@article {aCardona25, title = {Exploring Hyperfine Coupling in Molecular Qubits}, journal = {Chemical Science}, year = {2025}, month = {05/2025}, pages = {11291 - 11303}, abstract = {

Molecular qubits represent a promising avenue for advancing quantum sensing and computing technologies, yet significant challenges remain in optimising their performance. Hyperfine coupling has a critical influence on molecular qubit properties. While previous studies have exhaustively investigated this phenomenon, a comprehensive understanding of the underlying mechanisms across different systems remains elusive. A benchmark test was performed using DFT to assess which methodology worked best to accurately predict hyperfine coupling constants in molecular qubits predominantly composed of VIV and CuII. We systematically analysed the decomposition of hyperfine coupling and examined how variations in coordination sphere and molecular geometry impact dipolar, isotropic and spin-orbit contributions. By modelling diverse systems, we demonstrate how molecular design can fine-tune hyperfine coupling contributions, either minimising overall interaction or enhancing coupling along specific axes. This study provides useful insights into the structure-property relationships governing hyperfine coupling mechanisms and assesses the accuracy of different choices of density functional, basis sets and relativistic corrections in the prediction of hyperfine coupling constants.

}, doi = {https://doi.org/10.1039/D5SC02500A}, author = {Cardona, J. and Sol{\'e}, A. and Mella, P. and Aravena, D. and Ruiz-Hidalgo, J. and G{\'o}mez-Coca, S. and Ruiz, E.} } @conference {cAnglada24, title = {Enhancing Ki-67 Cell Segmentation with Dual U-Net Models: A Step Towards Uncertainty-Informed Active Learning}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, year = {2024}, month = {06/2024}, publisher = {IEEE}, organization = {IEEE}, author = {D. Anglada and J. Sala and Marqu{\'e}s, F. and Salembier, P. and M. Pard{\`a}s} } @article {a24, title = {Estimation of 3D Shape and Volume of Fire Plumes from Multiple Views}, journal = {IOP J. Phys.: Conf. Ser.}, volume = {2885}, year = {2024}, month = {09/2024}, chapter = {012075}, abstract = {

This study evaluates deep-learning and Shape from Silhouette (SfS) methods for 3D reconstruction of smoke plumes. It demonstrates the deep-learning method{\textquoteright}s superiority in cases with limited camera views and calibration data, achieving high-quality reconstructions of semitransparent smoke without precise calibration. The research emphasizes the significance of preprocessing and data appearance for neural network efficacy. By improving 3D reconstruction techniques, this work aids in advancing wildfire tracking and environmental analysis, offering a practical approach for real-world applications in fire science.

}, doi = {10.1088/1742-6596/2885/1/012075}, url = {https://iopscience.iop.org/article/10.1088/1742-6596/2885/1/012075}, author = {J{\'u}lia Ariadna Blanco and M. Pard{\`a}s and Casas, J. and Ronan Paugam and Alba {\`A}gueda and Wagner, Joel and Russell Parsons and Planas, Eul{\`a}lia} } @conference {cBlanco24, title = {Estimation of 3D Shape and Volume of Fire Plumes from Multiple Views}, booktitle = {4th European Symposium on Fire Safety Science}, volume = {2885}, year = {2024}, month = {10/2024}, publisher = {IOP J. Phys.: Conf. Ser.}, organization = {IOP J. Phys.: Conf. Ser.}, type = { 4th European Symposium on Fire Safety Science 09/10/2024 - 11/10/2024 Barcelona, Spain}, chapter = {012075}, address = {Barcelona}, abstract = {

This study evaluates deep-learning and Shape from Silhouette (SfS) methods for 3D reconstruction of smoke plumes. It demonstrates the deep-learning method{\textquoteright}s superiority in cases with limited camera views and calibration data, achieving high-quality reconstructions of semi-transparent smoke without precise calibration. The research emphasizes the significance of pre-processing and data appearance for neural network efficacy. By improving 3D reconstruction techniques, this work aids in advancing wildfire tracking and environmental analysis, offering a practical approach for real-world applications in fire science.

}, url = {https://esfss2024.com/}, author = {J{\'u}lia Ariadna Blanco and M. Pard{\`a}s and Casas, J. and Ronan Paugam and Alba {\`A}gueda and Wagner, Joel and Russell Parsons and Planas, Eul{\`a}lia} } @article {aDios24, title = {Experimental evaluation of a MIMO radar performance for ADAS application}, journal = {Telecom}, volume = {5}, year = {2024}, month = {06/2024}, pages = {508-521}, chapter = {508}, abstract = {

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

}, keywords = {advanced driver assistance systems, autonomous vehicles, MIMO radar, radar}, doi = {10.3390/telecom5030026}, author = {Federico Dios and Sergio Torres-Benito and Jos{\'e} Antonio L{\'a}zaro and Casas, J. and Jorge Pinazo and Adolfo Ler{\'\i}n} } @article {aPedersen22, title = {Experimental confirmation of efficient island divertor operation and successful neoclassical transport optimization in Wendelstein 7-X}, journal = {Nuclear Fusion}, volume = {62}, year = {2022}, month = {04/2022}, issn = {1741-4326}, doi = {10.1088/1741-4326/ac2cf5}, author = {Thomas Sunn Pedersen and al et and Casas, J. and Salembier, P.} } @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} } @conference {cFernandezf, title = {Enhancing Online Knowledge Graph Population with Semantic Knowledge}, booktitle = {19th International Semantic Web Conference (ISWC)}, year = {2020}, month = {11/2020}, address = {Virtual}, abstract = {

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

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

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




}, author = {V{\'\i}ctor Campos and Trott, Alexander and Xiong, Caiming and Socher, Richard and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @mastersthesis {xComas19, title = {Exploring Methods for Enhancing Linear Prediction of Video Sequences}, year = {2019}, abstract = {

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

}, author = {Comas, Armand}, editor = {Camps, Octavia and Xavier Gir{\'o}-i-Nieto} } @unpublished {xMoreno18, title = {English to ASL Translator for Speech2Signs}, year = {2018}, month = {01/2018}, type = {Internal report}, abstract = {

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

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

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

Grade: A (9.8/10.9)

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

Exploring Automatic Speech Recognition with Rensorflow from Universitat Polit{\`e}cnica de Catalunya
}, author = {Escur, Janna}, editor = {Costa-juss{\`a}, Marta R. and Xavier Gir{\'o}-i-Nieto} } @inbook {bBellot17, title = {Efficient Combination of Pairwise Feature Networks}, booktitle = {Neural Connectomics Challenge}, year = {2017}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, chapter = {7}, issn = {978-3-319-53069-7}, doi = {10.1007/978-3-319-53070-3}, author = {P Bellot and Patrick Meyer}, editor = {Battaglia, D and Guyon, I. and Lemaire, V. and Orlandi, J. and Ray, B. and Soriano, J.} } @mastersthesis {xCastello17, title = {Extracci{\'o}n de cr{\'a}neo en im{\'a}genes de resonancia magn{\'e}tica del cerebro utilizando una red neuronal convolucional 3D}, year = {2017}, author = {David Rodr{\'\i}guez Castell{\'o}}, editor = {Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana} } @unpublished {xBellver, title = {Efficient search of objects in images using deep reinforcement learning}, journal = {NIPS Women in Machine Learning Workshop}, year = {2016}, type = {Abstract}, address = {Barcelona.}, abstract = {

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

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

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

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

References:

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

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


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

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

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

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

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

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

[Project page]

Grade: B (8.2/10.0)

EgoMon Gaze and Video Dataset for Visual Saliency Prediction from Xavier Giro
}, author = {Chert{\'o}, M{\`o}nica}, editor = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xBellver-Bueno, title = {Efficient Exploration of Region Hierarchies for Semantic Segmentation}, year = {2015}, abstract = {

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

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

Grade: A (9.0/10.0)

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

Efficient exploration of region hierarchies for semantic segmentation from Xavier Giro

}, author = {M{\'\i}riam Bellver}, editor = {Ventura, C. and Xavier Gir{\'o}-i-Nieto} } @article {xPan, title = {End-to-end Convolutional Network for Saliency Prediction}, year = {2015}, month = {07/2015}, institution = {arXiv}, address = {Boston, MA (USA)}, abstract = {

The prediction of saliency areas in images has been traditionally addressed with hand crafted features based on neuroscience principles. This paper however addresses the problem with a completely data-driven approach by training a convolutional network. The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train a not very deep architecture which is both fast and accurate. The convolutional network in this paper, named JuntingNet, won the LSUN 2015 challenge on saliency prediction with a superior performance in all considered metrics.



}, url = {http://arxiv.org/abs/1507.01422}, author = {Pan, Junting and Xavier Gir{\'o}-i-Nieto} } @conference {cRoldan-Carlosa, title = {Event Video Retrieval using Global and Local Descriptors in Visual Domain}, booktitle = {IEEE/ACM International Workshop on Content-Based Multimedia Indexing - CBMI 2015 }, year = {2015}, month = {06/2015}, abstract = {

With the advent of affordable multimedia smart phones, it has become common that people take videos when they are at events. The larger the event, the larger is the amount of videos taken there and also, the more videos get shared online. To search in this mass of videos is a challenging topic. In this paper we present and discuss a prototype software for searching in such videos. We focus only on visual information, and we report on experiments based on a research data set. With a small study we show that our prototype demonstrates promising results by identifying the same scene in different videos taken from different angles solely based on content based image retrieval.

}, url = {https://upcommons.upc.edu/handle/2117/76553}, author = {Roldan-Carlos, Jennifer and Lux, Mathias and Xavier Gir{\'o}-i-Nieto and Pia Mu{\~n}oz-Trallero and Anagnostopoulos, Nektarios} } @conference {cMohedano, title = {Exploring EEG for Object Detection and Retrieval}, booktitle = {ACM International Conference on Multimedia Retrieval (ICMR) }, year = {2015}, address = {Shanghai, China}, abstract = {

This paper explores the potential for using Brain Computer Interfaces (BCI) as a relevance feedback mechanism in content-based image retrieval. We investigate if it is possible to capture useful EEG signals to detect if relevant objects are present in a dataset of realistic and complex images. \ We perform several experiments using a rapid serial visual presentation (RSVP) of images at different rates (5Hz and 10Hz) on 8 users with different degrees of familiarization with BCI and the dataset. We then use the feedback from the BCI and mouse-based interfaces to retrieve objects in a subset of TRECVid images. We show that it is indeed possible detect such objects in complex images and, also, that users with previous knowledge on the dataset or experience with the RSVP outperform others. When the users have limited time to annotate the images (100 seconds in our experiments) both interfaces are comparable in performance. Comparing our best users in a retrieval task, we found that EEG-based relevance feedback outperforms mouse-based feedback. The realistic and complex image dataset differentiates our work from previous studies on EEG for image retrieval.

[Extended version in arXiv:1504.02356]

[ACM ICMR 2015 website]

Overall acceptance rate: 33\% (source)

}, doi = {10.1145/2671188.2749368}, url = {http://arxiv.org/abs/1504.02356}, author = {Mohedano, Eva and Amaia Salvador and Porta, Sergi and Xavier Gir{\'o}-i-Nieto and Kevin McGuinness and Healy, Graham and O{\textquoteright}Connor, N.} } @article {cBellot Pujalte14, title = {Efficient combination of pairwise feature networks}, journal = {JMLR: Workshop and Conference Proceedings }, volume = {46}, year = {2014}, month = {10/2015}, pages = {77 - 84}, publisher = {JMLR: Workshop and Conference Proceedings}, address = {Nancy, France}, abstract = {

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

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

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

}, keywords = {approximate nearest neighbor, deblurring, epipolar line, Super resolution}, author = {Bosch, I. and Salvador, J. and E. Perez-Pellitero and Ruiz-Hidalgo, J.} } @mastersthesis {xSalvador, title = {Exploiting User Interaction and Object Candidates for Instance Retrieval and Object Segmentation}, year = {2014}, abstract = {

Author: Amaia Salvador-Aguilera

Advisors:\ Xavier Gir{\'o}-i-Nieto\ (UPC) and Kevin McGuinness (Dublin CIty University)

Degree:\ Master in Computer Vision\ (1 year)

Video: Thesis defense

This thesis addresses two of the main challenges nowadays for computer vision: object segmentation and visual instance retrieval. The methodologies proposed to solve both problems are based on the use of object candidates and human computation in the computer vision loop. In the object segmentation side, this work explores how human computation can be useful to achieve better segmentation results, by combining users{\textquoteright} traces with a segmentation algorithm based on object candidates. On the other hand, the instance retrieval problem is also addressed using object candidates to compute local features, and involving the user in the retrieval loop by applying relevance feedback strategies.

}, keywords = {computer Vision, human computing, instance search, object candidates, segmentation}, author = {Amaia Salvador}, editor = {Xavier Gir{\'o}-i-Nieto and Kevin McGuinness} } @mastersthesis {xAntoja-Sabin13, title = {El tel{\`e}fon m{\`o}bil com a eina d{\textquoteright}aprenentatge informal}, year = {2013}, abstract = {

Director: Xavier Gir{\'o}-i-Nieto

Els tel{\`e}fons intel{\textperiodcentered}ligents estan cada dia m{\'e}s presents a la nostra societat. L{\textquoteright}educaci{\'o} secund{\`a}ria obligat{\`o}ria no n{\textquoteright}est{\`a} al marge. M{\'e}s aviat al contrari, {\'e}s un dels temes a nivell de conducta que genera m{\'e}s pol{\`e}mica actualment amb l{\textquoteright}alumnat adolescent. Per altra banda, {\'e}s una tecnologia molt potent per accedir a informaci{\'o} i relacionar-se, en qualsevol moment i lloc.

La proposta {\'e}s que l{\textquoteright}alumne hagi de captar amb el tel{\`e}fon m{\`o}bil algun element que tingui a veure amb all{\`o} que s{\textquoteright}ha treballat a classe i li sigui proper. Es tracta de vincular la teoria amb la pr{\`a}ctica, els conceptes de classe amb la seva quotidianitat.

El docent proposar{\`a} uns deures relacionats amb els continguts de classe. L{\textquoteright}alumne utilitzar{\`a} el tel{\`e}fon m{\`o}bil intel{\textperiodcentered}ligent com a vehicle per realitzar la tasca.

Per tal de deixar const{\`a}ncia de la feina feta, s{\textquoteright}utilitzar{\`a} la metodologia de la carpeta d{\textquoteright}aprenentatge electr{\`o}nica (electronic portfolio). Aquesta segona eina permetr{\`a} deixar const{\`a}ncia de tot el que l{\textquoteright}alumne va fent, avaluar-lo i fer una coavaluaci{\'o}.

A la mem{\`o}ria del treball s{\textquoteright}explica quins fonaments pedag{\`o}gics t{\'e} la proposta, com encaixa en el context normatiu de l{\textquoteright}educaci{\'o} secund{\`a}ria obligat{\`o}ria, exemples pr{\`a}ctics d{\textquoteright}exercicis amb els que s{\textquoteright}aplica la metodologia, com aix{\`o} es pot dur a terme a nivell t{\`e}cnic i els resultats de l{\textquoteright}experiment fet amb un grup d{\textquoteright}alumnes utilitzant la metodologia proposada.\ 

}, keywords = {educaci{\'o}, internet, ortfolio, secund{\`a}ria, significatiu. bloc, smartphone, tecnologia. aprenentatge, wordpress}, author = {Antoja-Sabin, Joan}, editor = {Xavier Gir{\'o}-i-Nieto} } @conference {cBosio13a, title = {Ensemble learning and hierarchical data representation for microarray classification}, booktitle = {13th IEEE International Conference on BioInformatics and BioEngineering BIBE}, year = {2013}, month = {11/2013}, publisher = {13th IEEE International Conference on BioInformatics and BioEngineering,}, organization = {13th IEEE International Conference on BioInformatics and BioEngineering,}, address = {Chania, Crete}, abstract = {

Microarray data classification is an open and active research field.

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

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

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

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

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

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

}, keywords = {Microarray classification; metagenes; hierarchical representation; Treelets; feature selection; LDA; ensemble.}, author = {Bosio, M. and Salembier, P. and Albert Oliveras and P. Bellot} } @mastersthesis {xGarcia-delMolino13, title = {Extension of Instance Search Technique by Geometric Coding and Quantization Error Compensation}, year = {2013}, abstract = {

This MSc thesis analyzes two ways of improving the video retrieval techniques for instance search problem. In one hand, "Pairing Interest Points for a better Signature using Sparse Detector{\textquoteright}s Spatial Information", allows the Bag-of-Words model to keep some spatial information. In the other, "Study of the Hamming Embedding Signature Symmetry in Video Retrieval" provides binary signatures that refine the matching based on visual words, and aims to find the best way of matching taking into account the existent asymmetries between image query and videos.

}, url = {http://hdl.handle.net/2099.1/19159}, author = {Garcia-delMolino, Ana}, editor = {Satoh, Shin{\textquoteright}ichi and Ventura, C. and Xavier Gir{\'o}-i-Nieto} } @article {aGallego12, title = {Enhanced foreground segmentation and tracking combining Bayesian background, shadow and foreground modeling}, journal = {Pattern Recognition Letters}, volume = {33}, year = {2012}, month = {09/2012}, pages = {1558{\textendash}1568}, type = {journal}, chapter = {1558}, abstract = {

In this paper we present a foreground segmentation and tracking system for monocular static camera sequences and indoor scenarios that achieves correct foreground detection also in those complicated scenes where similarity between foreground and background colours appears. The work flow of the system is based on three main steps: An initial foreground detection performs a simple segmentation via Gaussian pixel color modeling and shadows removal. Next, a tracking step uses the foreground segmentation for identifying the objects, and tracks them using a modified mean shift algorithm. At the end, an enhanced foreground segmentation step is formulated into a Bayesian framework. For this aim, foreground and shadow candidates are used to construct probabilistic foreground and shadow models. The Bayesian framework combines a pixel-wise color background model with spatial-color models for the foreground and shadows. The final classification is performed using the graph-cut algorithm. The tracking step allows a correct updating of the probabilistic models, achieving a foreground segmentation that reduces the false negative and false positive detections, and obtaining a robust segmentation and tracking of each object of the scene.

}, keywords = {Foreground segmentation, GMM, Objects tracking, Shadow model, Space-color models}, issn = {0167-8655}, doi = {10.1016/j.patrec.2012.05.004}, url = {http://www.sciencedirect.com/science/article/pii/S016786551200164X}, author = {Gallego, J. and M. Pard{\`a}s and Haro, G.} } @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 {cGallego10, title = {Enhanced bayesian foreground segmentation using brightness and color distortion region-based model for shadow removal}, booktitle = {2010 IEEE International Conference on Image Processing}, year = {2010}, pages = {3449{\textendash}3452}, isbn = {1522-4880}, doi = {10.1109/ICIP.2010.5653897}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5653897\&tag=1}, author = {Gallego, J. and M. Pard{\`a}s} } @mastersthesis {xmunoz-trallero10, title = {Extensi{\'o} d{\textquoteright}una interf{\'\i}cie de cerca d{\textquoteright}imatges a les consultes amb regions}, year = {2010}, abstract = {

Aquest {\'e}s un projecte universitat-empresa que presenta una interf{\'\i}cie de cerca multimodal a partir de la integraci{\'o} de diferents eines ja existents per crear una eina global que combina les cerques textuals amb les consultes mitjan{\c c}ant exemple, ja sigui una consulta basada en una imatge o en una regi{\'o} d{\textquoteright}aquesta imatge. L{\textquoteright}eina global s{\textquoteright}integra en l{\textquoteright}entorn del Digition, el gestor de continguts audiovisuals de la Corporaci{\'o} Catalana de Mitjans Audiovisuals. La principal aportaci{\'o} del present projecte consisteix en la creaci{\'o} d{\textquoteright}una interf{\'\i}cie de cerca d{\textquoteright}imatges basada en regions i la seva integraci{\'o} en l{\textquoteright}eina global.


}, url = {http://hdl.handle.net/2099.1/13525}, author = {Pia Mu{\~n}oz-Trallero}, editor = {Xavier Gir{\'o}-i-Nieto and Vives, X.} } @conference {cDimiccoli09, title = {Exploiting T-junctions for depth segregation in single images}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009}, year = {2009}, address = {Taipei, Taiwan}, author = {Dimiccoli, M. and Salembier, P.} } @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.} } @phdthesis {dIregui08, title = {Efficient strategies for navigation through very large JPEG2000 image}, year = {2008}, school = {Universit{\'e} Catholique de Louvain (UCL)}, type = {phd}, url = {http://hdl.handle.net/2078.1/19637 }, author = {Iregui, M.}, editor = {Marqu{\'e}s, F. and Macq, B.} } @article {aOliveras08, title = {Elevated basal hepcidin levels in the liver may inhibit the development of malaria infection: Another piece towards solving the malaria puzzle?}, journal = {Medical hypotheses}, volume = {70}, number = {3}, year = {2008}, pages = {630{\textendash}634}, issn = {0306-9877}, doi = {10.1016/j.mehy.2007.07.021}, url = {http://www.sciencedirect.com/science/article/B6WN2-4PK8B8Y-5/2/f5ccc9584cc2e8c2095731c9be9d4a31}, author = {Albert Oliveras and Espel-Masferrer, E.} } @inbook {bCanton-Ferrer08a, title = {Exploiting Structural Hierarchy in Articulated Objects Towards Robust Motion Capture}, booktitle = {Articulated Motion and Deformable Objects}, volume = {5098}, year = {2008}, pages = {82{\textendash}91}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-540-70516-1}, doi = {10.1007/978-3-540-70517-8_9}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer08, title = {Exploiting Structural Hierarchy in Articulated Objects Towards Robust Motion Capture}, booktitle = {V Conference on Articulated Motion and Deformable Objects (AMDO)}, year = {2008}, pages = {82{\textendash}91}, isbn = {0302-9743}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cMorros07, title = {Event recognition for meaningful Human-Computer interaction in smart environments}, booktitle = {Proceedings of the eNTERFACE{\textquoteright}07 Workshop on Multimodal Interfaces}, year = {2007}, month = {08/2007}, abstract = {

The aim of this project is to monitor a room for the purposes of analysing the interactions and identities of a small set of individ- uals. We work with multiple uncalibrated sensors that observe a single environment and generate multimodal data streams. These streams are processed with the help of a generic client-server middleware called SmartFlow. Modules for visual motion de- tection, visual face tracking, visual face identification, visual op- portunistic sensing, audio-based localization, and audio-based identification are implemented and integrated under SmartFlow to work in coordination across different platforms.

}, keywords = {Audio systems, Image motion analysis, Image sensors, pattern recognition}, isbn = { 978-2874631054}, author = {Morros, J.R. and Albert Ali Salah and Ben Schouten and Carlos Segura Perales and Jordi Luque Serrano and Onkar Ambekar and Ceren Kayalar and Cem Keskin and Lale Akarun} } @conference {cGiro-i-Nieto06, title = {The edition of the Wikipedia as an academic activity}, booktitle = {4rt. Congr{\'e}s Internacional de Doc{\`e}ncia Unversit{\`a}ria i Innovaci{\'o}}, year = {2006}, month = {07/2006}, pages = {{\textendash}}, address = {Barcelona, Catalonia}, abstract = {

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

[CIDUI 2006 website]

}, keywords = {wikipedia, ~cooperative, ~online}, isbn = {84-8458-240-X}, url = {http://hdl.handle.net/2117/13157}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Casas, J.} } @conference {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} } @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 {cLobo04, title = {Enhanced audio data hiding synchronization using non-linear filter}, booktitle = {International Conference on Acoustics, Speech, and Signal Processing}, year = {2004}, pages = {885{\textendash}888}, author = {Lobo, A. and Bas, P. and Marqu{\'e}s, F.} } @conference {cPardas02, title = {Emotion recognition based on MPEG-4 facial animation parameters}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, year = {2002}, pages = {3624{\textendash}3627}, author = {M. Pard{\`a}s and Bonafonte, A. and Landabaso, J.} } @conference {cPineda01, title = {Estudio de campos de golf mediante t{\'e}cnicas de segmentaci{\'o}n}, booktitle = {IX Congreso Nacional de Teledetecci{\'o}n}, year = {2001}, address = {Lleida, Spain}, author = {Pineda, N and Jorge, J and Garrido, L. and Salembier, P.} } @conference {cPardas00, title = {Extraction and tracking of the eyelids}, booktitle = {International Conference on Acoustics, Speech and Signal Processing}, year = {2000}, author = {M. Pard{\`a}s} } @conference {cGasull99, title = {Editor gr{\'a}fico de figuras MATLAB}, booktitle = {III Congreso de Usuarios de MATLAB}, year = {1999}, pages = {219{\textendash}227}, isbn = {84-699-1358-1}, author = {Gasull, A. and Elisa Sayrol and Moreno, A. and Vallverdu, F. and Salavedra, J. and Albert Oliveras} } @article {aGarrido98, title = {Extensive Operators in Partition Lattices for Image Sequence Analysis}, journal = {Signal processing}, volume = {66}, number = {2}, year = {1998}, pages = {157{\textendash}180}, issn = {0165-1684}, author = {Garrido, L. and Salembier, P. and Garc{\'\i}a, D.} } @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 {cSayrol95a, title = {Estimation of motion parameters using hos}, booktitle = {IEEE SIGNAL PROCESSING-ATHOD WORKSHOP ON HIGHER-ORDER STATISTICS.}, year = {1995}, pages = {262{\textendash}265}, isbn = {1522-4880}, author = {Elisa Sayrol and Gasull, A. and R. Fonollosa, Javier} } @conference {cSalembier93c, title = {Edge versus contrast estimation of morphological filters}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1993}, year = {1993}, address = {Mineapolis (MI), USA}, author = {Salembier, P. and Serra, J. and Bangham, J.} } @conference {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 {cBonafonte92a, title = {Efficient integration of coarticulation and lexical information ...}, booktitle = {INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING}, year = {1992}, pages = {45{\textendash}48}, author = {Bonafonte, A. and Mari{\~n}o, J. and M. Pard{\`a}s} } @conference {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.} }