@conference {cLems25, title = {Towards a multicentric open DigitAL PatHology assIstant beNchmark: Initial Results from the DALPHIN Study}, booktitle = {United States \& Canadian Academy of Pathology 114th Annual Meeting (USCAP 2025)}, year = {2025}, month = {03/2025}, publisher = {Laboratory Investigation, Volume 105, Issue 3, Supplement, 2025, 103609, ISSN 0023-6837}, organization = {Laboratory Investigation, Volume 105, Issue 3, Supplement, 2025, 103609, ISSN 0023-6837}, address = {Boston, USA}, doi = {https://doi.org/10.1016/j.labinv.2024.103609.}, url = {https://www.sciencedirect.com/science/article/pii/S0023683724032872}, author = {Carlijn M Lems and Nat{\'a}lie Klub{\'\i}{\v c}kov{\'a} and Biagio Brattoli, and Taebum Lee and Ver{\'o}nica Vilaplana and Pedro Luis Fern{\'a}ndez and Laura Pons and Milda Poceviciute and Nadieh Khalili and Francesco Ciompi} } @conference {cLems25a, title = {Towards a multicentric open DigitAL PatHology assIstant beNchmark: Initial Results from the DALPHIN Study}, booktitle = {United States \& Canadian Academy of Pathology 114th Annual Meeting (USCAP 2025)}, year = {2025}, month = {03/2025}, publisher = {Laboratory Investigation, Volume 105, Issue 3, Supplement, 2025, 103609, ISSN 0023-6837}, organization = {Laboratory Investigation, Volume 105, Issue 3, Supplement, 2025, 103609, ISSN 0023-6837}, address = {Boston, USA}, doi = {https://doi.org/10.1016/j.labinv.2024.103609.}, url = {https://www.sciencedirect.com/science/article/pii/S0023683724032872}, author = {Carlijn M Lems and Nat{\'a}lie Klub{\'\i}{\v c}kov{\'a} and Biagio Brattoli and Taebum Lee} } @article {aManjabacas25, title = {Twenty years of AFORO: New developments and connections enhancing otolith research}, journal = {Fisheries Research}, volume = {281}, year = {2025}, month = {01/2025}, abstract = {

Since 2003, with the aim of addressing different issues related to otoliths and the advancement of technologies, AFORO acronym of An{\`a}lisi de FORmes d{\textquoteright}Ot{\`o}lits (otolith shape analysis), a computational environment with a set of tools including a website for this purpose, has gradually expanded its functionality. This implies different ways to measure otoliths in order to obtain different types of information of the same individual. The 2D otoliths shape description using different methods permits the comparison between classical methods and the Wavelet transform, used in the automatic classification system of AFORO. The 3D otoliths description in a few images opens the possibility of classifications using volumetric information, which they have performed well. The data of relationships between otolith and fish length in AFORO is used in predator-prey studies. Our website offers the possibility to display otolith georeferencing information interactively. Furthermore, the automatic classification system has been improved by incorporating geographic filters based on georeferenced data. The diverse information that AFORO offers on the otoliths opens the possibility to approach new studies or to improve the existing ones, combining this information or applying the most appropriate one to the specific problem. Classification cases with Wavelets that have been improved by introducing geographical area information will be presented. In addition, Deep Learning algorithms will be introduced by performing classifications with a small subset of otoliths of species which contain a sufficient number of specimens (\>8).

}, keywords = {artificial intelligence, computer Vision, deep learning, neural networks, Otoliths}, issn = {0165-7836}, doi = {https://doi.org/10.1016/j.fishres.2024.107242}, author = {A. Manjabacas and O. Chic and E. Garc{\'\i}a-Ladona and V.M. Tuset and Morros, J.R. and Elisa Sayrol and P. Mart{\'\i}-Puig and J. Forest and J. Vasconcelos and A. Lombarte} } @conference {cTheodoropoulou24, title = {Towards AI-Enhanced Fibre-Wireless Optical 6G Network in Support of Connected Mobility}, year = {2024}, month = {10/2024}, publisher = {IEEE}, organization = {IEEE}, address = {Athens}, abstract = {

The 6G-EWOC project seeks to create an AI-enhanced fiber-wireless optical 6G network to support advanced use cases for Fully Autonomous Vehicles (FAVs) by establishing a new access network incorporating the integration of both fixed/wireless optical and wireless/RF technologies. The access network will be supported by a fast, reconfigurable, highly dynamic and customizable optical fiber-based fronthaul infrastructure which will minimize optoelectronic transitions through tunable and programmable devices, as well as low energy photonic switching of (packet/optical) spectrum and spatial resources, managed by AI-enabled SDN. End-to-end connectivity between AI-based edge computation units will enable advanced Connected and Automated Mobility (CAM) services over a rapid, adaptable network architecture.

}, doi = {10.5281/zenodo.14246039}, author = {Theodoropoulou, E. and Lyberopoulos, G. and Setaki, Foteini and Pablo Garc{\'\i}a-G{\'o}mez and Santiago Royo and Marcus, C. and Eriksson, O. and F{\`a}brega, J.M. and Ricardo Mart{\'\i}nez and Ramon Casellas and Schrenk, B. and Casas, J. and Jos{\'e} Antonio L{\'a}zaro} } @conference {cTarres22, title = {Tackling Low-Resourced Sign Language Translation: UPC at WMT-SLT 22}, booktitle = {EMNLP 2022 Seventh Conference on Machine Translation (WMT22)}, year = {2022}, month = {10/2022}, abstract = {

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

Poster view

}, author = {Laia Tarr{\'e}s and Gerard I. G{\'a}llego and Xavier Gir{\'o}-i-Nieto and Jordi Torres} } @mastersthesis {xBudria, title = {Topic Detection from Sign Language Videos}, year = {2022}, abstract = {

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

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

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

}, author = {{\'A}lvaro Budria and Laia Tarr{\'e}s and Gerard I. G{\'a}llego and Moreno, Francesc and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @article {xGiro-i-Nieto22, title = {Towards Sign Language Translation and Production}, year = {2022}, abstract = {

Machine translation and computer vision have greatly benefited from the advances in deep learning. A large and diverse amount of textual and visual data have been used to train neural networks whether in a supervised or self-supervised manner. Nevertheless, the convergence of the two fields in sign language translation and production still poses multiple open challenges, like the low video resources, limitations in hand pose estimation, or 3D spatial grounding from poses.

}, author = {Xavier Gir{\'o}-i-Nieto and Amanda Duarte} } @mastersthesis {xIturralde, title = {Towards video alignment across cameras with sign language 2D poses}, year = {2021}, abstract = {

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

}, author = {Andrea Iturralde}, editor = {Laia Tarr{\'e}s and Xavier Gir{\'o}-i-Nieto} } @conference {cBernal, title = {Transcription-Enriched Joint Embeddings or Spoken Descriptions of Images and Videos}, booktitle = {CVPR 2020 Workshop on Egocentric Perception, Interaction and Computing}, year = {2020}, month = {06/2020}, publisher = {arXiv}, organization = {arXiv}, address = {Seattle, WA, USA}, abstract = {

In this work, we propose an effective approach for training unique embedding representations by combining three simultaneous modalities: image and spoken and textual narratives. The proposed methodology departs from a baseline system that spawns a embedding space trained with only spoken narratives and image cues. Our experiments on the EPIC-Kitchen and Places Audio Caption datasets show that introducing the human-generated textual transcriptions of the spoken narratives helps to the training procedure yielding to get better embedding representations. The triad speech, image and words allows for a better estimate of the point embedding and show an improving of the performance within tasks like image and speech retrieval,\ even when text third modality, text, is not present in the task.

Transcription-Enriched Joint Embeddings for Spoken Descriptions of Images and Videos from Universitat Polit{\`e}cnica de Catalunya
}, author = {Oriol, Benet and Luque, J. and Diego, Ferran and Xavier Gir{\'o}-i-Nieto} } @conference {cLinardos, title = {Temporal Saliency Adaptation in Egocentric Videos}, booktitle = {ECCV 2018 Workshop on Egocentric Perception, Interaction and Computing}, year = {2018}, month = {07/2018}, publisher = {Extended abstract}, organization = {Extended abstract}, address = {Munich, Germany}, abstract = {

}, author = {Linardos, Panagiotis and Mohedano, Eva and Chert{\'o}, M{\`o}nica and Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto} } @article {aLin, title = {Temporally Coherent 3D Point Cloud Video Segmentation in Generic Scenes}, journal = {IEEE Transactions on Image Processing}, volume = {27}, year = {2018}, month = {06/2018}, pages = {3087 - 3099}, abstract = {

Video segmentation is an important building block for high level applications such as scene understanding and interaction analysis. While outstanding results are achieved in this field by state-of-the-art learning and model based methods, they are restricted to certain types of scenes or require a large amount of annotated training data to achieve object segmentation in generic scenes. On the other hand, RGBD data, widely available with the introduction of consumer depth sensors, provides actual world 3D geometry compared to 2D images. The explicit geometry in RGBD data greatly helps in computer vision tasks, but the lack of annotations in this type of data may also hinder the extension of learning based methods to RGBD. In this paper, we present a novel generic segmentation approach for 3D point cloud video (stream data) thoroughly exploiting the explicit geometry in RGBD. Our proposal is only based on low level features, such as connectivity and compactness. We exploit temporal coherence by representing the rough estimation of objects in a single frame with a hierarchical structure, and propagating this hierarchy along time. The hierarchical structure provides an efficient way to establish temporal correspondences at different scales of object-connectivity, and to temporally manage the splits and merges of objects. This allows updating the segmentation according to the evidence observed in the history. The proposed method is evaluated on several challenging datasets, with promising results for the presented approach.

}, issn = {1057-7149}, doi = {10.1109/TIP.2018.2811541}, url = {https://ieeexplore.ieee.org/document/8306148/}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @conference {cDuarte, title = {Towards Speech to Sign Language Translation}, booktitle = {ECCV 2018 Workshop on Shortcomings in Vision and Language}, year = {2018}, month = {08/2018}, abstract = {

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

}, author = {Amanda Duarte and Camli, Gorkem and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xAssens, title = {The Temporal Dimension of Visual Attention Models}, year = {2017}, abstract = {

Program: Bachelor Degree on Telecommunications Science and Technologies (CITTEL)

Grade: A with honours (10.0/10.0)

This thesis explores methodologies for scanpath prediction on images using deep learning\ frameworks.\ As a preliminary step, we analyze the characteristics of the data provided by di erent datasets.\ We then explore the use of Convolutional Neural Networks (CNN) and Long-Short-Term-Memory\ (LSTM) newtworks for scanpath prediction. We observe that these models fail due to the high\ stochastic nature of the data.\ With the gained insight, we propose a novel time-aware visual saliency representation named\ Saliency Volume, that averages scanpaths over multiple observers.\ Next, we explore the SalNet network and adapt it for saliency volume prediction, and we find\ several ways of generating scanpaths from saliency volumes.\ Finally, we ne-tuned our model for scanpaht prediction on 360-degree images and successfully\ submitted it to the Salient360! Challenge from ICME. The source code and models are publicly\ available at https://github.com/massens/saliency-360salient-2017.

The Temporal Dimension of Visual Attention Models from Xavier Giro-i-Nieto
}, author = {Assens, Marc}, editor = {McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto and Noel E. O{\textquoteright}Connor} } @conference {xDuarte, title = {Temporal-aware Cross-modal Embeddings for Video and Audio Retrieval}, booktitle = {NIPS 2017 Women in Machine Learning Workshop (WiML)}, year = {2017}, month = {12/2017}, publisher = {NIPS 2017 Women in Machine Learning Workshop}, organization = {NIPS 2017 Women in Machine Learning Workshop}, address = {Long Beach, CA, USA}, abstract = {

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

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

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

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

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

References

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

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

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

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

}, author = {Amanda Duarte and Sur{\'\i}s, D{\'\i}dac and Amaia Salvador and Jordi Torres and Xavier Gir{\'o}-i-Nieto} } @conference {cLe, title = {Towards large scale multimedia indexing: A case study on person discovery in broadcast news}, booktitle = {International Workshop on Content-Based Multimedia Indexing - CBMI 2017}, year = {2017}, month = {06/2017}, address = {Firenze, Italy}, abstract = {

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

}, doi = {https://doi.org/10.1145/3095713.3095732}, author = {Nam Le and Herv{\'e} Bredin and Gabriel Sargent and Miquel India and Paula Lopez-Otero and Claude Barras and Camille Guinaudeau and Guillaume Gravier and Gabriel Barbosa da Fonseca and Izabela Lyon Freire and Zenilton Patroc{\'\i}nio Jr. and Silvio Jamil F. Guimaraes and Gerard Mart{\'\i} and Morros, J.R. and Javier Hernando and Laura Docio-Fernandez and Carmen Garcia-Mateo and Sylvain Meignier and Jean-Marc Odobez} } @conference {cMontes, title = {Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks}, booktitle = {1st NIPS Workshop on Large Scale Computer Vision Systems 2016}, year = {2016}, month = {12/2016}, abstract = {

This work proposes a simple pipeline to classify and temporally localize activities in untrimmed videos. Our system uses features from a 3D Convolutional Neural Network (C3D) as input to train a a recurrent neural network (RNN) that learns to classify video clips of 16 frames. After clip prediction, we post-process the output of the RNN to assign a single activity label to each video, and determine the temporal boundaries of the activity within the video. We show how our system can achieve competitive results in both tasks with a simple architecture. We evaluate our method in the ActivityNet Challenge 2016, achieving a 0.5874 mAP and a 0.2237 mAP in the classification and detection tasks, respectively. Our code and models are publicly available at:\ https://imatge-upc.github.io/activitynet-2016-cvprw/

}, author = {Montes, Alberto and Amaia Salvador and Pascual-deLaPuente, Santiago and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xMontes, title = {Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks}, year = {2016}, abstract = {

Advisors: Amaia Salvador and Xavier Gir{\'o}-i-Nieto.

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

Grade: A (9.8/10.0)

This thesis explore different approaches using Convolutional and Recurrent Neural Networks to classify and temporally localize activities on videos, furthermore an implementation to achieve it has been proposed.

As the first step, features have been extracted from video frames using an state of the art 3D Convolutional Neural Network. This features are fed in a recurrent neural network that solves the activity classification and temporally location tasks in a simple and flexible way.

Different architectures and configurations have been tested in order to achieve the best performance and learning of the video dataset provided. In addition it has been studied different kind of post processing over the trained network{\textquoteright}s output to achieve a better results on the temporally localization of activities on the videos.

The results provided by the neural network developed in this thesis have been submitted to the ActivityNet Challenge 2016 of the CVPR, achieving competitive results using a simple and flexible architecture.

Temporal Activity Detection in Untrimmed Videos with Recurrent Neural Networks from Xavier Giro
}, keywords = {deep learning, neural networks, videos}, author = {Montes, Alberto}, editor = {Amaia Salvador and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xReyesa, title = {Time-sensitive Egocentric Image Retrieval for Fidings Objects in Lifelogs}, year = {2016}, abstract = {

Advisors: Eva Mohedano (Insight DCU), Kevin McGuinness (Insight DCU) 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 (9.4/10.0)

This work explores diverse practices for conducting an object search from large amounts of egocentric images taking into account their temporal information. The application of this technology is to identify where personal belongings were lost or forgotten. We develop a pipeline-structured system. Firstly, the images of the day being scanned are sorted based on their probability to depict the forgotten object. This stage is solved by applying an existing visual search engine based on deep learning features. Secondly, a learned threshold selects the top ranked images as candidates to contain the object. Finally the images are reranked based on temporal and diversity criteria. Furthermore, we build a validation environment for assessing the system{\textquoteright}s performance aiming to find the optimal configuration of its parameters. Due to the lack of related works to be compared with, this thesis proposes an novel evaluation framework and metric to assess the problem.

[Tfg cristian reyes] time sensitive egocentric image retrieval for finding objects in lifelogs from Xavier Giro
}, author = {Reyes, Cristian}, editor = {Mohedano, Eva and McGuinness, Kevin and Xavier Gir{\'o}-i-Nieto} } @article {xMora, title = {Towards Automatic Generation of Question Answer Pairs from Images}, year = {2016}, institution = {Visual Question Answering Challenge Workshop, CVPR 2016}, address = {Las Vegas, NV, USA}, abstract = {

This extended abstract presents our research in the generic field of Visual Question-Answering (VQA) focusing on a new branch that aims to generate question-answer pairs based on an image.\ To do so, we use the VQA dataset provided for the VQA challenge to train a Deep Neural Network which has the image as an input and two different outputs, the question and its associated answer.

Extended abstract presented as poster in the Visual Question Answering Challenge Workshop, CVPR 2016.

}, url = {http://www.visualqa.org/abstracts.html}, author = {Masuda-Mora, Issey and Pascual-deLaPuente, Santiago and Xavier Gir{\'o}-i-Nieto} } @conference {cLin15, title = {Time consistent estimation of End-effectors from RGB-D data}, booktitle = {Image and Video Technology: 7th Pacific-Rim Symposium, PSIVT 2015, Auckland, New Zealand, November 25-27, 2015, Revised Selected Papers}, number = {9431}, year = {2015}, month = {12/2015}, pages = {529-543}, publisher = {Springer International Publishing}, organization = {Springer International Publishing}, address = {Cham}, abstract = {

End-effectors are usually related to the location of the free end of a kinematic chain. Each of them contains rich structure information about the entity. Hence, estimating stable end-effectors of different entities enables robust tracking as well as a generic representation. In this paper, we present a system for end-effector estimation from RGB-D stream data. Instead of relying on a specific pose or configuration for initialization, we exploit time coherence without making any assumption with respect to the prior knowledge. This makes the estimation process more robust in a predict-update framework. Qualitative and quantitative experiments are performed against the reference method with promising results.

}, keywords = {End-effector estimation; Time coherence; Topology representation}, isbn = {978-3-319-29451-3}, doi = {10.1007/978-3-319-29451-3_42}, author = {X. Lin and Casas, J. and M. Pard{\`a}s} } @conference {cNiamut13, title = {Towards A Format-agnostic Approach for Production, Delivery and Rendering of Immersive Media}, booktitle = {ACM Multimedia Systems}, year = {2013}, month = {3/2013}, address = {Oslo, Norway}, abstract = {

The media industry is currently being pulled in the often-opposing directions of increased realism (high resolution, stereoscopic, large screen) and personalization (selection and control of content, availability on many devices). We investigate the feasibility of an end-to-end format-agnostic approach to support both these trends. In this paper, different aspects of a format- agnostic capture, production, delivery and rendering system are discussed. At the capture stage, the concept of layered scene representation is introduced, including panoramic video and 3D audio capture. At the analysis stage, a virtual director component is discussed that allows for automatic execution of cinematographic principles, using feature tracking and saliency detection. At the delivery stage, resolution-independent audiovisual transport mechanisms for both managed and unmanaged networks are treated. In the rendering stage, a rendering process that includes the manipulation of audiovisual content to match the connected display and loudspeaker properties is introduced. Different parts of the complete system are revisited demonstrating the requirements and the potential of this advanced concept.

}, author = {Niamut, O. and Kaiser, R. and Kienast, G. and Kochale, A. and Spille, J. and Schreer, O. and Ruiz-Hidalgo, J. and Macq, J. and Shirley, B.} } @conference {cMohedano12, title = {Teaching Students to Teach Computers}, booktitle = { International Congress on University Teaching and Innovation (CIDUI)}, year = {2012}, month = {07/2012}, abstract = {

This paper presents a Problem-Based Learning activity that introduce machine learning contents in a study plan that is based on electronics and signal processing subjects. The activity proposes students to follow the same evaluation protocols adopted in scientific challenges, where differents research groups test their own machine learning techniques on a common dataset and evaluation metrics. Teams of students adopt the role of a research group to develop their novel solution for classifying images depending on\ whether they represent a certain semantic concepts. Student must implement software solutions that are tested and presented in class to prove their performance and originalty. This paper includes a detailed evaluation of the workload and satisfaction activity, based on questionaries answered by the students.

}, keywords = {groups, machine learning, problem-based learning}, url = {http://hdl.handle.net/2117/16354}, author = {Mohedano, Eva and Xavier Gir{\'o}-i-Nieto} } @conference {cAlonso-Gonzalez12a, title = {Temporal polsar image series exploitation with binary partition trees}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2012}, year = {2012}, address = {Munich, Germany}, author = {Alonso-Gonz{\'a}lez, A. and L{\'o}pez-Mart{\'\i}nez, C. and Salembier, P.} } @mastersthesis {xVentura11, title = {Tools for Image Retrieval in Large Multimedia Databases}, year = {2011}, abstract = {

One of the challenges in the development of an image retrieval system is to achieve an efficient indexing scheme since both developers and users, who are used to make requests in order to find a multimedia element in a large database, can be frustrated due to the long computational time of the search.

The traditional indexing schemes neither fulfil the dynamic indexing requirement, which allows to add or remove elements from the structure, nor fit well in high dimensional feature spaces due to the phenomenon so called {\textquotedblleft}the curse of dimensionality.{\textquotedblright}

After analysing several indexing techniques from the literature, we have decided to implement an indexing scheme called Hierarchical Cellular Tree (HCT), which was designed to bring an effective solution especially for indexing large multimedia databases. The HCT has allowed to improve the performance of our implemented image retrieval system based on the MPEG-7 visual descriptors. We have also made some contributions by proposing some modifications to the original HCT which have resulted in an improvement of its performance. Thus, we have proposed a redefinition of the covering radius, which does not consider only the elements belonging to the cell, but also all the elements holding from that cell. Since this consideration implies a much more computationally costly algorithm, we have proposed an approximation by excess for the covering radius value. However, we have also implemented a method which allows to update the covering radius to its actual value whenever it is desired. In addition to this, the pre-emptive insertion method has been adapted as a searching technique in order to improve the performance given by the retrieval scheme called Progressive Query, which was originally proposed to be used over the HCT.

Furthermore, the HCT indexing scheme has been also adapted to a server/client architecture by using a messenger system called KSC, which allows to have the HCT loaded on a server waiting for the query requests which are launched for the several clients of the retrieval system. In addition to this, the tool used to request a search over the indexed database has been adapted to a graphic user interface, named GOS (Graphic Object Searcher), which allows the user to order the retrievals in a more friendly way.

[Thesis report in UPCommons]

}, url = {http://hdl.handle.net/2099.1/13011}, author = {Ventura, C.}, editor = {Xavier Gir{\'o}-i-Nieto and Ver{\'o}nica Vilaplana and Marqu{\'e}s, F.} } @mastersthesis {xSancho10, title = {Tweet@TV: Televisi{\'o} social en 140 car{\`a}cters}, year = {2010}, abstract = {

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

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

Cristina Ruiz Sancho, "Tweet@TV, la Televisi{\'o} Social en 140 car{\`a}cters" from Xavi Gir{\'o}
}, keywords = {interactive, microblogging, television, twitter}, url = {http://hdl.handle.net/2099.1/13523}, author = {Ruiz-Sancho, Cristina}, editor = {Xavier Gir{\'o}-i-Nieto and Cucurella, Eduard} } @mastersthesis {xVaras10, title = {Type of view estimation in football sequences}, year = {2010}, abstract = {

Due to the huge repercussion of football broadcast in society, an enormous number of applications can be derived to both analyze the match and enhance the visual experience of the spectator. These applications request semantical information about the content of the images. In particular, the type of view in a football image contains valuable information about the game. Thus, the type of view must be automatically computed to be able to process the large amount of information extracted from each football match. In this work, we propose a robust classification system that estimates the type ofview in football images in real time. For each frame of the sequence, a set of descriptors is extracted to characterize a specific part of the scene: the grass field. Gathering all these descriptors and a few ones related with texture, a decision tree determines the view that is shown in that frame. In order to improve the robustness of the algorithm, the redundancy of the temporal domain is exploited. The validity of the proposed algorithm has been tested on a large amount of frames from broadcasted football sequences in a wide variety of scenarios (stadiums, light conditions, ...). Promising results have been obtained with a 96\% of accuracy in the classification of these images.

}, author = {David Varas}, editor = {Marqu{\'e}s, F.} } @conference {cCanton-Ferrer09a, title = {Towards a low cost multi-camera marker based human motion capture system}, booktitle = {16th International Conference on Image Processing}, year = {2009}, pages = {2581{\textendash}2584}, isbn = {978-1-4244-5655-6}, doi = {10.1109/ICIP.2009.5413915}, url = {http://dx.doi.org/10.1109/ICIP.2009.5413915}, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @article {aChang09, title = {Trajectory tree as an object-oriented hierarchical representation for video}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {19}, number = {4}, year = {2009}, pages = {547{\textendash}560}, issn = {1051-8215}, author = {Chang, C. and M. Pard{\`a}s and Marqu{\'e}s, F.} } @conference {cGudmundsson08, title = {TOF Imaging in Smart Room Environments towards Improved People Tracking}, booktitle = {Computer Vision and Pattern Recognition: Workshop on Time of Flight based Computer Vision (CVPR/TOF-CV)}, year = {2008}, pages = {1{\textendash}6}, author = {Gudmundsson, S. and M. Pard{\`a}s and Casas, J. and Aanaes, H. and Larsen, R.} } @inbook {bCanton-Ferrer05, title = {Towards a Bayesian Approach to Robust Finding Correspondances in Multiple View Geometry Environments}, booktitle = {Computational Science {\textendash} ICCS 2005}, volume = {3515}, year = {2005}, pages = {281{\textendash}289}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

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

}, isbn = {978-3-540-26043-1}, doi = {10.1007/11428848_35 }, author = {Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cCanton-Ferrer05, title = {Towards a bayesian approach to robust finding correspondences in multiple view geometry environments}, booktitle = {Workshop on Computer Graphics and Geometric Modelling. Intrernational Conference on Computational Science}, year = {2005}, pages = {281{\textendash}289}, abstract = {

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

}, isbn = {3-540-26043-9}, author = {Cristian Canton-Ferrer and Cristian Canton-Ferrer and Casas, J. and M. Pard{\`a}s} } @conference {cRuiz-Hidalgo99, title = {Towards stereo from scale-trees}, booktitle = {7th International Conference on Image Processing and its Applications}, year = {1999}, pages = {52{\textendash}56}, author = {Moravec, K. and Ruiz-Hidalgo, J. and Harvey, R. and Bangham, J.} } @conference {cSalembier95 , title = {Texture coding using morphological interpolation}, booktitle = {IEEE workshop on Nonlinear Signal and Image Processing, NSIP 1995}, year = {1995}, address = {Halkidiki, Greece}, author = {Salembier, P. and Ru{\'e}, R} } @conference {cPardas94, title = {Time-recursive segmentation of image sequences}, booktitle = {European Signal Processing Conference, EUSIPCO-94}, year = {1994}, pages = {18{\textendash}21}, address = {Edinburgh, UK}, author = {M. Pard{\`a}s and Salembier, P.} } @conference {cMarques94, title = {Top-down 3d image sequence segmentation technique controlled by morphological}, booktitle = {EUSIPCO-94.}, year = {1994}, pages = {415{\textendash}418}, author = {Marqu{\'e}s, F. and Vera, V. and Gasull, A.} } @conference {cGasull92a, title = {T{\'e}cnicas de preprocesado para la segmentaci{\'o}n de im{\'a}genes}, booktitle = {U.R.S.I. 92}, year = {1992}, pages = {367{\textendash}371}, isbn = {84-600-8219-9}, author = {Gasull, A. and Marqu{\'e}s, F. and Montolio, P. and Torres, L.} } @conference {cGasull92b, title = {T{\'e}cnicas de preprocesado para la segmentaci{\'o}n de im{\'a}genes}, booktitle = {VII Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {1992}, pages = {367{\textendash}371}, author = {Gasull, A. and Marqu{\'e}s, F. and Torres, L.} } @conference {cOliveras91, title = {Tou - Brazo Robot Asistencial: Control Verbal}, booktitle = {2{\textordmasculine} Congreso de la Asociaci{\'o}n Espa{\~n}ola de Rob{\'o}tica}, year = {1991}, pages = {93{\textendash}100}, isbn = {0-7803-7043-0}, author = {Albert Oliveras and Fuertes, J. and Vill{\`a}, R.} } @conference {cLleida91, title = {Two level c.s.r. using demisyllable..}, booktitle = {EUROPEAN CONF. ON SPEECH COMMUN. AND TECHN. EUROSPEECH{\textquoteright}91}, year = {1991}, pages = {1199{\textendash}1202}, isbn = {10184074}, author = {Lleida, E. and Albert Oliveras and Nadeu, C. and Mari{\~n}o, J.} } @conference {cTorres90, title = {Temporal automatic edge detection of echocardiographic images}, booktitle = {ICASSP}, year = {1990}, pages = {2149{\textendash}2152}, isbn = {078030033}, author = {Torres, L. and Gasull, A.} }