, title = {Predicting emotion in movies: Recurrent and convolutional models applied to videos}, year = {2017}, abstract = {

This Thesis explores di erent approaches using deep learning techniques to predict emotions\ in videos.\ Working with videos implies a huge amount of data including visual frames and acoustic\ samples. The rst step of the project is basically to extract features to represent the videos in\ small sets of arrays. This procedure is done using pre-trained models based on Convolutional\ Networks, the state of the art in visual recognition. Firstly, visual features are extracted using\ 3D convolutions and acoustic features are extracted using VGG19, a pre-trained convolutional\ model for images fi ne-tuned to accept the audio inputs.\ Later, these features are fed into a Recurrent model capable of exploiting the temporal information.\ Emotions are measured in terms of valence and arousal, values between [-1, 1]. Additionally,\ the same techniques are also used to attempt to predict fear scenes. In consequence, this thesis\ deals with both regression and classi cation problems.\ Several architectures and di erent parameters have been tested in order to achieve the best\ performance. Finally, the results will be published in the MediaEval 2017 Challenge and compared\ to the state-of-the-art solutions.

Predicting Emotion in Movies from Xavier Giro-i-Nieto
}, author = {Bernal, Oriol}, editor = {Xavier Gir{\'o}-i-Nieto and Zaharieva, Maia} } @conference {xSalvadora, title = {Recurrent Semantic Instance Segmentation}, booktitle = {NIPS 2017 Women in Machine Learning Workshop (WiML)}, year = {2017}, month = {12/2017}, publisher = {NIPS 2017 Women in Machine Learning Workshop}, organization = {NIPS 2017 Women in Machine Learning Workshop}, address = {Long Beach, CA, USA}, abstract = {

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

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

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

}, author = {Amaia Salvador and Baradad, Manel and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {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 {xLidona, title = {Semantic Summarization of Egocentric Photo Stream Events}, booktitle = {ACM Multimedia 2017 Workshop on Lifelogging Tools and Applications}, year = {2017}, month = {10/2017}, publisher = {ACM}, organization = {ACM}, address = {Mountain View, CA, USA}, abstract = {

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

}, doi = {10.1145/3133202.3133204}, url = {https://dl.acm.org/citation.cfm?id=3133204}, author = {Lidon, Aniol and Bola{\~n}os, Marc and Dimiccoli, M. and Radeva, Petia and Garolera, Maite and Xavier Gir{\'o}-i-Nieto} } @conference {cRomero-Lopez, title = {Skin Lesion Classification from Dermoscopic Images using Deep Learning}, booktitle = {The 13th IASTED International Conference on Biomedical Engineering (BioMed 2017)}, year = {2017}, month = {02/2017}, address = {Innsbruck Austria}, abstract = {

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

[BioMed conference 2017]

}, keywords = {Convolutional Neural Networks, deep learning, machine learning, Medical Decision Support Systems, Medical Image Analysis, Skin Lesions}, url = {http://upcommons.upc.edu/handle/2117/103386}, author = {Romero-Lopez, Adria and Burdick, Jack and Xavier Gir{\'o}-i-Nieto and Marques, Oge} } @phdthesis {dBellot17, title = {Study of Gene Regulatory Networks Inference Methods from Gene Expression Data}, year = {2017}, author = {Pau Bellot}, editor = {Salembier, P.} } @conference {cLe, title = {Towards large scale multimedia indexing: A case study on person discovery in broadcast news}, booktitle = {International Workshop on Content-Based Multimedia Indexing - CBMI 2017}, year = {2017}, month = {06/2017}, address = {Firenze, Italy}, abstract = {

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

}, author = {Nam Le and Herv{\'e} Bredin and Gabriel Sargent and Miquel India and Paula Lopez-Otero and Claude Barras and Camille Guinaudeau and Guillaume Gravier and Gabriel Barbosa da Fonseca and Izabela Lyon Freire and Zenilton Patroc{\'\i}nio Jr. and Silvio Jamil F. Guimaraes and Gerard Mart{\'\i} and Morros, J.R. and Javier Hernando and Laura Docio-Fernandez and Carmen Garcia-Mateo and Sylvain Meignier and Jean-Marc Odobez} } @conference {cFernandeza, title = {ViTS: Video Tagging System from Massive Web Multimedia Collections}, booktitle = {ICCV 2017 Workshop on Web-scale Vision and Social Media }, year = {2017}, month = {10/2017}, address = {Venice, Italy}, abstract = {

The popularization of multimedia content on the Web has arised the need to automatically understand, index and retrieve it. In this paper we present ViTS, an automatic Video Tagging System which learns from videos, their web context and comments shared on social networks. ViTS analyses massive multimedia collections by Internet crawling, and maintains a knowledge base that updates in real time with no need of human supervision. As a result, each video is indexed with a rich set of labels and linked with other related contents. ViTS is an industrial product under exploitation with a vocabulary of over 2.5M concepts, capable of indexing more than 150k videos per month. We compare the quality and completeness of our tags with respect to the ones in the YouTube-8M dataset, and we show how ViTS enhances the semantic annotation of the videos with a larger number of labels (10.04 tags/video), with an accuracy of 80,87\%.

}, author = {Fern{\`a}ndez, D{\`e}lia and David Varas and Espadaler, Joan and Ferreira, Jordi and Woodward, Alejandro and Rodr{\'\i}guez, David and Xavier Gir{\'o}-i-Nieto and Riveiro, Juan Carlos and Bou, Elisenda} } @conference {cPoignant16, title = {The CAMOMILE Collaborative Annotation Platform for Multi-modal, Multi-lingual and Multi-media Documents}, booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)}, year = {2016}, month = {05/2016}, address = {Portoro{\v z} (Slovenia)}, abstract = {

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

}, keywords = {active learning, Annotation tool, collaborative annotation, multimedia, person annotation}, isbn = {978-2-9517408-9-1}, url = {http://www.lrec-conf.org/proceedings/lrec2016/pdf/456_Paper.pdf}, author = {Johann Poignant and Mateusz Budnik and Herv{\'e} Bredin and Claude Barras and Mickael Stefas and Pierrick Bruneau and Gilles Adda and Laurent Besacier and Hazim Ekenel and Gil Francopoulo and Javier Hernando and Joseph Mariani and Morros, J.R. and Georges Qu{\'e}not and Sophie Rosset and Thomas Tamisier} } @unpublished {xBellver, title = {Efficient search of objects in images using deep reinforcement learning}, journal = {NIPS Women in Machine Learning Workshop}, year = {2016}, type = {Abstract}, address = {Barcelona.}, abstract = {

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

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

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

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

References:

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

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


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

}, author = {M{\'\i}riam Bellver and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F.} } @conference {cBellver, title = {Hierarchical Object Detection with Deep Reinforcement Learning}, booktitle = {Deep Reinforcement Learning Workshop, NIPS 2016}, year = {2016}, month = {12/2016}, abstract = {

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

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

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

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

}, author = {M{\'\i}riam Bellver and Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Jordi Torres} } @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 {xBellver-Bueno, title = {Efficient Exploration of Region Hierarchies for Semantic Segmentation}, year = {2015}, abstract = {

Advisors: Carles Ventura-Royo (UPC) and Xavier Gir{\'o}-i-Nieto (UPC)

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

Grade: A (9.0/10.0)

The motivation of this work is the efficient exploration of hierarchical partitions for semantic segmentation as a method for locating objects in images. While many efforts have been focused on efficient image search in large-scale databases, few works have addressed the problem of locating and recognizing objects efficiently within a given image. My work considers as an input a hierarchical partition of an image that defines a set of regions as candidate locations to contain an object. This approach will be compared to other state of the art algorithms that extract object candidates for an image. The final goal of this work is to semantically segment images efficiently by exploiting the multiscale information provided by a hierarchical partition, maximizing the accuracy of the segmentation when only a very few regions of the partition are analysed.

Efficient exploration of region hierarchies for semantic segmentation from Xavier Giro

}, author = {M{\'\i}riam Bellver}, editor = {Ventura, C. and Xavier Gir{\'o}-i-Nieto} } @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.} } @article {aBellot Pujalte15, title = {NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference}, journal = {BMC Bioinformatics}, volume = {16}, year = {2015}, month = {09/2015}, abstract = {

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

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

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

}, keywords = {Benchmark, Bioconductor package, Gene expression, Gene regulation network reconstruction, Gene regulatory networks, Synthetic genetic networks}, doi = {10.1186/s12859-015-0728-4}, url = {http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0728-4}, author = {P. Bellot and Olsen, Catharina and Salembier, P. and Albert Oliveras and Meyer, P. E.} } @article {aBonet-Carne14a, title = {Quantitative Ultrasound Texture Analysis of Fetal Lungs To Predict Neonatal Respiratory Morbidity}, journal = {Ultrasound in Obstetrics and Gynecology, Wiley}, volume = {45}, year = {2015}, pages = {427{\textendash}433}, author = {E. Bonet-Carne and M. Palacio and T. Cobo and A. Perez-Moreno and M. Lopez and J. P. Piraquive and J. C. Ramirez and F. Marques and E. Gratacos} } @conference {Bell1512:Study, title = {Study of Normalization and Aggregation Approaches for Consensus Network Estimation}, booktitle = {2015 IEEE Symposium Series on Computational Intelligence: IEEE Symposium on Artificial Life (2015 IEEE ALIFE)}, year = {2015}, address = {Cape Town, South Africa}, abstract = {

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

}, author = {P. Bellot and Salembier, P. and Albert Oliveras and Meyer, P. E.} } @conference {cLidon, title = {UPC-UB-STP @ MediaEval 2015 Diversity Task: Iterative Reranking of Relevant Images}, booktitle = {MediaEval 2015 Workshop}, year = {2015}, month = {09/2015}, abstract = {

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

UPC-UB-STP @ MediaEval 2015 diversity task iterative reranking of relevant images from Xavier Giro
}, url = {http://ceur-ws.org/Vol-1436/}, author = {Lidon, Aniol and Bola{\~n}os, Marc and Seidl, Markus and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Zeppelzauer, Matthias} } @conference {cMestre, title = {Visual Summary of Egocentric Photostreams by Representative Keyframes}, booktitle = {IEEE International Workshop on Wearable and Ego-vision Systems for Augmented Experience (WEsAX) 2015}, year = {2015}, month = {07/2015}, address = {Turin, Italy}, abstract = {

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

}, keywords = {egocentric, keyframes, lifelogging, summarization}, url = {http://arxiv.org/abs/1505.01130}, author = {Bola{\~n}os, Marc and Mestre, Ricard and Talavera, Estefan{\'\i}a and Xavier Gir{\'o}-i-Nieto and Radeva, Petia} } @article {cBellot Pujalte14, title = {Efficient combination of pairwise feature networks}, journal = {JMLR: Workshop and Conference Proceedings }, volume = {46}, year = {2014}, month = {10/2015}, pages = {77 - 84}, publisher = {JMLR: Workshop and Conference Proceedings}, address = {Nancy, France}, abstract = {

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

}, keywords = {connectomes, elimination of indirect links, network reconstruction algorithms}, author = {P. Bellot and Meyer, P. E.} } @conference {cBosch14, title = {An Epipolar-Constrained Prior for Efficient Search in Multi-View Scenarios}, booktitle = {EUSIPCO}, year = {2014}, month = {09/2014}, address = {Lisbon}, abstract = {

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

}, keywords = {approximate nearest neighbor, deblurring, epipolar line, Super resolution}, author = {Bosch, I. and Salvador, J. and E. Perez-Pellitero and Ruiz-Hidalgo, J.} } @phdthesis {dBosio14, title = {Hierarchical information representation and efficient classification of gene expression microarray data}, volume = {PhD Thesis}, year = {2014}, month = {06/2014}, pages = {150}, school = {Universitat Politecnica de Catalunya}, address = {Barcelona}, abstract = {

In the field of computational biology, microarryas are used to measure the activity of thousands of genes at once and create a global picture of cellular function. Microarrays allow scientists to analyze expression of many genes in a single experiment quickly and efficiently. Even if microarrays are a consolidated research technology nowadays and the trends in high-throughput data analysis are shifting towards new technologies like Next Generation Sequencing (NGS), an optimum method for sample classification has not been found yet.

Microarray classification is a complicated task, not only due to the high dimensionality of the feature set, but also to an apparent lack of data structure. This characteristic limits the applicability of processing techniques, such as wavelet filtering or other filtering techniques that take advantage of known structural relation. On the other hand, it is well known that genes are not expressed independently from other each other: genes have a high interdependence related to the involved regulating biological process.

This thesis aims to improve the current state of the art in microarray classification and to contribute to understand how signal processing techniques can be developed and applied to analyze microarray data. The goal of building a classification framework needs an exploratory work in which algorithms are constantly tried and adapted to the analyzed data. The developed algorithms and classification frameworks in this thesis tackle the problem with two essential building blocks. The first one deals with the lack of a priori structure by inferring a data-driven structure with unsupervised hierarchical clustering tools. The second key element is a proper feature selection tool to produce a precise classifier as an output and to reduce the overfitting risk.

The main focus in this thesis is the binary data classification, field in which we obtained relevant improvements to the state of the art. The first key element is the data-driven structure, obtained by modifying hierarchical clustering algorithms derived from the Treelets algorithm from the literature. Several alternatives to the original reference algorithm have been tested, changing either the similarity metric to merge the feature or the way two feature are merged. Moreover, the possibility to include external sources of information from publicly available biological knowledge and ontologies to improve the structure generation has been studied too. About the feature selection, two alternative approaches have been studied: the first one is a modification of the IFFS algorithm as a wrapper feature selection, while the second approach involved an ensemble learning focus. To obtain good results, the IFFS algorithm has been adapted to the data characteristics by introducing new elements to the selection process like a reliability measure and a scoring system to better select the best feature at each iteration. The second feature selection approach is based on Ensemble learning, taking advantage of the microarryas feature abundance to implement a different selection scheme. New algorithms have been studied in this field, improving state of the art algorithms to the microarray data characteristic of small sample and high feature numbers.

In addition to the binary classification problem, the multiclass case has been addressed too. A new algorithm combining multiple binary classifiers has been evaluated, exploiting the redundancy offered by multiple classifiers to obtain better predictions.

All the studied algorithm throughout this thesis have been evaluated using high quality publicly available data, following established testing protocols from the literature to offer a proper benchmarking with the state of the art.

Whenever possible, multiple Monte Carlo simulations have been performed to increase the robustness of the obtained results.\ 

}, url = {http://hdl.handle.net/10803/145902}, author = {Bosio, M.}, editor = {Salembier, P. and Albert Oliveras} } @conference {cArbelaez14, title = {Multiscale Combinatorial Grouping}, booktitle = {Computer Vision and Pattern Recognition (CVPR)}, year = {2014}, abstract = {

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

Code and pre-computed results available here.

}, author = {Pablo Arbelaez and Jordi Pont-Tuset and Barron, Jon and Marqu{\'e}s, F. and Jitendra Malik} } @article {aBarkhuus14, title = {New interaction modes for rich panoramic live video experiences}, journal = {Behaviour \& Information Technology}, volume = {33}, year = {2014}, month = {07/2014}, chapter = {859-869}, abstract = {

The possibilities of panoramic video are based on the capabilities of high-resolution digital video streams and higher band- width{\textquoteright}s opportunities to broadcast, stream and transfer large content across platforms. With these opportunities also come challenges such as how to focus on sub-parts of the video stream and interact with the content shown on a large screen. In this paper, we present studies of two different interaction modes with a large-scale panoramic video for live experiences; we focus on interactional challenges and explore if it is (1) possible to develop new interactional methods/ways of approaching this type of high-resolution content and (2) feasible for users to interact with the content in these new ways. We developed prototypes for two different interaction modes: an individual system on a mobile device, either a tablet or a mobile phone, for interacting with the content on the same and a non-touch gesture-based system for the home or small group interaction. We present pilot studies where we explore the possibilities and challenges with these two interaction modes for panoramic content.

}, keywords = {interaction modes, interactive television, panoramic video, pilot studies}, doi = {10.1080/0144929X.2014.914975}, url = {http://www.tandfonline.com/doi/full/10.1080/0144929X.2014.914975}, author = {Barkhuus, Louise and Zoric, Goranka and Engstr{\"o}m, Arvid and Ruiz-Hidalgo, J. and Verzijp, Nico} } @article {aBonet-Carne14, title = {Quantitative Ultrasound Texture Analysis of Fetal Lungs To Predict Neonatal Respiratory Morbidity}, journal = {Ultrasound in Obstetrics and Gynecology, Wiley}, volume = {44}, year = {2014}, author = {E. Bonet-Carne and M. Palacio and T. Cobo and A. Perez-Moreno and M. Lopez and J. P. Piraquive and J. C. Ramirez and F. Marques and E. Gratacos} } @conference {cBosio13a, title = {Ensemble learning and hierarchical data representation for microarray classification}, booktitle = {13th IEEE International Conference on BioInformatics and BioEngineering BIBE}, year = {2013}, month = {11/2013}, publisher = {13th IEEE International Conference on BioInformatics and BioEngineering,}, organization = {13th IEEE International Conference on BioInformatics and BioEngineering,}, address = {Chania, Crete}, abstract = {

Microarray data classification is an open and active research field.

The development of more accurate algorithms is of great interest also because many of the techniques can be straightforwardly applied in analyzing different kinds of omics data.

In this work, an ensemble feature selection algorithm is applied within a binary classification framework from [1] that already got good predictive results. Ensemble feature selection is a rich field of research.

Ensemble techniques take some individual experts (i.e. classifiers) to combine them to improve the individual expert results with a voting scheme.

In this case, a thinning algorithm is proposed which starts by using all the available experts and removes them one by one focusing on improving the ensemble vote.

Two versions of an ensemble thinning algorithm have been tested and three key elements have been introduced to work with microarray data: the ensemble cohort definition, the nonexpert notion, which defines a set of excluded expert from the thinning process, and a rule to break ties in the thinning process. Experiments have been done on seven public datasets from the Microarray Quality Control study, MAQC [2].

The studied ensemble technique improves the state of the art results by producing classifiers with significantly better results and the proposed key elements have shown to be useful for the prediction performance.

}, keywords = {Microarray classification; metagenes; hierarchical representation; Treelets; feature selection; LDA; ensemble.}, author = {Bosio, M. and Salembier, P. and Albert Oliveras and P. Bellot} } @conference {cKochale13, title = {Gesture controlled interactive rendering in a panoramic scene}, booktitle = {European Interactive TV Conference, EuroITV}, year = {2013}, month = {06/2013}, address = {Como, Italy}, url = {http://upcommons.upc.edu/e-prints/handle/2117/20470}, author = {Kochale, A. and Ruiz-Hidalgo, J. and M. Borsum} } @conference {cZoric13, title = {Gesture Interaction with Rich TV Content in the Social Setting}, booktitle = {Exploring and Enhancing the User Experience for Television, Workshop of ACM SIGCHI Conference on Human Factors in Computing Systems, CHI{\textquoteright}13}, year = {2013}, month = {04/2013}, address = {Paris, France}, abstract = {

The appearance of new immersive TV content has increased the interactive possibilities presented to the viewers. Increased interactivity is seen as a valuable feature in viewing richer television content, but new functionalities are limited by what can be done naturally and intuitively using available devices like remote controls. Therefore, new interaction techniques, such as visual gestures control systems, have appeared aiming to enhance the viewers{\textquoteright} viewing experience. In this work we begin uncovering the potential and challenges of gesture interaction with ultra high definition video for people watching TV together. As a first step we have done a study with a group of people interacting with such content using a gesture-based system in the home environment.

}, url = {http://livingroomexperience.wikispaces.com/}, author = {Zoric, Goranka and Engstr{\"o}m, Arvid and Barkhuus, Louise and Ruiz-Hidalgo, J. and Kochale, A.} } @conference {cBosio13, title = {Hierarchical Clustering Combining Numerical and Biological Similarities for Gene Expression Data Classification}, booktitle = {35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}13) }, year = {2013}, month = {07/2013}, publisher = {IEEE EMBS}, organization = {IEEE EMBS}, address = {Osaka, Japan}, keywords = {Biological interpretation, Hierarchical representation, microarray classification}, author = {Bosio, M. and Salembier, P. and P. Bellot and Albert Oliveras} } @article {aMolina11 , title = {Real-time user independent hand gesture recognition from time-of-flight camera video using static and dynamic models}, journal = {Machine vision and applications}, volume = {24}, year = {2013}, month = {08/2011}, pages = {187{\textendash}204}, chapter = {187}, abstract = {

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

}, issn = {0932-8092}, doi = {10.1007/s00138-011-0364-6}, url = {http://www.springerlink.com/content/062m51v58073572h/fulltext.pdf}, author = {Molina, J. and Escudero-Vi{\~n}olo, M. and Signorelo, A. and M. Pard{\`a}s and Ferran, C. and Bescos, J. and Marqu{\'e}s, F. and Mart{\'\i}nez, J.} } @article {aBosio12, title = {Gene Expression Data Classification Combining Hierarchical Representation and Efficient Feature Selection}, journal = {Journal of Biological Systems}, volume = {20}, year = {2012}, pages = {349-375}, abstract = {

A general framework for microarray data classification is proposed in this paper. It produces precise and reliable classifiers through a two-step approach. At first, the original feature set is enhanced by a new set of features called metagenes. These new features are obtained through a hierarchical clustering process on the original data. Two different metagene generation rules have been analyzed, called Treelets clustering and Euclidean clustering. Metagenes creation is attractive for several reasons: first, they can improve the classification since they broaden the available feature space and capture the common behavior of similar genes reducing the residual measurement noise. Furthermore, by analyzing some of the chosen metagenes for classification with gene set enrichment analysis algorithms, it is shown how metagenes can summarize the behavior of functionally related probe sets. Additionally, metagenes can point out, still undocumented, highly discriminant probe sets numerically related to other probes endowed with prior biological information in order to contribute to the knowledge discovery process.

The second step of the framework is the feature selection which applies the Improved Sequential Floating Forward Selection algorithm (IFFS) to properly choose a subset from the available feature set for classification composed of genes and metagenes. Considering the microarray sample scarcity problem, besides the classical error rate, a reliability measure is introduced to improve the feature selection process. Different scoring schemes are studied to choose the best one using both error rate and reliability. The Linear Discriminant Analysis classifier (LDA) has been used throughout this work, due to its good characteristics, but the proposed framework can be used with almost any classifier. The potential of the proposed framework has been evaluated analyzing all the publicly available datasets offered by the Micro Array Quality Control Study, phase II (MAQC). The comparative results showed that the proposed framework can compete with a wide variety of state of the art alternatives and it can obtain the best mean performance if a particular setup is chosen. A Monte Carlo simulation confirmed that the proposed framework obtains stable and repeatable results.

}, doi = {10.1142/S0218339012400025}, url = {http://www.worldscientific.com/doi/abs/10.1142/S0218339012400025}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} } @conference {cBosio12, title = {Microarray classification with hierarchical data representation and novel feature selection criteria}, booktitle = {IEEE 12th International Conference on BioInformatics and BioEngineering}, year = {2012}, month = {11/2012}, address = {Larnaca, Cyprus}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} } @conference {cBosio12a, title = {Multiclass cancer microarray classification algorithm with Pair-Against-All redundancy}, booktitle = {The 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS{\textquoteright}12)}, year = {2012}, month = {12/2012}, address = {Washington, DC, USA}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} } @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.} } @article {aTenorio11, title = {Correlation between a semiautomated method based on ultrasound texture analysis and standard ultrasound diagnosis using white matter damage in preterm neonates as a model}, journal = {Journal of ultrasound in medicine}, volume = {30}, number = {10}, year = {2011}, pages = {1365{\textendash}1377}, issn = {0278-4297}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21968487}, author = {Tenorio, V. and Bonet, E. and Botet, F. and Marqu{\'e}s, F. and Amat, I. and Gratacos, E.} } @conference {cBosio11, title = {Feature set enhancement via hierarchical clustering for microarray classification}, booktitle = {IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS 2011}, year = {2011}, month = {dec.}, pages = {226 -229}, abstract = {

A new method for gene expression classification is proposed in this paper. In a first step, the original feature set is enriched by including new features, called metagenes, produced via hierarchical clustering. In a second step, a reliable classifier is built from a wrapper feature selection process. The selection relies on two criteria: the classical classification error rate and a new reliability measure. As a result, a classifier with good predictive ability using as few features as possible to reduce the risk of overfitting is obtained. This method has been tested on three public cancer datasets: leukemia, lymphoma and colon. The proposed method has obtained interesting classification results and the experiments have confirmed the utility of both metagenes and feature ranking criterion to improve the final classifier.

}, keywords = {cancer, classical classification error rate, classifier, colon, feature set enhancement, gene expression classification, hierarchical clustering, leukemia, lymphoma, medical computing, metagenes, microarray classification, overfitting risk reduction, pattern classification, pattern clustering, public cancer datasets, reliability measure, wrapper feature selection process}, doi = {10.1109/GENSiPS.2011.6169486}, author = {Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras} } @conference {cSchreer11, title = {Format-agnostic approach for production, delivery and rendering of immersive media}, booktitle = {Networked and Electronic Media Summit}, year = {2011}, pages = {{\textendash}}, isbn = {SN}, url = {http://nem-summit.eu/program/}, author = {Schreer, O. and Thomas, G. and Niamut, O. and Macq, J. and Kochale, A. and Batke, J. and Ruiz-Hidalgo, J. and Oldfield, R. and Shirley, B. and Thallinger, G.} } @conference {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.} } @conference {cBragos11a, title = {Implementation and first results of the Introduction to Engineering course in the ETSETB-UPC new degrees}, booktitle = {II Conferencia Internacional en Fomento e Innovaci{\'o}n con Nuevas Tecnolog{\'\i}as en la Docencia de la Ingenier{\'\i}a}, year = {2011}, pages = {1{\textendash}4}, isbn = {978-1-4577-0559-5}, doi = {10.1109/FINTDI.2011.5945971}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5945971}, author = {Bragos, R. and Pegueroles, J. and Alarc{\'o}n, E. and Camps, A. and Sarda, J. and Consolacion, C. and Mussons, J. and Pons, O. and Albert Oliveras and Garc{\'\i}a, M. and Onrubia, R. and Elisa Sayrol} } @conference {cAlarcon11, title = {Learning to conceive, design, implement and operate circuits and systems}, booktitle = {2011 IEEE International Symposium on Circuits and Systems}, year = {2011}, pages = {1183{\textendash}1186}, isbn = {978-1-4244-9472-9}, doi = {10.1109/ISCAS.2011.5937780}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5937780\&tag=1}, author = {Alarc{\'o}n, E. and Bragos, R. and Elisa Sayrol} } @conference {cPardas11, title = {Work in progress - Cooperative and competitive projects for engaging students in advanced ICT subjects}, booktitle = {41st Annual Frontiers in Education Conference}, year = {2011}, pages = {1{\textendash}3}, isbn = {3-540-76258-2}, doi = {10.1109/FIE.2011.6143032}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6143032\&tag=1}, author = {M. Pard{\`a}s and Bonafonte, A.} } @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 {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-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.} } @inbook {bBach09, title = {How can physicians quantify brain degeneration?}, booktitle = {Applied signal processing}, year = {2009}, pages = {411{\textendash}449}, isbn = {978-0-387-74534-3}, url = {http://cataleg.upc.edu/record=b1353617~S1*cat}, author = {Bach, M. and Thiran, J. and Marqu{\'e}s, F.}, editor = {Dutoit, T. and Marqu{\'e}s, F.} } @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.} } @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 {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 {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} } @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.} } @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} } @article {aSalembier07, title = {Structure description tools}, journal = {Journal of the American Society for Information Science}, volume = {58}, number = {9}, year = {2007}, pages = {1329{\textendash}1337}, issn = {0002-8231}, author = {Salembier, P. and Benitez, A.B} } @conference {cLopez06, title = {Rotating convection: Eckhauss-Benjamin-Feir instability}, booktitle = {59th Annual Meeting of the APS Division of Fluid Dynamics}, year = {2006}, pages = {2004{\textendash}2004}, isbn = {-}, author = {Lopez, J. and Mercader, M. and Marqu{\'e}s, F. and Batiste, O.} } @conference {cAnton05, title = {Collaborative Network Space: Infrastructure and Learning Application}, booktitle = {IEEE Region 8 EUROCON 2005 Conference: Computer as a tool.}, year = {2005}, pages = {803{\textendash}806}, isbn = {1-4244-0050-3}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01630054}, author = {Anton, M. and Vall-Llosera, M. and Jordi Torres and Romeu, J. and Jofre, L. and Sole, F. and Marqu{\'e}s, F. and Sabate, F. and Berenguer, J.} } @conference {cMarques05, title = {El concepto NetCampus}, booktitle = {3es Jornadas de la C{\'a}tedra Telef{\'o}nica-UPC}, year = {2005}, pages = {15{\textendash}20}, url = {https://catedratelefonica.upc.edu/documents/llibres/docs/el_espacio_innovador_y_la_red.pdf}, author = {Marqu{\'e}s, F. and Jofre, L. and Sole, F. and Sabate, F. and Berenguer, J. and Romeu, J. and Jordi Torres} } @book {eJofre05, title = {El "Espacio Innovador" y la red}, year = {2005}, url = {http://www.upc.edu/web/CatedraTelefonicaUPC}, author = {Jofre, L. and Sole, F. and Sabate, F. and Berenguer, J. and Marqu{\'e}s, F. and Romeu, J. and Jordi Torres} } @conference {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} } @conference {cBroquetas04, title = {Definici{\'o} d{\textquoteright}un Master Internacional de Recerca: la proposta del Departament de Teoria del Senyal i Comunicacions}, booktitle = {Jornada de reflexi{\'o}n y trabajo sobre el modelo docente de la UPC en el Espacio Europeo de ecuaci{\'o}n Superior (EEES)}, year = {2004}, pages = {1{\textendash}3}, author = {Broquetas, A. and Hernando, J. and Marqu{\'e}s, F. and Romeu, J.} } @conference {cLobo04, title = {Enhanced audio data hiding synchronization using non-linear filter}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing}, year = {2004}, pages = {885{\textendash}888}, author = {Lobo, A. and Bas, P. and Marqu{\'e}s, F.} } @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 {cLandabaso03, title = {HMM recognition of expressions in unrestrained video intervals}, booktitle = {International conference on Acoustics, Speech, and Signal Processing}, year = {2003}, pages = {197{\textendash}200}, isbn = {0-7803-7664-1}, author = {Landabaso, J. and M. Pard{\`a}s and Bonafonte, A.} } @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.} } @inbook {bSalembier02b, title = {Description of a Single Multimedia Document}, booktitle = {Introduction to the mpeg-7: multimedia content description interface}, year = {2002}, pages = {111{\textendash}138}, publisher = {Wiley}, organization = {Wiley}, edition = {B. S. Manjunath, P. Salembier, T. Sikora (Eds.)}, chapter = {8}, isbn = {0471486787}, author = {Benitez, A.B and Martinez, J.M and Rising, H and Salembier, P.} } @conference {cPardas02, title = {Emotion recognition based on MPEG-4 facial animation parameters}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, year = {2002}, pages = {3624{\textendash}3627}, author = {M. Pard{\`a}s and Bonafonte, A. and Landabaso, J.} } @article {aPardas02, title = {Facial animation parameters extraction and expression recognition using Hidden Markov Models}, journal = {Signal processing: image communication}, number = {17}, year = {2002}, pages = {675{\textendash}688}, issn = {0923-5965}, author = {M. Pard{\`a}s and Bonafonte, A.} } @conference {cPardas01, title = {Facial Animation Parameters extraction and Expression detection using HMM}, booktitle = {International Conference on Augmented, Virtual Environments and 3D Imaging}, year = {2001}, pages = {120{\textendash}123}, isbn = {1-4244-1764-3}, author = {M. Pard{\`a}s and Bonafonte, A.} } @conference {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 {cBroquetas00, title = {Detecci{\'o}n de vertidos de petr{\'o}leo en el mar a partir de im{\'a}genes SAR}, booktitle = {XV Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {2000}, pages = {327{\textendash}328}, isbn = {84-600-9597-5}, author = {Broquetas, A. and Toronjo, C. and Gasull, A. and F{\'a}bregas, F.X. and Moreno, V. and Herrero, M.} } @conference {cMarques99a, title = {Human face segmentation and tracking using connected components and partition projection}, booktitle = {1999 IEEE INternational Conference on Image Processing}, year = {1999}, isbn = {0-7803-5470-2}, author = {Marqu{\'e}s, F. and Ver{\'o}nica Vilaplana and Buxes, A.} } @conference {cMarques99, title = {A morphological approach for human face segmentation and tracking}, booktitle = {Workshop on Image Analysis for Multimedia Services}, year = {1999}, pages = {41{\textendash}44}, author = {Marqu{\'e}s, F. and Ver{\'o}nica Vilaplana and Buxes, A.} } @conference {cRuiz-Hidalgo99, title = {Towards stereo from scale-trees}, booktitle = {7th International Conference on Image Processing and its Applications}, year = {1999}, pages = {52{\textendash}56}, author = {Moravec, K. and Ruiz-Hidalgo, J. and Harvey, R. and Bangham, J.} } @conference {cRuiz-Hidalgo98a, title = {Robust morphological scale-space trees}, booktitle = {Noblesse Workshop on Non-Linear Model Based Image Analysis}, year = {1998}, month = {07/1998}, pages = {133{\textendash}139}, author = {Ruiz-Hidalgo, J. and Bangham, J. and Harvey, R.} } @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 {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 {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 {cSalembier96e, title = {Morphological operators for very low bit rate video coding}, booktitle = {IEEE International Conference on Image Processing, ICIP 1996}, year = {1996}, pages = {659{\textendash}662}, address = {Lausanne, Switzerland}, isbn = {0-8194-5654-3}, author = {Salembier, P. and Meyer, F. and Brigger, P. and Bouchard, L.} } @article {pSalembier96a, title = {Segmented Picture Coding Method and System and Corresponding Decoding Method and System}, number = {964009161-}, year = {1996}, type = {Invention patent}, author = {Salembier, P. and Marqu{\'e}s, F. and Corset, I. and Bouchard, L. and Jeannin, S. and M. Pard{\`a}s and Morros, J.R. and Meyer, F. and Marcotegui, B.} } @inbook {bCasas96c, title = {Strong edge features for image coding}, booktitle = {Mathematical Morphology and its Applications to Image and Signal Processing}, volume = {5}, year = {1996}, pages = {443{\textendash}450}, publisher = {Springer}, organization = {Springer}, address = {Boston}, abstract = {

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

}, isbn = {0-793-9733-9}, doi = {10.1007/978-1-4613-0469-2_52}, author = {Casas, J. and Torres, L.}, editor = {Schafer, R. W. and Maragos, P. and Butt, M. A.} } @conference {cMarques95, title = {Coding of image partitions by morphological skeleton using overlapping structuring elements}, booktitle = {IEEE WORKSHOP ON NONLINEAR SIGNAL AND IMAGE PROCESSING}, year = {1995}, pages = {250{\textendash}253}, author = {Marqu{\'e}s, F. and Fioravanti, S. and Brigger, P.} } @conference {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 {cSalembier93c, title = {Edge versus contrast estimation of morphological filters}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1993}, year = {1993}, address = {Mineapolis (MI), USA}, author = {Salembier, P. and Serra, J. and Bangham, J.} } @conference {cBonafonte92a, title = {Efficient integration of coarticulation and lexical information ...}, booktitle = {INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING}, year = {1992}, pages = {45{\textendash}48}, author = {Bonafonte, A. and Mari{\~n}o, J. and M. Pard{\`a}s} } @conference {cBonafonte92, title = {N-best hypotheses:an approach taking into account coarticulation}, booktitle = {EUROPEAN SIGNAL PROCESSING CONFERENCE}, year = {1992}, pages = {379{\textendash}382}, author = {Bonafonte, A. and M. Pard{\`a}s and Mari{\~n}o, J.} }