@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 {cTarresa, title = {Sign Language Translation from Instructional Videos}, booktitle = {CVPR 2023 Women in Computer Vision Workshop}, year = {2023}, month = {04/2023}, publisher = {Computer Vision Foundation / IEEE}, organization = {Computer Vision Foundation / IEEE}, address = {Vancouver, Canada}, abstract = {

The advances in automatic sign language translation (SLT) to spoken languages have been mostly benchmarked with datasets of limited size and restricted domains. Our work advances the state of the art by providing the first baseline results on How2Sign, a large and broad dataset. We train a Transformer over I3D video features, using the reduced BLEU as a reference metric for validation, instead of the widely used BLEU score. We report a result of 8.03 on the BLEU score, and publish the first open-source implementation of its kind to promote further advances.

}, author = {Laia Tarr{\'e}s and Gerard I. G{\'a}llego and Amanda Duarte and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cCaselles, title = {SIRA: Relightable Avatars from a Single Image}, booktitle = {Winter Conference on Applications of Computer Vision (WACV)}, year = {2023}, abstract = {

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

}, author = {Caselles, Pol and Ramon, Eduard and Garcia, Jaime and Xavier Gir{\'o}-i-Nieto and Moreno, Francesc and Triginer, Gil} } @article {canet2022context, title = {Context-unsupervised adversarial network for video sensors}, journal = {Sensors}, volume = {22}, year = {2022}, pages = {3171}, abstract = {

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

}, author = {Canet Tarr{\'e}s, Gemma and Pard{\`a}s, Montse} } @phdthesis {dDuarte, title = {Data and methods for a visual understanding of sign languages}, volume = {PhD}, year = {2022}, type = {International Mention}, abstract = {

Signed languages are complete and natural languages used as the first or preferred mode of communication by millions of people worldwide. However, they, unfortunately, continue to be marginalized languages. Designing, building, and evaluating models that work on sign languages presents compelling research challenges and requires interdisciplinary and collaborative efforts. The recent advances in Machine Learning (ML) and Artificial Intelligence (AI) has the power to enable better accessibility to sign language users and narrow down the existing communication barrier between the Deaf community and non-sign language users. However, recent AI-powered technologies still do not account for sign language in their pipelines. This is mainly because sign languages are visual languages, that use manual and non-manual features to convey information, and do not have a standard written form. Thus, the goal of this thesis is to contribute to the development of new technologies that account for sign language by creating large-scale multimodal resources suitable for training modern data-hungry machine learning models and developing automatic systems that focus on computer vision tasks related to sign language that aims at learning better visual understanding of sign languages.Thus, in Part I, we introduce the How2Sign dataset, which is a large-scale collection of multimodal and multiview sign language videos in American Sign Language. In Part II, we contribute to the development of technologies that account for sign languages by presenting in Chapter 4 a framework called Spot-Align, based on sign spotting methods, to automatically annotate sign instances in continuous sign language. We further present the benefits of this framework and establish a baseline for the sign language recognition task on the How2Sign dataset. In addition to that, in Chapter 5 we benefit from the different annotations and modalities of the How2Sign to explore sign language video retrieval by learning cross-modal embeddings. Later in Chapter 6, we explore sign language video generation by applying Generative Adversarial Networks to the sign language domain and assess if and how well sign language users can understand automatically generated sign language videos by proposing an evaluation protocol based on How2Sign topics and English translation.

}, author = {Amanda Duarte}, editor = {Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @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} } @mastersthesis {xMohamed22, title = {Exploring Visual Representations for Sign Language Translation}, year = {2022}, abstract = {

Sign Language Translation (SLT) task has been addressed in multiple approaches in recent years. In this work we aim to investigate the impact of using different types of visual sign language representation for SLT. For this investigation we use the state-of-the-art in SLT, the Sign Language Transformers model. We compare the translation output performance of two types of body pose estimation models as our skeleton extractor,\  and 2D CNN features trained on the test dataset. These later perform best, and I3D features outperform the pose estimation-based ones.\ 

}, author = {Maram A. Mohamed}, editor = {Laia Tarr{\'e}s and Xavier Gir{\'o}-i-Nieto} } @article {pTerradas22, title = {A method, system and computer programs to automatically transform an image}, journal = {European Patent Office}, volume = {21382176}, year = {2022}, abstract = {

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

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

}, issn = {21382176.2}, url = {https://patentscope.wipo.int/search/es/detail.jsf?docId=EP373278976}, author = {Terradas, R. and Pau Domingo and Grau, M. and Alarc{\'o}n, E. and Ruiz-Hidalgo, J.} } @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} } @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} } @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} } @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 {xCabot22, title = {Sign Language Translation based on Transformers for the How2Sign Dataset}, year = {2022}, abstract = {

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

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

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

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


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

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

Patricia Cabot with her advisors Laia Tarr{\'e}s, Gerard I. G{\'a}llego and Xavier Gir{\'o}-i-Nieto.
}, author = {Patricia Cabot and Laia Tarr{\'e}s and Xavier Gir{\'o}-i-Nieto} } @conference {cTarres22, title = {Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22}, booktitle = {EMNLP 2022 Seventh Conference on Machine Translation (WMT22)}, year = {2022}, month = {10/2022}, abstract = {

This paper describes the system developed at the Universitat Polit{\`e}cnica de Catalunya for the Workshop on Machine Translation 2022 Sign Language Translation Task, in particular, for the sign-to-text direction. We use a Transformer model implemented with the Fairseq modeling toolkit. We have experimented with the vocabulary size, data augmentation tech- niques and pretraining the model with the PHOENIX-14T dataset. Our system obtains 0.50 BLEU score for the test set, improving the organizers{\textquoteright} baseline by 0.38 BLEU. We remark the poor results for both the baseline and our system, and thus, the unreliability of our findings.

Poster view

}, author = {Laia Tarr{\'e}s and Gerard I. G{\'a}llego and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @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} } @mastersthesis {xTarres21, title = {GAN-based Image Colourisation with Feature Reconstruction Loss}, year = {2021}, abstract = {

Automatic image colourisation is a complex and ambiguous task due to having multiple correct solutions. Previous approaches have resulted in desaturated results unless relying on significant user interaction.\ In this thesis we study the state of the art for colourisation and we propose an automatic colourisation approaches based on generative adversarial networks that incorporates a feature reconstruction loss during training. The generative network is framed in an adver- sarial model that learns how to colourise by incorporating a perceptual understanding of the colour. Qualitative and quantitative results show the capacity of the proposed method to colourise images in a realistic way, boosting the colourfulness and perceptual realism of previous GAN-based methodologies.\ We also study and propose a second approach that incorporates segmentation information in the GAN framework and obtain quantitative and qualitative results.

}, author = {Laia Tarr{\'e}s}, editor = {Mrak, Marta and Xavier Gir{\'o}-i-Nieto} } @conference {cTarres, title = {GAN-based Image Colourisation with Feature Reconstruction Loss}, booktitle = {CVPR 2021 Women in Computer Vision Workshop}, year = {2021}, month = {06/2021}, address = {Virtual}, abstract = {

Image colourisation is the task of adding plausible colour to grayscale images. This transformation requires obtaining a three dimensional colour-valued mapping from a real-valued grayscale image, which leads to an undetermined problem because the gray-scale semantics and texture provide cues for multiple possible colour mappings. The goal of image colourisation in not to recover the ground truth colour in a manner that it is perceived as natural by a human observer.\ Our work takes as a baseline a scheme based on an end-to-end trainable convolutional neural network (CNN) trained with a smooth L1 loss to predict the $ab$ channels of a colour image given the $L$ channel. We introduce an extra perceptual reconstruction loss during training to improve the capabilities of a adversarial adversarial model, that we adopt as a baseline.

}, author = {Laia Tarr{\'e}s and G{\'o}rriz, Marc and Xavier Gir{\'o}-i-Nieto and Mrak, Marta} } @conference {cRamonb, title = {H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction}, booktitle = {International Conference on Computer Vision (ICCV)}, year = {2021}, publisher = {IEEE/CVF}, organization = {IEEE/CVF}, address = {Virtual}, abstract = {

Recent learning approaches that implicitly represent surface geometry using coordinate-based neural representations have shown impressive results in the problem of multi-view 3D reconstruction. The effectiveness of these techniques is, however, subject to the availability of a large number (several tens) of input views of the scene, and computationally demanding optimizations. In this paper, we tackle these limitations for the specific problem of few-shot full 3D head reconstruction, by endowing coordinate-based representations with a probabilistic shape prior that enables faster convergence and better generalization when using few input images (down to three). First, we learn a shape model of 3D heads from thousands of incomplete raw scans using implicit representations. At test time, we jointly overfit two coordinate-based neural networks to the scene, one modeling the geometry and another estimating the surface radiance, using implicit differentiable rendering. We devise a two-stage optimization strategy in which the learned prior is used to initialize and constrain the geometry during an initial optimization phase. Then, the prior is unfrozen and fine-tuned to the scene. By doing this, we achieve high-fidelity head reconstructions, including hair and shoulders, and with a high level of detail that consistently outperforms both state-of-the-art 3D Morphable Models methods in the few-shot scenario, and non-parametric methods when large sets of views are available.

}, author = {Ramon, Eduard and Triginer, Gil and Escur, Janna and Pumarola, Albert and Garcia, Jaime and Xavier Gir{\'o}-i-Nieto and Moreno, Francesc} } @conference {cDuarte20, title = {How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language}, booktitle = {CVPR 2021}, year = {2021}, month = {06/2021}, abstract = {

Sign Language is the primary means of communication for the majority of the Deaf community. One of the factors that has hindered the progress in the areas of automatic sign language recognition, generation, and translation is the absence of large annotated datasets, especially continuous sign language datasets, i.e. datasets that are annotated and segmented at the sentence or utterance level. Towards this end, in this work we introduce How2Sign, a work-in-progress dataset collection. How2Sign consists of a parallel corpus of 80 hours of sign language videos (collected with multi-view RGB and depth sensor data) with corresponding speech transcriptions and gloss annotations. In addition, a three-hour subset was further recorded in a geodesic dome setup using hundreds of cameras and sensors, which enables detailed 3D reconstruction and pose estimation and paves the way for vision systems to understand the 3D geometry of sign language.





}, author = {Amanda Duarte and S. Palaskar and Lucas Ventura and Ghadiyaram, Deepti and DeHaan, Kenneth and F. Metze and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @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 {cPuig-Sitjes21, title = {Spatio-temporal Detection and Tracking of Thermal Events on the Plasma Facing Components of Wendelstein 7-X}, booktitle = {4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis}, year = {2021}, month = {11/2021}, publisher = {iaea.org}, organization = {iaea.org}, address = {Shanghai (online)}, abstract = {

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

}, url = {https://conferences.iaea.org/event/251/contributions/20704/}, author = {Puig-Sitjes, A. and Casas, J. and Salembier, P. and Vizcarro, D. and Clemente, R. and Jakubowski, M. and Gao, Y. and W7-X Team} } @mastersthesis {xIturralde, title = {Towards video alignment across cameras with sign language 2D poses}, year = {2021}, abstract = {

This thesis degree is part of a project from the Image Group at UPC that is focused on sign language translation using deep learning technologies. This thesis builds on top of an existing database called How2Sign, that contains more than 83 hours of sign language translation videos. This database has some textual annotations aligned to a front RGB camera. The same scenes are also captured by a side RGB and a front RGB-D cameras. These three cameras are not synchronized, so it is necessary to align the segments annotated on the RGB front camera to the other cameras. This thesis explores a solution based on the cross correlation operator. Our work is to process the coordinates of the joints of the subject that appears in the videos, not from the point of view of image or video processing based on pixels. The first part if this thesis is to investigate the properties of the cross-correlation function by locating short video segments of a long recording based on automatically extracted 2D human poses. The experiments studied the impact of adding noise. The second part applied the cross-correlation to try to align two videos with the same scene, but recorded with different cameras from different points of view.

}, author = {Andrea Iturralde}, editor = {Laia Tarr{\'e}s and Xavier Gir{\'o}-i-Nieto} } @phdthesis {dCampos20, title = {Deep Learning that Scales: Leveraging Compute and Data}, year = {2020}, month = {12/2020}, school = {Universitat Polit{\`e}cnica de Catalunya}, address = {Barcelona, Catalonia}, abstract = {

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

}, url = { http://hdl.handle.net/10803/670372}, author = {V{\'\i}ctor Campos}, editor = {Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cCamposb, title = {Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills}, booktitle = {International Conference on Machine Learning (ICML) 2020}, year = {2020}, month = {07/2020}, abstract = {

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




}, author = {V{\'\i}ctor Campos and Trott, Alexander and Xiong, Caiming and Socher, Richard and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @article {aRey-Arena20, title = {FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations}, journal = {Information Fusion}, volume = {63}, year = {2020}, month = {10/2020}, chapter = {188}, abstract = {

It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i.e., the network becomes highly biased to the data it has been trained on. This issue is often alleviated using transfer learning, regularization techniques and/or data augmentation. This work presents a new approach, independent but complementary to the previous mentioned techniques, for improving the generalization of DNNs on very small datasets in which the involved classes share many visual features. The proposed model, called FuCiTNet (Fusion Class inherent Transformations Network), inspired by GANs, creates as many generators as classes in the problem. Each generator, k, learns the transformations that bring the input image into the k-class domain. We introduce a classification loss in the generators to drive the leaning of specific k-class transformations. Our experiments demonstrate that the proposed transformations improve the generalization of the classification model in three diverse datasets.

}, doi = {10.1016/j.inffus.2020.06.015}, author = {Rey-Arena, M. and Guirado, E. and Tabik, S. and Ruiz-Hidalgo, J.} } @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 {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 {xTarres19, title = {Clasificaci{\'o}n de lesiones de piel con un ensemble de redes neuronales residuales}, year = {2019}, abstract = {

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

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

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

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

}, author = {Laia Tarr{\'e}s}, editor = {Ver{\'o}nica Vilaplana and Marc Combalia} } @conference {cDuarteb, title = {Cross-modal Neural Sign Language Translation}, booktitle = {Proceedings of the 27th ACM International Conference on Multimedia - Doctoral Symposium}, year = {2019}, month = {10/2019}, publisher = {ACM}, organization = {ACM}, address = {Nice, France}, abstract = {

Sign Language is the primary means of communication for the majority of the Deaf and hard-of-hearing communities. Current computational approaches in this general research area have focused specifically on sign language recognition and the translation of sign language to text. However, the reverse problem of translating from spoken to sign language has so far not been widely explored.

The goal of this doctoral research is to explore sign language translation in this generalized setting, i.e. translating from spoken language to sign language and vice versa. Towards that end, we propose a concrete methodology for tackling the problem of speech to sign language translation and introduce How2Sign, the first public, continuous American Sign Language dataset that enables such research. With a parallel corpus of almost 60 hours of sign language videos (collected with both RGB and depth sensor data) and the corresponding speech transcripts for over 2500 instructional videos, How2Sign is a public dataset of unprecedented scale that can be used to advance not only sign language translation, but also a wide range of sign language understanding tasks.

Poster at ACM Multimedia 2019

Xavier Giro and Amanda Duarte in ACM Multimedia 2019.

}, doi = {10.1145/3343031.3352587}, url = {https://dl.acm.org/citation.cfm?id=3352587}, author = {Amanda Duarte}, editor = {Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cPareto, title = {Prediction of a second clinical event in CIS patients by combining lesion and brain features}, booktitle = {Congress of the European Comitee for Treatment and Research in Multiple Sclerosis (ECTRIMS 2019)}, year = {2019}, month = {09/2019}, abstract = {

Prediction of a second clinical event in clinically isolated syndrome (CIS) patients, leading to clinically definite multiple sclerosis (CDMS), is still a matter of investigation. The aim of the current study was to predict conversion to CDMS after a first attack, by means of a machine-learning approach and MRI-derived brain features. For that purpose, lesion-based features (volume and distribution within brain lobes) as well as cortical thickness (CT) and deep grey matter volumes (DGMV) were used. The final goal was to determine which features were more frequently found in the classification between converters and non-converters.

}, author = {Deborah Pareto and Pau Vidal and M. Alberich and Carlos Lopez and C. Auger and M. Tintor{\'e} and X. Montalban and J. Sastre-Garriga and Ver{\'o}nica Vilaplana and Alex Rovira} } @mastersthesis {xTubau, title = {Wav2Pix: Enhancement and Evaluation of a Speech-conditioned Image Generator}, year = {2019}, abstract = {

The interest for image synthesis has grown exponentially for the last years. Few years ago, it was invented a very powerful tool for this task: Generative Adversarial Networks (GANs). As its high performance in generating realistic images has been proved, nowadays many researchers are putting the focus on cross-modal learning.

Taking advantage of the huge amount of information we can extract from speech (such as identity, gender or emotional state), in this\  work\  we\  explore\  its\  potential\  to\  generate\  face\  images of a speaker by conditioning a GAN with his/her voice.\  We propose the enhancement and evaluation of a deep neural network that is trained from scratch in an end-to-end fashion, generating a face directly from the raw speech waveform without any additional identity information (e.g reference image or one-hot encoding).

This project focus on the enhancement of a previous model proposed by Francisco Roldan. As a result of a deep analysis on the former project strengths and weaknesses, we present a novel dataset collected for this work, with high-quality videos of ten youtubers with notable expressiveness in both the speech and visual signals. Besides, unlike in the preliminary project, four different techniques are proposed in order to assess the results.

}, author = {Tubau, Miquel}, editor = {Amanda Duarte and Xavier Gir{\'o}-i-Nieto} } @conference {cDuartea, title = {Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks}, booktitle = {ICASSP}, year = {2019}, month = {05/2019}, publisher = {IEEE}, organization = {IEEE}, address = {Brighton, UK}, abstract = {

Speech is a rich biometric signal that contains information\ about the identity, gender and emotional state of the speaker.\ In this work, we explore its potential to generate face images\ of a speaker by conditioning a Generative Adversarial\ Network (GAN) with raw speech input. We propose a deep\ neural network that is trained from scratch in an end-to-end\ fashion, generating a face directly from the raw speech waveform\ without any additional identity information (e.g reference\ image or one-hot encoding). Our model is trained in a\ self-supervised fashion by exploiting the audio and visual signals\ naturally aligned in videos. With the purpose of training\ from video data, we present a novel dataset collected for this\ work, with high-quality videos of ten youtubers with notable\ expressiveness in both the speech and visual signals.

}, doi = {10.1109/ICASSP.2019.8682970}, url = {http://hdl.handle.net/2117/167073}, author = {Amanda Duarte and Rold{\'a}n, Francisco and Tubau, Miquel and Escur, Janna and Pascual-deLaPuente, Santiago and Amaia Salvador and Mohedano, Eva and McGuinness, Kevin and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @inbook {bTochon18, title = {Advances in utilization of hierarchical representations in remote sensing data analysis}, booktitle = {Reference Module in Earth Systems and Environmental Sciences}, volume = {2}, year = {2018}, pages = {77-107}, publisher = {Elsevier}, organization = {Elsevier}, issn = {978-0-12-409548-9}, author = {G. Tochon and Dalla Mura, M. and Veganzones, M.A. and Valero, S. and Salembier, P. and Chanussot, J.} } @conference {cSuris, title = {Cross-modal Embeddings for Video and Audio Retrieval}, booktitle = {ECCV 2018 Women in Computer Vision Workshop}, year = {2018}, month = {09/2018}, publisher = {Springer}, organization = {Springer}, address = {Munich, Germany}, abstract = {

The increasing amount of online videos brings several opportunities for training self-supervised neural networks. The creation of large scale datasets of videos such as the YouTube-8M allows us to deal with this large amount of data in manageable way. In this work, we find new ways of exploiting this dataset by taking advantage of the multi-modal information it provides. By means of a neural network, we are able to create links between audio and visual documents, by projecting them into a common region of the feature space, obtaining joint audio-visual embeddings. These links are used to retrieve audio samples that fit well to a given silent video, and also to retrieve images that match a given a query audio. The results in terms of Recall@K obtained over a subset of YouTube-8M videos show the potential of this unsupervised approach for cross-modal feature learning. We train embeddings for both scales and assess their quality in a retrieval problem, formulated as using the feature extracted from one modality to retrieve the most similar videos based on the features computed in the other modality.

Cross Modal Embeddings for Video and Audio Retrieval $\#$WiCV18 from Universitat Polit{\`e}cnica de Catalunya
}, isbn = {978-3-030-11018-5}, doi = {10.1007/978-3-030-11018-5_62}, url = {https://doi.org/10.1007/978-3-030-11018-5_62}, author = {Sur{\'\i}s, D{\'\i}dac and Amanda Duarte and Amaia Salvador and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cCamposa, title = {Importance Weighted Evolution Strategies}, booktitle = {NeurIPS 2018 Deep Reinforcement Learning Workshop }, year = {2018}, month = {11/2018}, address = {Montreal, Quebec}, abstract = {

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

}, author = {V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @article {aCasamitjana, title = {MRI-Based Screening of Preclinical Alzheimer{\textquoteright}s Disease for Prevention Clinical Trials}, journal = {Journal of Alzheimer{\textquoteright}s Disease}, volume = {64}, year = {2018}, month = {07/2018}, chapter = {1099}, abstract = {

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

}, author = {Adri{\`a} Casamitjana and Paula Petrone and Alan Tucholka and Carles Falcon and Stavros Skouras and Jose Luis Molinuevo and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {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} } @inbook {bCampos, title = {Sentiment concept embedding for visual affect recognition}, booktitle = {Multimodal Behavior Analysis in theWild}, year = {2018}, publisher = {Elsevier}, organization = {Elsevier}, edition = {1}, chapter = {16}, abstract = {

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

}, url = {https://www.elsevier.com/books/multimodal-behavior-analysis-in-the-wild/alameda-pineda/978-0-12-814601-9}, author = {V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto and Jou, Brendan and Jordi Torres and Chang, Shih-Fu} } @conference {cCampos18, title = {Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks}, booktitle = {International Conference on Learning Representations (ICLR)}, year = {2018}, month = {01/2018}, abstract = {

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

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks from Xavier Giro-i-Nieto
}, author = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Jordi Torres and Chang, Shih-Fu} } @conference {cDuarte, title = {Towards Speech to Sign Language Translation}, booktitle = {ECCV 2018 Workshop on Shortcomings in Vision and Language}, year = {2018}, month = {08/2018}, abstract = {

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

}, author = {Amanda Duarte and Camli, Gorkem and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @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 {cLina, title = {Disentangling Motion, Foreground and Background Features in Videos}, booktitle = {CVPR 2017 Workshop Brave New Motion Representations}, year = {2017}, month = {05/2017}, abstract = {

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

Disentangle motion, Foreground and Background Features in Videos from Xavier Giro-i-Nieto
}, author = {Lin, Xunyu and V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto and Jordi Torres and Cristian Canton-Ferrer} } @conference {c, title = {Distributed training strategies for a computer vision deep learning algorithm on a distributed GPU cluster}, booktitle = {International Conference on Computational Science (ICCS)}, year = {2017}, month = {06/2017}, publisher = {Elsevier}, organization = {Elsevier}, address = {Zurich, Switzerland}, abstract = {

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

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

}, keywords = {distributed computing; parallel systems; deep learning; Convolutional Neural Networks}, doi = {https://doi.org/10.1016/j.procs.2017.05.074}, url = {http://www.sciencedirect.com/science/article/pii/S1877050917306129}, author = {V{\'\i}ctor Campos and Sastre, Francesc and Yag{\"u}es, Maurici and M{\'\i}riam Bellver and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @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 {cSalvadorc, title = {Learning Cross-modal Embeddings for Cooking Recipes and Food Images}, booktitle = {CVPR}, year = {2017}, month = {03/2017}, publisher = {CVF / IEEE}, organization = {CVF / IEEE}, address = {Honolulu, Hawaii, USA}, abstract = {

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

[Project page]

In the news:

}, doi = {10.1109/CVPR.2017.327}, url = {http://openaccess.thecvf.com/content_cvpr_2017/html/Salvador_Learning_Cross-Modal_Embeddings_CVPR_2017_paper.html}, author = {Amaia Salvador and Hynes, Nicholas and Aytar, Yusuf and Marin, Javier and Ofli, Ferda and Weber, Ingmar and Torralba, Antonio} } @article {aPetrone17, title = {Magnetic Resonance Imaging as a valuable tool for Alzheimer{\textquoteright}s disease screening}, journal = {Alzheimer{\textquoteright}s \& Dementia: The Journal of the Alzheimer{\textquoteright}s Association}, volume = {13}, year = {2017}, month = {07/2017}, pages = {P1245}, doi = {10.1016/j.jalz.2017.07.457}, url = {https://doi.org/10.1016/j.jalz.2017.07.457}, author = {P. Petrone and Ver{\'o}nica Vilaplana and Adri{\`a} Casamitjana and D. Sanchez-Escobedo and A. Tucholka and R. Cacciaglia and G. Operto and S. Skouras and C. Falcon and J.L. Molinuevo and J.D. Gispert} } @conference {cPetrone17, title = {Magnetic Resonance Imaging as a valuable tool for Alzheimer{\textquoteright}s disease screening}, booktitle = {Alzheimer{\textquoteright}s Association International Conference, London, 2017}, year = {2017}, month = {07/2017}, author = {P. Petrone and Ver{\'o}nica Vilaplana and Adri{\`a} Casamitjana and A. Tucholka and C. Falcon and R. Cacciaglia and G. Operto and S. Skouras and J.L. Molinuevo and J.D. Gispert} } @conference {cPana, title = {SalGAN: Visual Saliency Prediction with Generative Adversarial Networks}, booktitle = {CVPR 2017 Scene Understanding Workshop (SUNw)}, year = {2017}, address = {Honolulu, Hawaii, USA}, abstract = {

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

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

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

[CCGRID 2017] [Paper page]

}, url = {http://easychair.org/smart-program/CCGRID2017/2017-05-15.html$\#$session:13550}, author = {Jordi Torres and Sastre, Francesc and Yag{\"u}es, Maurici and V{\'\i}ctor Campos and Xavier Gir{\'o}-i-Nieto} } @conference {cCampos, title = {Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks}, booktitle = {NIPS Time Series Workshop 2017}, year = {2017}, month = {08/2017}, address = {Long Beach, CA, USA}, abstract = {

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

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks from Xavier Giro-i-Nieto
}, url = {https://imatge-upc.github.io/skiprnn-2017-telecombcn/}, author = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Jordi Torres and Chang, Shih-Fu} } @conference {xDuarte, title = {Temporal-aware Cross-modal Embeddings for Video and Audio Retrieval}, 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 = {

The increasing amount of videos online brings several opportunities for training self-supervised neural networks. In this work, we explore cross-modal embeddings between audio and vision by exploiting their alignment on YouTube videos.

Joint audio-visual embeddings allow creating links between audio and visual documents by projecting them to a common region of the feature space. They can be applied to enriching radio broadcasts with images, finding soundtracks for user-generated videos or simply enriching a topic search with both audio and video documents.

The idea of creating a joint embedding space across modalities has being exploited by other areas [3, 4]. However, joint representation between the video frames and its audio have yet to be fully exploited. A similar approach to the proposed one was [2], where a soundtrack was retrieved to match a music video. However, this work did not target a synchronization between both modalities.

We aim at training a temporal-aware embedding which can align both audio and visual tracks. We use the visual and audio features provided in the YouTube-8M dataset [1]. The dataset includes features at both the clip and frame (temporal window) level. We train embeddings for both scales and assess their quality in a retrieval problem, formulated as using the feature extracted from one modality to retrieve the most similar videos based on the features computed in the other modality.

We aim at not only finding related documents, but synchronize both sequences. The alignment between the two sequences will rely on computing temporal-aware features with recurrent neural networks at different scales. At retrieval time, different scales will be assessed and results evaluated both with ranking metrics and Amazon Mechanical Turk.

References

[1] Sami Abu-El-Haija, Nisarg Kothari, Joonseok Lee, Paul Natsev, George Toderici, Balakrishnan Varadarajan, and Sudheendra Vijayanarasimhan. Youtube-8m: A large-scale video classification benchmark. arXiv preprint arXiv:1609.08675, 2016.

[2] Sungeun Hong, Woobin Im, and Hyun S Yang. Deep learning for content-based, cross-modal retrieval of videos and music. arXiv preprint arXiv:1704.06761, 2017.

[3] Amaia Salvador, Nicholas Hynes, Yusuf Aytar, Javier Marin, Ferda Ofli, Ingmar Weber, and Antonio Torralba. Learning cross-modal embeddings for cooking recipes and food images. In CVPR, 2017.

[4] Liwei Wang, Yin Li, and Svetlana Lazebnik. Learning deep structure-preserving image-text embeddings. In CVPR, 2016.

}, author = {Amanda Duarte and Sur{\'\i}s, D{\'\i}dac and Amaia Salvador and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cPoignant16, title = {The CAMOMILE Collaborative Annotation Platform for Multi-modal, Multi-lingual and Multi-media Documents}, booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)}, year = {2016}, month = {05/2016}, address = {Portoro{\v z} (Slovenia)}, abstract = {

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

}, keywords = {active learning, Annotation tool, collaborative annotation, multimedia, person annotation}, isbn = {978-2-9517408-9-1}, url = {http://www.lrec-conf.org/proceedings/lrec2016/pdf/456_Paper.pdf}, author = {Johann Poignant and Mateusz Budnik and Herv{\'e} Bredin and Claude Barras and Mickael Stefas and Pierrick Bruneau and Gilles Adda and Laurent Besacier and Hazim Ekenel and Gil Francopoulo and Javier Hernando and Joseph Mariani and Morros, J.R. and Georges Qu{\'e}not and Sophie Rosset and Thomas Tamisier} } @conference {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} } @mastersthesis {xFerri16, title = {Object Tracking in Video with TensorFlow}, year = {2016}, abstract = {

[Project repo]

[Additional repo for setting up the environment]

}, author = {Ferri, Andrea}, editor = {Xavier Gir{\'o}-i-Nieto and Jordi Torres and Amaia Salvador} } @conference {cTacchini, title = {Do you have a Pop face? Here is a Pop song. Using profile pictures to mitigate the cold-start problem in Music Recommender Systems}, booktitle = {9th ACM Conference on Recommender Systems}, year = {2015}, month = {09/2015}, author = {Tacchini, E. and Morros, J.R. and Ver{\'o}nica Vilaplana and Sa{\~n}oso, E.} } @conference {cNguyen, title = {NII-HITACHI-UIT at TRECVID 2015 Instance Search}, booktitle = {TRECVID 2015 Workshop}, year = {2015}, month = {11/2015}, publisher = {NIST}, organization = {NIST}, address = {Gaithersburg, MD, USA}, abstract = {

In this paper, we propose two methods to improve last year instance search framework. Both of them are based on post processing scheme that try to rerank top K shots returned from BOW model. The rst system is to propose a query-adaptive weighting technique between DPM object detectors score and BOW{\textquoteright}s score. In order to nd a good weight, we use a neural network which learns characteristics of the query including number of features, number of shared words and area of the query topic. The second system combines two state-of-the-art object detectors: DPM and Fast RCNN to estimate object location and similarity score, respectively. The nal score is computed using these components together with BOW based similarity score returned from the baseline system. The experimental results show that our system improved pretty much even with a smaller number of top K input ranked list. Compared to other teams, we got the second place with the same run.

}, url = {http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.15.org.html}, author = {Nguyen, Vinh-Tiep and Duy-Dinh-Le and Amaia Salvador and Caizhi-Zhu and Nguyen, Dinh-Luan and Tran, Minh-Triet and Ngo Duc, Thanh and Anh Duong, Duc and Satoh, Shin{\textquoteright}ichi and Xavier Gir{\'o}-i-Nieto} } @article {aTochon15, title = {On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images}, journal = {Remote Sensing of Environment}, volume = {159}, year = {2015}, month = {03/2015}, pages = {318-331}, abstract = {

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

}, issn = {0034-4257}, author = {G. Tochon and J.B. F{\'e}eret and Valero, S. and R.E. Martin and D.E. Knapp and Salembier, P. and Chanussot, J. and G. Asner} } @conference {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} } @mastersthesis {xTella, title = {Contextless Object Recognition with Shape-enriched SIFT and Bags of Features}, year = {2014}, abstract = {
Final thesis slides (1) from Xavi Gir{\'o}

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

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

Currently, there are highly competitive results in the field of object recognition based on the aggregation of point-based features. The aggregation process, typically with an average or max-pooling of the features generates a single vector that represents the image or region that contains the object.

The aggregated point-based features typically describe the texture around the points with descriptors such as SIFT. These descriptors present limitations for wired and textureless objects. A possible solution is the addition of shape-based information. Shape descriptors have been previously used to encode shape information and thus, recognise those types of objects. But generally an alignment step is required in order to match every point from one shape to other ones. The computational cost of the similarity assessment is high.

We purpose to enrich location and texture-based features with shape-based ones. Two main architectures are explored: On the one side, to enrich the SIFT descriptors with shape information before they are aggregated. On the other side, to create the standard Bag of Words histogram and concatenate a shape histogram, classifying them as a single vector.

We evaluate the proposed techniques and the novel features on the Caltech-101 dataset.

Results show that shape features increase the final performance. Our extension of the Bag of Words with a shape-based histogram(BoW+S) results in better performance. However, for a high number of shape features, BoW+S and enriched SIFT architectures tend to converge.

Final grade: A with honors (10/10)

}, keywords = {Bag of Words, Interest Points, object candidates, Object detection, segmentation, shape coding, SIFT, textureless objects, wired objects.}, url = {http://hdl.handle.net/2099.1/22390}, author = {Tella, Marcel}, editor = {Zeppelzauer, Matthias and Xavier Gir{\'o}-i-Nieto} } @conference {cPerez-Pellitero14, title = {Fast Super-Resolution via Dense Local Training and Inverse Regressor Search}, booktitle = {Asian Conference in Computer Vision (ACCV)}, year = {2014}, month = {11/2014}, address = {Singapore}, abstract = {

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

}, author = {E. Perez-Pellitero and Salvador, J. and Torres-Xirau, I. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @book {bSchreer14, title = {Media Production, Delivery and Interaction for Platform Independent Systems}, volume = {ISBN 978-1-118-60533-2}, year = {2014}, publisher = {Wiley, ISBN 978-1-118-60533-2}, organization = {Wiley, ISBN 978-1-118-60533-2}, issn = {978-1-118-60533-2}, url = {http://eu.wiley.com/WileyCDA/WileyTitle/productCd-1118605330,subjectCd-EEJ0.html}, author = {Schreer, O. and Macq, J. and Niamut, O. and Ruiz-Hidalgo, J. and Shirley, B. and Thallinger, G. and Thomas, G.} } @article {aTochon13, title = {Segmentation hyperspectrales de forets tropicales par arbres de partition binaires}, journal = {Revue fran{\c c}aise de photogramm{\'e}trie et de t{\'e}l{\'e}d{\'e}tection}, volume = {202}, year = {2013}, month = {May 2013}, pages = {55-65}, author = {G. Tochon and J.B. Feret and Valero, S. and R.E. Martin and R. Tupayachi and Chanussot, J. and Salembier, P. and G. Asner} } @conference {cVentura13a, title = {UPC at MediaEval 2013 Hyperlinking Task}, booktitle = {MediaEval 2013 Workshop}, year = {2013}, month = {10/2013}, publisher = {CEUR Workshop Proceedings Vol-1043}, organization = {CEUR Workshop Proceedings Vol-1043}, address = {Barcelona, Catalonia}, abstract = {

These working notes paper present the contribution of the UPC team to the Hyperlinking sub-task of the Search and Hyperlinking Task in MediaEval 2013. Our contribution explores the potential of a solution based only on visual cues.

In particular, every automatically generated shot is represented by a keyframe. The linking between video segments is based on the visual similarity of the keyframes they contain. Visual similarity is assessed with the intersection of bag of features histograms generated with the SURF descriptor.

Xavier Gir{\'o}, "UPC at MediaEval Hyperlinking 2013" from Xavi Gir{\'o}

Presentation on MediaEval Search \& Linking task 2013 from Netherlands Institute for Sound and Vision, University of Twente
}, keywords = {Benchmark, Hyperlinking, Video Collections}, url = {http://ceur-ws.org/Vol-1043/mediaeval2013_submission_35.pdf}, author = {Ventura, C. and Tella, Marcel and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xTort13, title = {Video Clustering Using Camera Motion}, year = {2013}, abstract = {

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

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

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

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

}, url = {http://hdl.handle.net/2099.1/17337}, author = {Tort, Laura}, editor = {Xavier Gir{\'o}-i-Nieto and Rombaut, Mich{\`e}le and Pellerin, Denis} } @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.} } @article {aTello11, title = {Edge enhancement algorithm based on the wavelet transform for automatic edge detection in SAR images}, journal = {IEEE transactions on geoscience and remote sensing}, volume = {49}, number = {1}, year = {2011}, pages = {222{\textendash}235}, issn = {0196-2892}, doi = {10.1109/TGRS.2010.2052814}, url = {http://hdl.handle.net/2117/11057}, author = {Tello, M. and L{\'o}pez-Mart{\'\i}nez, C. and Mallorqui, J.J. and Salembier, P.} } @conference {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.} } @mastersthesis {xTella11, title = {Interactive Image Processing demonstrations for the web}, year = {2011}, abstract = {

This diploma thesis aims to provide a framework for developing web applications for ImagePlus, the software develpment platform in C++ of the Image Processing Group of the Technical University of Catalonia (UPC). These web applications are to demonstrate the functionality of the image processing algorithms to any visitor to the group website. Developers are also benefited from this graphical user interface because they can easily create Graphical User Interfaces (GUIs) for the processing algorithms

Interactive Image Processing Demos for the Web from Xavi Gir{\'o}
}, keywords = {c++, demo, image processing, web}, url = {http://hdl.handle.net/2099.1/13540}, author = {Tella, Marcel}, editor = {Xavier Gir{\'o}-i-Nieto} } @inbook {bWaibel10, title = {Computers in the Human Interaction Loop}, booktitle = {Handbook on Ambient Intelligence and Smart Environments (AISE)}, year = {2010}, pages = {1071{\textendash}1116}, publisher = {Springer}, organization = {Springer}, address = {Boston, MA}, abstract = {

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

}, isbn = {978-0-387-93807-3}, doi = {10.1007/978-0-387-93808-0_40}, author = {Waibel, A. and Stiefelhagen, R. and Carlson, R. and Casas, J. and Kleindienst, J. and Lamel, L. and Lanz, O. and Mostefa, D. and Omologo, M. and Pianesi, F. and Polymenakos, L. and Potamianos, G. and Soldatos, J. and Sutschet, G. and Terken, J.} } @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.} } @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.} } @article {aTurkan08, title = {Edge Projections for Eye Localization}, journal = {Optical engineering}, volume = {47}, number = {4}, year = {2008}, pages = {1{\textendash}6}, issn = {0091-3286}, doi = {10.1117/1.2902437}, url = {http://spiedigitallibrary.org/oe/resource/1/opegar/v47/i4/p047007_s1}, author = {Turkan, M. and M. Pard{\`a}s and Cetin, E.} } @inbook {bVilaplana08, title = {Face and speech interaction}, booktitle = {Multimodal user interfaces: from signals to interaction}, year = {2008}, pages = {85{\textendash}118}, isbn = {978-3-540-78344-2}, author = {Ver{\'o}nica Vilaplana and Marqu{\'e}s, F. and Gurban, M. and Thiran, J.} } @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} } @conference {cHaro08, title = {On the non-uniform complexity of brain connectivity}, booktitle = {5th IEEE International Symposium on Biomedical Imaging (ISBI 2008)}, year = {2008}, month = {05/2008}, publisher = {IEEE}, organization = {IEEE}, address = {Paris}, abstract = {

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

}, keywords = {biodiffusion, biological tissues, biology computing, brain, brain connectivity, cellular biophysics, cerebral white matter, Clustering methods, complex microstructure, density, Density measurement, Diffusion tensor imaging, Geometry, high-angular resolution diffusion imaging, high-dimensional spaces, High-resolution imaging, Image analysis, Image resolution, Magnetic resonance imaging, manifold learning approach, Microstructure, molecular biophysics, nonuniform complexity, Point processes, Poisson processes, Signal resolution, stratification learning, Switches, Tensile stress, Unsupervised learning}, doi = {10.1109/ISBI.2008.4541139}, author = {Haro, G. and Lenglet, C. and Sapiro, Guillermo and Thompson, P.} } @article {aMostefa07, title = {The CHIL Audiovisual Corpus for Lecture and Meeting Analysis inside Smart Rooms}, journal = {Language resources and evaluation}, volume = {41}, number = {3}, year = {2007}, month = {01/2008}, pages = {389{\textendash}407}, abstract = {

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

}, issn = {1574-020X}, doi = {10.1007/s10579-007-9054-4}, author = {Mostefa, D. and Moreau, N. and Choukri, K. and Potamianos, G. and Chu, S. and Tyagi, A. and Casas, J. and Turmo, J. and Cristoforetti, L. and Tobia, F. and Pnevmatikakis, A. and Mylonakis, V. and Talantzis, F. and Burger, S. and Stiefelhagen, R. and Bernardin, K. and Rochet, C.} } @conference {cPetras07, title = {Flexible test-bed for unusual behavior detection}, booktitle = {6th ACM International Conference on Image and Video Retrieval}, year = {2007}, pages = {105{\textendash}108}, isbn = {0 646 34170 7}, author = {Petras, I. and Beleznai, C. and Dedeoglu, Y. and M. Pard{\`a}s and Kov{\'a}cs, L. and Szl{\'a}vik, Z. and Havasi, L. and Szir{\'a}nyi, T. and T{\"o}reyin, B. and G{\"u}d{\"u}kbay, U. and Cetin, E. and Cristian Canton-Ferrer} } @conference {cTurkan07, title = {Human eye localization using edge projections}, booktitle = {International Conference on Computer Vision Theory and Applications, VISAPP 2007}, year = {2007}, pages = {410{\textendash}415}, isbn = {-}, author = {Turkan, M. and M. Pard{\`a}s and Cetin, E.} } @conference {cCanton-Ferrer06, title = {3D Human Action Recognition In Multiple View Scenarios}, booktitle = {2ones Jornades UPC de Investigaci{\'o}n en Autom{\'a}tica, Visi{\'o}n y Rob{\'o}tica}, year = {2006}, pages = {1{\textendash}5}, isbn = {84-7653-885-5}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s and Sargin, M. and Tekalp, M.} } @inbook {bCanton-Ferrer06, title = {Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding}, booktitle = {Machine Learning for Multimodal Interaction}, volume = {3869}, year = {2006}, pages = {250{\textendash}261}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-540-32549-9}, doi = {10.1007/11677482_22}, author = {Cristian Canton-Ferrer and Casas, J. and Tekalp, M. and M. Pard{\`a}s} } @conference {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 {cSayrol05, title = {Development of a platform offering video copyright protection and security against illegal distribution}, booktitle = {Security, Steganography, and Watermarking of Multimedia Contents}, year = {2005}, pages = {76{\textendash}83}, isbn = {9963-607-06-3}, author = {Elisa Sayrol and Soriano, M. and Fernandez, M. and Casanelles, J. and Tom{\`a}s, J.} } @conference {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} } @inbook {bSoriano05, title = {Multimedia Copyright Protection Platform Demonstrator}, booktitle = {Lecture notes in computer science}, volume = {3477}, year = {2005}, pages = {76{\textendash}83}, issn = {0302-9743}, url = {http://www.springerlink.com/(n0yw1g55rs45pg55g5zcinrm)/app/home/contribution.asp?referrer=parent\&backto=issue,32,34;journal,197,2212;linkingpublicationresults,1:105633,1}, author = {Soriano, M. and Fernandez, M. and Elisa Sayrol and Tom{\`a}s, J. and Casanelles, J. and Pegueroles, J. and Juan Hern{\'a}ndez Serrano} } @conference {cCanton-Ferrer05b, title = {Projective Kalman Filter: Multiocular Tracking of 3D Locations Towards Scene Understanding}, booktitle = {2nd Joint Workshop on Multimodal Interaction and Related Machine Learning Algorithms}, year = {2005}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and Tekalp, M. and M. Pard{\`a}s} } @book {eJofre04, title = {El {\textquoteright}Profesional Innovador{\textquoteright} y la red}, year = {2004}, url = {http://catedratelefonica.upc.edu/documents/llibres/docs/jornada_2004_catedra_telf_upc.pdf}, author = {Jofre, L. and Sole, F. and Sabate, F. and Marqu{\'e}s, F. and Romeu, J. and Jordi Torres} } @conference {cJofre04, title = {Una Enginyeria per a la Societat del Coneixement}, booktitle = {II Congr{\'e}s d{\textquoteright}Enginyeria en Llengua Catalana}, year = {2004}, url = {http://www.eicc.cat/celc/formacio.htm}, author = {Jofre, L. and Sole, F. and Sabate, F. and Marqu{\'e}s, F. and Romeu, J. and Jordi Torres} } @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 {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 {cTorres99, title = {Face location and recognition for video indexing in the Hypermedia project}, booktitle = {European Conference on Multimedia Applications, Services and Techniques}, year = {1999}, pages = {364{\textendash}377}, author = {Torres, L. and Marqu{\'e}s, F. and Lorente, L. and Ver{\'o}nica Vilaplana} } @inbook {aTorres99, title = {Face Location and Recognition for Video Indexing in the Hypermedia Project}, booktitle = {Lecture Notes in Computer Science}, volume = {1629}, year = {1999}, issn = {364-377}, doi = {DOI: 10.1007/3-540-48757-3_25}, author = {Torres, L. and Marqu{\'e}s, F. and Lorente, L. and Ver{\'o}nica Vilaplana} } @inbook {bTorres98, title = {Codificaci{\'o}n de imagen}, booktitle = {Reconocimiento de formas y an{\'a}lisis de im{\'a}genes}, year = {1998}, pages = {101{\textendash}135}, publisher = {AERFAI}, organization = {AERFAI}, edition = {A. Sanfeliu (Ed.)}, isbn = {84-922529-4-4}, author = {Torres, L. and Salembier, P.} } @article {aCasas97, title = {A region-based subband coding scheme}, journal = {Signal Processing: Image Communication}, volume = {10}, number = {1-2}, year = {1997}, month = {10/1997}, pages = {173{\textendash}200}, abstract = {

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

}, keywords = {SCHEMA}, issn = {0923-5965}, doi = {10.1016/S0923-5965(97)00024-6}, author = {Casas, J. and Torres, L.} } @article {aTorres97, title = {Stochastic vector quantization of images}, journal = {Signal Processing}, volume = {62}, number = {3}, year = {1997}, month = {11/1997}, pages = {291{\textendash}301}, abstract = {

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

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

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

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

}, url = {http://hdl.handle.net/10803/6920}, author = {Casas, J.}, editor = {Torres, L.} } @conference {cTorres96, title = {Prediction error image coding using a modified stochastic vector quantization scheme}, booktitle = {IEEE International Conference on Image Processing}, year = {1996}, pages = {451{\textendash}454}, author = {Torres, L. and Casas, J.} } @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 {cCasas96a, title = {Strong edge features for image coding}, booktitle = {International Symposium on Mathematical Morphology and its Applications to Image and Signal Processing III}, year = {1996}, pages = {443{\textendash}450}, isbn = {0-7923-9733-9}, author = {Casas, J. and Torres, L.} } @conference {cTorres95a, title = {An efficient technique of texture representation in segmentation-based image coding schemes}, booktitle = {IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING.}, year = {1995}, pages = {588{\textendash}591}, author = {Torres, L. and Casas, J. and Campins, J.} } @conference {cCasas95, title = {Morphological interpolation for texture coding}, booktitle = {IEEE International Conference on Image Processing, ICIP 1995}, year = {1995}, pages = {903{\textendash}907}, address = {Washington DC, USA}, author = {Casas, J. and Salembier, P. and Torres, L.} } @conference {cTorres95, title = {New approaches to texture coding in segmentation and feature-based image coding schemes}, booktitle = {International Conference on Digital Signal Processing}, year = {1995}, pages = {12{\textendash}17}, isbn = {84-600-7766-7}, author = {Torres, L. and Casas, J.} } @article {aSalembier95, title = {Region-based video coding using mathematical morphology}, journal = {Proceedings of the IEEE}, volume = {83}, number = {6}, year = {1995}, pages = {843{\textendash}857}, issn = {0018-9219}, author = {Salembier, P. and Torres, L. and Meyer, F. and Gu, C.} } @article {aCasas94, title = {Coding of details in very low bit-rate video systems}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {4}, number = {3}, year = {1994}, month = {06/1994}, pages = {317{\textendash}327}, abstract = {

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

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

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

}, author = {Casas, J. and Torres, L.} } @conference {cMarques94c, title = {Interleaved segmentation and motion estimation by means of morphological tools}, booktitle = {Workshop on Image Analysis and Synthesis in Image Coding}, year = {1994}, address = {Berlin, Germany}, author = {Marqu{\'e}s, F. and Bouchard, L. and Corset, I. and Jeannin, S. and Morros, J.R. and M. Pard{\`a}s and Salembier, P. and Torres, L.} } @conference {cCasas94b, title = {Morphological filter for lossless image subsampling}, booktitle = {IEEE International Conference on Image Processing{\textquoteright}94}, year = {1994}, pages = {903{\textendash}907}, isbn = {0-8186-6950-0}, author = {Casas, J. and Torres, L.} } @conference {cGimeno94, title = {A new approach to texture coding using stochastic vector quantization}, booktitle = {IEEE International Conference on Image Processing{\textquoteright}94}, year = {1994}, pages = {119{\textendash}123}, abstract = {

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

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

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

}, isbn = {84-600-7766-7}, doi = {10.1109/ICASSP.1994.389380}, author = {Torres, L. and Casas, J. and Diego, D.} } @conference {cPardas93, title = {3D morphological segmentation or image sequence processing}, booktitle = {IEEE Winter Workshop on Nonlinear Signal Processing}, year = {1993}, pages = {31{\textendash}36}, address = {Tampere, Finland}, isbn = {960-7620-06-4}, author = {M. Pard{\`a}s and Salembier, P. and Torres, L.} } @conference {cCasas93a, title = {Efficient coding of residual images}, booktitle = {SPIE Visual Communications {\textquoteright}93}, year = {1993}, month = {10/1993}, pages = {694{\textendash}705}, publisher = {SPIE}, organization = {SPIE}, address = {Cambridge, MA}, abstract = {

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

}, doi = {10.1117/12.157987}, author = {Casas, J. and Torres, L. and Jare{\~n}o, M.} } @conference {cSalembier93f, title = {Morphological segmentation-based coding of image sequences}, booktitle = {IEEE European Conference on Circuits Theory and Design}, year = {1993}, pages = {1245{\textendash}1250}, address = {Davos, Switzerland}, isbn = {0-7803-5470-2}, url = {Problemes de connexi{\'o} a l{\textquoteright}Eprints}, author = {Salembier, P. and Torres, L. and M. Pard{\`a}s and Marqu{\'e}s, F. and HIERRO, P. and Gasull, A.} } @book {eTorres93, title = {Sistemas Anal{\'o}gicos y Digitales de Televisi{\'o}n}, year = {1993}, publisher = {Edicions UPC}, organization = {Edicions UPC}, address = {Barcelona}, isbn = {84-7653-288-1}, author = {Torres, L. and Lleida, E. and Casas, J.} } @inbook {bGasull92, title = {Analysis and optimization of the K-Means algorithm for remote sensing applications}, booktitle = {Pattern recognition and image analysis}, year = {1992}, pages = {0{\textendash}0}, isbn = {9810208812}, url = {http://cataleg.upc.edu/search~S1*cat/?searchtype=i\&searcharg=+9810208812\&searchscope=1\&SORT=D\&extended=0\&SUBMIT=Cerca\&searchlimits=\&searchorigarg=t+Proceedings+of++Workshop+on+Network+Robot+Systems+2009}, author = {Gasull, A. and Monte, E. and Torres, L. and Montolio, P. and Marqu{\'e}s, F.} } @article {aTorres92, title = {Compresi{\'o}n de audio e imagen para sistemas multimedia}, journal = {Bit numerical mathematics}, number = {76}, year = {1992}, pages = {50{\textendash}58}, issn = {0006-3835}, author = {Torres, L. and Moreno, A. and Masgrau, E. and Gasull, A.} } @conference {cPardas92, title = {Connectivity filters for image sequences}, booktitle = {IMAGE ALGEBRA AND MORPHOLOGICAL IMAGE PROCESSING. SPIE}, year = {1992}, pages = {318{\textendash}329}, isbn = {0 7923 5133 9}, author = {M. Pard{\`a}s and Torres, L.} } @conference {cCasas92a, title = {Fuzzy classification of Remote Sensing images: a pseudocolor representation of fuzzy partitions}, booktitle = {SPIE Neural and Stochastic Methods in Image and Signal Processing}, year = {1992}, month = {07/1992}, publisher = {SPIE}, organization = {SPIE}, address = {San Diego, CA}, doi = {10.1117/12.130844}, author = {Casas, J. and Hillion, A. and Roux, C. and Torres, L. and Gasull, A.} } @conference {cGasull92, title = {Non-linear techniques for image interpolation}, booktitle = {VI European Signal Processing Conference}, year = {1992}, pages = {1473{\textendash}1476}, isbn = {0 444 89587 6}, author = {Gasull, A. and Marqu{\'e}s, F. and Torres, L.} } @conference {cGasull92a, title = {T{\'e}cnicas de preprocesado para la segmentaci{\'o}n de im{\'a}genes}, booktitle = {U.R.S.I. 92}, year = {1992}, pages = {367{\textendash}371}, isbn = {84-600-8219-9}, author = {Gasull, A. and Marqu{\'e}s, F. and Montolio, P. and Torres, L.} } @conference {cGasull92b, title = {T{\'e}cnicas de preprocesado para la segmentaci{\'o}n de im{\'a}genes}, booktitle = {VII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1992}, pages = {367{\textendash}371}, author = {Gasull, A. and Marqu{\'e}s, F. and Torres, L.} } @conference {cCasas92, title = {Una interpretaci{\'o}n colorim{\'e}trica en clasificaciones fuzzy de im{\'a}genes de teledetecci{\'o}n}, booktitle = {V Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1992}, pages = {281{\textendash}287}, author = {Casas, J. and Torres, L. and Gasull, A.} } @conference {cGasull91, title = {Analisis de no estacionariedades en la interpolacion de imagenes}, booktitle = {VI Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1991}, pages = {985{\textendash}989}, isbn = {84-600-7766-7}, author = {Gasull, A. and Marqu{\'e}s, F. and Torres, L.} } @conference {cCasas91, title = {Dise{\~n}o de filtros de imagen con funciones de transferencia}, booktitle = {VI Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1991}, pages = {965{\textendash}969}, isbn = {84-600-7766-7}, author = {Casas, J. and Torres, L.} } @conference {cMarques91b, title = {Obtencion de un esqueleto morfologico sin puntos redundantes}, booktitle = {VI Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1991}, pages = {980{\textendash}984}, author = {Marqu{\'e}s, F. and Gasull, A. and Torres, L.} } @conference {cGasull90a, title = {Analisis y optimizacion del algoritmo k-means aplicado a teledeteccion en ima}, booktitle = {IV Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1990}, pages = {17{\textendash}23}, author = {Gasull, A. and Torres, L.} } @conference {cGasull90, title = {Contour extraction and image preprocessing of echocardiographic images using r}, booktitle = {Latvian Signal Processing International Conference}, year = {1990}, pages = {26{\textendash}30}, author = {Gasull, A. and Torres, L.} } @conference {cGasull90b, title = {Eleccion de componentes principales para la clasific. no supervisada de image}, booktitle = {IV Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1990}, pages = {9{\textendash}16}, author = {Gasull, A. and Torres, L.} } @conference {cTorres90, title = {Temporal automatic edge detection of echocardiographic images}, booktitle = {ICASSP}, year = {1990}, pages = {2149{\textendash}2152}, isbn = {078030033}, author = {Torres, L. and Gasull, A.} } @conference {cGasull89a, title = {Automated left ventricular contour extraction and volume calculation form echocardiographic images}, booktitle = {Ultrasonics International 89}, year = {1989}, pages = {1{\textendash}4}, isbn = {SN.}, author = {Gasull, A. and Alquezar, R. and Torres, L. and Sallent, S. and Marqu{\'e}s, F. and Vidal, J. and Sangr{\'a}, E.} } @conference {cSallent88, title = {Codificaci{\'o}n piramidal generalizada}, booktitle = {III Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1988}, pages = {1{\textendash}2}, isbn = {142440469X/1520-6149}, author = {Sallent, S. and Torres, L. and Gasull, A.} } @conference {cGasull88, title = {Detecci{\'o}n autom{\'a}tica de contornos en im{\'a}genes ecogr{\'a}ficas}, booktitle = {III Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1988}, pages = {1{\textendash}4}, isbn = {?}, author = {Gasull, A. and Marqu{\'e}s, F. and Sallent, S. and Torres, L. and Vidal, J.} }