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

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

}, author = {Bonet, David and Mas-Montserrat, Daniel and Xavier Gir{\'o}-i-Nieto and Ioannidis, Alexander G.} } @mastersthesis {xDomenech, title = {Hiding Images in their Spoken Narratives}, year = {2022}, abstract = {

Steganography is the technique of hiding secret data within an ordinary, non-secret, file or message in order to avoid its detection. Throughout our work, we study the case where the hidden secret data is an image and the non-secret data or cover signal is an audio. To this end, we use a recently proposed residual architecture operating on top of short-time discrete cosine transform (STDCT) audio spectrograms. In our work, we evaluate the above mentioned residual steganography architecture with the Localized Narratives dataset, explore the feasibility of using short-time fourier transform (STFT) audio spectrograms instead of STDCTs to improve the efficiency of the system, investigate the use of hidden signals permuted with the objective to spread the audio corruption of the revealed images, apply averaged audio windows to improve quality results and tested the system in real-world distortions.

}, author = {Teresa Domenech}, editor = {McGuinness, Kevin and Pons, Jordi and Xavier Gir{\'o}-i-Nieto} } @conference {cSchurholt22, title = {Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights}, booktitle = {NeurIPS 2022 - Neural Information Processing Systems}, year = {2022}, abstract = {

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

}, author = {Sch{\"u}rholt, Konstantin and Knyazev, Boris and Xavier Gir{\'o}-i-Nieto and Borth, Damian} } @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} } @conference {cOriol, title = {Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation}, booktitle = {NeurIPS 2019 Workshop on AI for Social Good}, year = {2019}, month = {09/2019}, address = {Vancouver, Canada}, abstract = {

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

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

\>
}, author = {Oriol, Benet and Cristian Canton-Ferrer and Xavier Gir{\'o}-i-Nieto} } @conference {cRamon, title = {Hyperparameter-Free Losses for Model-Based Monocular Reconstruction}, booktitle = {ICCV 2019 Workshop on Geometry Meets Deep Learning}, year = {2019}, month = {11/2019}, publisher = {IEEE / Computer Vision Foundation}, organization = {IEEE / Computer Vision Foundation}, address = {Seoul, South Corea}, abstract = {

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

}, author = {Ramon, Eduard and Ruiz, Guillermo and Batard, Thomas and Xavier Gir{\'o}-i-Nieto} } @conference {cSanchez-Escobedo, title = {HybridNet for Depth Estimation and Semantic Segmentation}, booktitle = {ICASSP 2018}, year = {2018}, month = {04/2018}, publisher = {IEEE}, organization = {IEEE}, address = {Calgary, Alberta, Canada}, abstract = {

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

}, doi = {10.1109/ICASSP.2018.8462433}, url = {https://ieeexplore.ieee.org/abstract/document/8462433}, author = {D. Sanchez-Escobedo and X. Lin and Casas, J. and M. Pard{\`a}s} } @inbook {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} } @article {aFrias-Velazquez, title = {Hierarchical stack filtering: a bitplane-based algorithm for massively parallel processors}, journal = {Journal of Real-Time Image Processing}, year = {2017}, month = {03/2017}, abstract = {

Full version available at http://rdcu.be/p6w1

With the development of novel parallel architectures for image processing, the implementation of well-known image operators needs to be reformulated to take advantage of the so-called massive parallelism. In this work, we propose a general algorithm that implements a large class of nonlinear filters, called stack filters, with a 2D-array processor. The proposed method consists of decomposing an image into bitplanes with the bitwise decomposition, and then process every bitplane hierarchically. The filtered image is reconstructed by simply stacking the filtered bitplanes according to their order of significance. Owing to its hierarchical structure, our algorithm allows us to trade-off between image quality and processing time, and to significantly reduce the computation time of low-entropy images. Also, experimental tests show that the processing time of our method is substantially lower than that of classical methods when using large structuring elements. All these features are of interest to a variety of real-time applications based on morphological operations such as video segmentation and video enhancement.\ 

}, keywords = {Array processors, Bitwise decomposition, Morphological operators, Smart camera, Stack filters}, url = {http://rdcu.be/p6w1}, author = {Frias-Velazquez, A. and Morros, J.R. and Garc{\'\i}a, M. and Philips, Wilfried} } @conference {cPerez-Pellitero16, title = {Half Hypersphere Confinement for Piecewise Linear Regression}, booktitle = {IEEE Winter Conference on Applications of Computer Vision}, year = {2016}, month = {03/2016}, address = {Lake Placid, NY, USA}, abstract = {

2016 WACV\  \  \ 

IIn this paper we study the characteristics of the metrics best suited for the piecewise regression algorithms, in which comparisons are usually made between normalized vectors that lie on the unitary hypersphere. Even though Euclidean distance has been widely used for this purpose, it is suboptimal since it does not handle antipodal points (i.e. diametrically opposite points) properly. Therefore, we propose the usage of antipodally invariant metrics and introduce the Half Hypersphere Confinement (HHC), a fast alternative to Multidimensional Scaling (MDS) that allows to map antipodally invariant distances in the Euclidean space with very little approximation error. The performance of our method, which we named HHC Regression (HHCR), applied to Super-Resolution (SR) improves both in quality (PSNR) and it is faster than any other state-of-the-art method. Additionally, under an application-agnostic interpretation of our regression framework, we also test our algorithm for denoising and depth upscaling with promising results.

}, doi = {10.1109/WACV.2016.7477651}, author = {E. Perez-Pellitero and Salvador, J. and Ruiz-Hidalgo, J. and Rosenhahn, B.} } @unpublished {xFernandeza, title = {Is a {\textquotedblleft}happy dog{\textquotedblright} more {\textquotedblleft}happy{\textquotedblright} than {\textquotedblleft}dog{\textquotedblright}? - Adjective and Noun Contributions for Adjective-Noun Pair prediction}, journal = {NIPS Women in Machine Learning Workshop}, year = {2016}, month = {12/2016}, address = {Barcelona}, abstract = {

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

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

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

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

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

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

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

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

}, author = {Fern{\`a}ndez, D{\`e}lia and V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Chang, Shih-Fu} } @conference {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} } @phdthesis {dBosio14, title = {Hierarchical information representation and efficient classification of gene expression microarray data}, volume = {PhD Thesis}, year = {2014}, month = {06/2014}, pages = {150}, school = {Universitat Politecnica de Catalunya}, address = {Barcelona}, abstract = {

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

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

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

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

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

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

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

}, url = {http://hdl.handle.net/10803/145902}, author = {Bosio, M.}, editor = {Salembier, P. and Albert Oliveras} } @conference {cBosio13, title = {Hierarchical Clustering Combining Numerical and Biological Similarities for Gene Expression Data Classification}, booktitle = {35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC{\textquoteright}13) }, year = {2013}, month = {07/2013}, publisher = {IEEE EMBS}, organization = {IEEE EMBS}, address = {Osaka, Japan}, keywords = {Biological interpretation, Hierarchical representation, microarray classification}, author = {Bosio, M. and Salembier, P. and P. Bellot and Albert Oliveras} } @conference {cPalou13, title = {Hierarchical Video Representation with Trajectory Binary Partition Tree}, booktitle = {Computer Vision and Pattern Recognition (CVPR)}, year = {2013}, month = {06/2013}, address = {Portland, Oregon}, abstract = {

As early stage of video processing, we introduce an iterative trajectory merging algorithm that produces a region-based and hierarchical representation of the video sequence, called the Trajectory Binary Partition Tree (BPT). From this representation, many analysis and graph cut techniques can be used to extract partitions or objects that are useful in the context of specific applications.\ 

In order to define trajectories and to create a precise merging algorithm, color and motion cues have to be used. Both types of informations are very useful to characterize objects but present strong differences of behavior in the spatial and the temporal dimensions. On the one hand, scenes and objects are rich in their spatial color distributions, but these distributions are rather stable over time. Object motion, on the other hand, presents simple structures and low spatial variability but may change from frame to frame. The proposed algorithm takes into account this key difference and relies on different models and associated metrics to deal with color and motion information. We show that the proposed algorithm outperforms existing hierarchical video segmentation algorithms and provides more stable and precise regions.

}, author = {Palou, G. and Salembier, P.} } @phdthesis {dSuau13, title = {Human body analysis using depth data}, year = {2013}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

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

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

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

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

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

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

}, doi = {10.1109/TIP.2012.2231687}, author = {Valero, S. and Salembier, P. and Chanussot, J.} } @inbook {bVentura12, title = {Hierarchical Navigation and Visual Search for Video Keyframe Retrieval}, booktitle = {Advances in Multimedia Modeling}, series = {Lecture Notes in Computer Science}, volume = {7131}, year = {2012}, pages = {652-654}, publisher = {Springer Berlin / Heidelberg}, organization = {Springer Berlin / Heidelberg}, abstract = {

This work presents a browser that supports two strategies for video browsing: the navigation through visual hierarchies and the retrieval of similar images. The input videos are firstly processed by a keyframe extractor to reduce the temporal redundancy and decrease the number of elements to consider. These generated keyframes are hierarchically clustered with the Hierachical Cellular Tree (HCT) algorithm, an indexing technique that also allows the creation of data structures suitable for browsing. Different clustering criteria are available, in the current implementation, based on four MPEG-7 visual descriptors computed at the global scale. The navigation can directly drive the user to find the video timestamps that best match the query or to a keyframe which is globally similar in visual terms to the query. In the latter case, a visual search engine is also available to find other similar keyframes, based as well on MPEG-7 visual descriptors.

Winners of the Novice Run at the VideoBrowser Showdown 2012 at the 18th International Conference on MultiMedia Modeling, Kalgenfurt, Austria, January 4-6, 2012.

}, keywords = {hierarchical navigation, image retrieval, video browser}, isbn = {978-3-642-27354-4}, doi = {10.1007/978-3-642-27355-1_67}, url = {http://dx.doi.org/10.1007/978-3-642-27355-1_67}, author = {Ventura, C. and Martos, Manel and Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @conference {cBenediktsson11, title = {Hierarchical analysis of remote sensing data: morphological attribute profiles and binary partition trees}, booktitle = {International Symposium on Mathematical Morphology 2011}, year = {2011}, pages = {306{\textendash}319}, address = {Intra, Lake Maggiore, Italy}, isbn = {3642215688}, doi = {10.1007/978-3-642-21569-8_27}, url = {http://hdl.handle.net/2117/14624}, author = {Benediktsson, J. and Bruzzone, L. and Chanussot, J. and Dalla Mura, M. and Salembier, P. and Valero, S.} } @article {aCanton-Ferrer11, title = {Human motion capture using scalable body models}, journal = {Computer vision and image understanding}, volume = {115}, number = {10}, year = {2011}, pages = {1363{\textendash}1374}, abstract = {

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

}, issn = {1077-3142}, doi = {10.1016/j.cviu.2011.06.001}, url = {http://hdl.handle.net/2117/13393}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @phdthesis {dValero11, title = {Hyperspectral image representation and Processing with Binary Partition Trees}, year = {2011}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. Therefore, this PhD thesis proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation: the Binary Partition Tree (BPT). This representation can be interpreted as a set of hierarchical regions stored in a tree structure. Based on region-merging techniques, the construction of BPT is investigated in this work by studying hyperspectral region models and the associated similarity metrics. Once the BPT is constructed,the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. Accordingly, some pruning techniques are proposed and discussed according to different applications. This Ph.D is focused in particular on segmentation, object detection and classification of hyperspectral imagery. Experimental results on various hyper spectral data sets demonstrate the interest and the good performances of the BPT representation.

}, url = {http://www.gipsa-lab.grenoble-inp.fr/formation/theses-soutenues.php?id_these=572}, author = {Valero, S.}, editor = {Salembier, P.} } @conference {cValero11b, title = {Hyperspectral image segmentation using binary partition trees}, booktitle = {IEEE International Conference on Image Processing, ICIP 2011}, year = {2011}, pages = {1273{\textendash}1276}, address = {Brussels, Belgium}, isbn = {0-7803-5470-2}, doi = {10.1109/ICIP.2011.6115666}, url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6094293}, author = {Valero, S. and Salembier, P. and Chanussot, J.} } @conference {cCalderero09a, title = {Hierarchical fusion of color and depth information at partition level by cooperative region merging}, booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing 2009}, year = {2009}, pages = {973{\textendash}976}, doi = {10.1109/ICASSP.2009.4959748}, url = {http://hdl.handle.net/2117/9491}, author = {Calderero, F. and Marqu{\'e}s, F.} } @conference {cDimiccoli09a, title = {Hierarchical region-based representation for segmentation and filtering with depth in single images}, booktitle = {IEEE International Conference on Image Processing, ICIP 2009}, year = {2009}, address = {Cairo, Egypt}, author = {Dimiccoli, M. and Salembier, P.} } @conference {cCalderero09, title = {Hierarchical segmentation of vegetation areas in high spatial resolution images by fusion of multispectral information}, booktitle = {2009 IEEE International Geoscience and Remote Sensing Symposium}, year = {2009}, pages = {232{\textendash}235}, isbn = {978-1-4244-3394-0}, doi = {10.1109/IGARSS.2009.5417329}, url = {http://hdl.handle.net/2117/9494}, author = {Calderero, F. and Marqu{\'e}s, F. and Marcello, J. and F. Eugenio} } @conference {cFrias-Velazquez09a, title = {Histogram computation based on image bitwise decomposition}, booktitle = {ICIP 2009}, year = {2009}, isbn = {978-1-4244-5653-6}, doi = {10.1109/ICIP.2009.5413981}, url = {http://hdl.handle.net/2117/9144}, author = {Frias-Velazquez, A. and Morros, J.R.} } @inbook {bMarques09, title = {How are digital images compressed in the web?}, booktitle = {Applied signal processing}, year = {2009}, pages = {265{\textendash}310}, isbn = {978-0-387-74534-3}, url = {http://cataleg.upc.edu/record=b1353617~S1*cat}, author = {Marqu{\'e}s, F. and Menezes, M. and Ruiz-Hidalgo, J.}, editor = {Dutoit, T. and Marqu{\'e}s, F.} } @inbook {bMarques09a, title = {How are digital TV programs compressed to allow broadcasting?}, booktitle = {Applied signal processing}, year = {2009}, pages = {311{\textendash}359}, isbn = {978-0-387-74534-3}, url = {http://cataleg.upc.edu/record=b1353617~S1*cat}, author = {Marqu{\'e}s, F. and Menezes, M. and Ruiz-Hidalgo, J.}, editor = {Dutoit, T. and Marqu{\'e}s, F.} } @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.} } @inbook {bDescamps09, title = {How does digital cinema compress images?}, booktitle = {Applied signal processing}, year = {2009}, pages = {361{\textendash}410}, isbn = {978-0-387-74534-3}, url = {http://cataleg.upc.edu/record=b1353617~S1*cat}, author = {Descamps, A. and De Vleeschouwer, C. and Jacques, L. and Marqu{\'e}s, F.}, editor = {Dutoit, T. and Marqu{\'e}s, F.} } @phdthesis {dCanton-Ferrer09, title = {Human Motion Capture with Scalable Body Models}, year = {2009}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

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

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

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

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

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

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

}, author = {Cristian Canton-Ferrer}, editor = {Casas, J. and M. Pard{\`a}s} } @inbook {bCanton-Ferrer08, title = {Head Orientation Estimation Using Particle Filtering in Multiview Scenarios}, booktitle = {Multimodal Technologies for Perception of Humans}, volume = {4625}, year = {2008}, pages = {317{\textendash}327}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-540-68584-5}, doi = {10.1007/978-3-540-68585-2_30}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer07a, title = {Head Orientation Estimation using Particle Filtering in Multiview Scenarios}, booktitle = {CLEAR{\textquoteright}07 Second International Evaluation Workshop on Classification of Events, Activities and Relationships}, year = {2007}, pages = {1{\textendash}11}, isbn = {978-3-540-68584-5}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @inbook {bCanton-Ferrer07, title = {Head Pose Detection based on Fusion of Multiple Viewpoint Information}, booktitle = {Multimodal Technologies for Perception of Humans}, volume = {4122}, year = {2007}, pages = {305{\textendash}310}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-540-69567-7}, doi = {10.1007/978-3-540-69568-4_28}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cDorea07, title = {Hierarchical partition-based representations for image sequences using trajectory merging criteria}, booktitle = {2007 International Conference on Acoustics, Speech and Signal Processing}, year = {2007}, author = {Dorea, C. and M. Pard{\`a}s and Marqu{\'e}s, F.} } @phdthesis {dChang07, title = {Hierarchical Partition-Based Representations of Motion-Coherent Regions For Video Object Segmentation}, year = {2007}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, author = {Chang, C.}, editor = {M. Pard{\`a}s and Marqu{\'e}s, F.} } @conference {cDorea07a, title = {A hierarchical trajectory-based representation for video}, booktitle = {Fith International Workshop on Content-Based Multimedia Indexing}, year = {2007}, pages = {275{\textendash}282}, isbn = {1-4244-1011-8}, author = {Dorea, C. and M. Pard{\`a}s and Marqu{\'e}s, F.} } @conference {cTurkan07, title = {Human eye localization using edge projections}, booktitle = {International Conference on Computer Vision Theory and Applications, VISAPP 2007}, year = {2007}, pages = {410{\textendash}415}, isbn = {-}, author = {Turkan, M. and M. Pard{\`a}s and Cetin, E.} } @conference {cCanton-Ferrer06b, title = {Head Pose Detection based on Fusion of Multiple Viewpoint Information}, booktitle = {CLEAR{\textquoteright}06 Evaluation Campaign and Workshop - Classification of Events, Activities and Relationships}, year = {2006}, pages = {305{\textendash}310}, isbn = {978-3-540-69567-7}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer06a, title = {Human Model and Motion Based 3D Action Recognition in Multiple View Scenarios}, booktitle = {14th European Signal Processing Conference}, year = {2006}, pages = {1{\textendash}1}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cLandabaso05, title = {Hierarchical Representation of Scenes using Activity Information}, booktitle = {2005 IEEE International Conference on Acoustics, Speech, and Signal Processing}, year = {2005}, pages = {677{\textendash}680}, isbn = {0780388755}, author = {Landabaso, J. and M. Pard{\`a}s} } @conference {cLandabaso03, title = {HMM recognition of expressions in unrestrained video intervals}, booktitle = {International conference on Acoustics, Speech, and Signal Processing}, year = {2003}, pages = {197{\textendash}200}, isbn = {0-7803-7664-1}, author = {Landabaso, J. and M. Pard{\`a}s and Bonafonte, A.} } @phdthesis {dGarrido02, title = {Hierarchical Region Based Processing of Images and Video Sequences: Application to Filtering, Segmentation and Information Retrieval}, year = {2002}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, abstract = {

This work discusses the usefulness of hierarchical region based representations for image and video processing. Region based representations offer a way to perform a first level of abstraction and reduce the number of elements to process with respect to the classical pixel based representation. In this work the two representations that have demonstrated to be useful for region based processing are reviewed, namely region adjacency graphs and trees, and it is discussed why tree based representations are better suited for our purpose. In fact, trees allow representing the image in a hierarchical way and efficient and complex processing techniques can be applied on it. Two major issues are discussed in this work: how the hierarchical representation may be created from a given image and how the tree may be manipulated or processed. Two tree based representations have been developed: the Max-Tree, and the Binary Partition Tree. The Max-Tree structures in a compact way the connected components that arise from all possible level sets from a gray-level image. It is suitable for the implementation of anti-extensive connected operators, ranging from classical ones (for instance, area filter) to new ones (such as the motion filter developed in this work). The Binary Partition Tree structures the set of regions that are obtained during the execution of a region merging algorithm. Developed to overcome some of the drawbacks imposed by the Max-Tree {\textendash} in particular the lack of flexibility of the tree creation and the self-duality of the tree representation {\textendash}, it has demonstrated to be a representation useful for a rather large range of applications, as it is shown in this work. Processing strategies are focused on pruning techniques. Pruning techniques remove some of the branches of the tree based on an analysis algorithm applied on the nodes of the tree. Pruning techniques applied on the Max-Tree lead to anti-extensive operators, whereas self-dual operators are obtained on the Binary Partition Tree, if the tree is created in a self-dual manner. The pruning techniques that have been developed in this work are directed to the following applications: filtering, segmentation and content based image retrieval. The filtering (in the context of connected operators) and segmentation applications are based on the same principle: the nodes of the tree are analyzed according to a fixed criterion, and the decision to remove or preserve a node usually relies on a threshold applied on the former measured criterion. Pruning is then performed according to the previous decision. As a result, the image associated to the pruned tree represents a filtered or segmented version of the original image according to the selected criterion. Some of the criteria that are discussed in this work are based, for instance, on area, motion, marker \& propagation or a rate-distortion strategy. The problem of the lack of robustness of classical decision approaches of non-increasing criteria is discussed and solved by means of an optimization strategy based on the Viterbi algorithm. Content based image retrieval is the third application we have focused on in this work. Hierarchical region based representations are particularly well suited for this purpose since they allow to represent the image at different scales of resolution, and thus the regions of the image can be described at different scales of resolution. In this work we focus on an image retrieval system which supports low level queries based on visual descriptors and spatial relationships. For that purpose, region descriptors are attached to the nodes of the tree. Two types of queries are discussed: single region query, in which the query is made up of one region and, multiple region query, in which the query is made up of a set of regions. In the former visual descriptors are used to perform the retrieval whereas visual descriptors and spatial relationships are used in the latter case. Moreover, a relevance feedback approach is presented to avoid the need of manually setting the weights associated to each descriptor. An important aspect that has been taken into account throughout this work is the efficient implementation of the algorithms that have been developed for both creation and processing of the tree. In the case of the tree creation, efficiency has been obtained mainly due to the use of hierarchical queues, whereas in the processing step analysis algorithms based on recursive strategies are used to get efficient algorithms.

}, url = {http://hdl.handle.net/10803/6878}, author = {Garrido, L.}, editor = {Salembier, P.} } @conference {cSalembier99a, title = {Hierarchical visual description schemes for still images and video sequences}, booktitle = {1999 IEEE International Conference on Image Processing, ICIP 1999}, year = {1999}, address = {Kobe, Japan}, author = {Salembier, P. and O{\textquoteright}Connor, N. and Correia, P. and Pereira, F.} } @conference {cMarques99a, title = {Human face segmentation and tracking using connected components and partition projection}, booktitle = {1999 IEEE INternational Conference on Image Processing}, year = {1999}, isbn = {0-7803-5470-2}, author = {Marqu{\'e}s, F. and Ver{\'o}nica Vilaplana and Buxes, A.} } @conference {cGarrido97b, title = {A hierarchical technique for image sequence analysis}, booktitle = {Workshop on Image Analysis for Multimedia Application Services, WIAMIS{\textquoteright}97}, year = {1997}, pages = {13{\textendash}20}, address = {Louvain-la-Neuve, Belgium}, author = {Garrido, L. and Marqu{\'e}s, F. and M. Pard{\`a}s and Salembier, P. and Ver{\'o}nica Vilaplana} } @conference {cMarques94a, title = {Hierarchical image sequence model for segmentation: application to region-based sequence coding}, booktitle = {VISUAL COMMUNICATION AND IMAGE PROCESSING}, year = {1994}, pages = {554{\textendash}563}, author = {Marqu{\'e}s, F. and Gasull, A. and Vera, V.} } @article {aSalembier94, title = {Hierarchical morphological segmentation for image sequence coding}, journal = {IEEE transactions on image processing}, volume = {3}, number = {5}, year = {1994}, pages = {639{\textendash}651}, issn = {1057-7149}, author = {Salembier, P. and M. Pard{\`a}s} } @phdthesis {dSayrol94, title = {Higher-order statistics applications in image sequence processing}, year = {1994}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, url = {http://hdl.handle.net/10803/6950}, author = {Elisa Sayrol}, editor = {Gasull, A.} }