@conference {cJimenezb, title = {Segmenting Invasive and In Situ Carcinoma in Breast WSIs with a Pretrained Histopathology Transformer}, booktitle = {2nd Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in breast care - MICCAI2025}, year = {In Press}, author = {Lauren Jimenez and Felip Vilardel and Ana Petit and Eduard Dorca and Teresa Soler-Mons{\'o} and Irma Ramos-Oliver and Laura Pons and Pedro L. Fern{\'a}ndez and Laia Adalid-Llansa and Daniel Mata and Lourdes Salazar-Huayna and Casas, J. and Ver{\'o}nica Vilaplana} } @inbook {bJimenez, title = {Breast Cancer Molecular Subtyping from H\&E Whole Slide Images using Foundation Models and Transformers}, booktitle = {Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care}, volume = {15451}, number = {Lecture Notes in Computer Science}, year = {2025}, publisher = {Springer}, organization = {Springer}, abstract = {

This study tackles the challenge of classifying breast cancer molecular subtypes using H\&E-stained Whole Slide Images (WSI), avoiding the cost and labor limitations of the commonly used immunohistochemistry. We leverage the Attention-Challenging Multiple Instance Learning framework and introduce a variant, ACTrans, which utilizes a transformer aggregator for improved performance. We also compare two publicly available foundation feature extractors pre-trained on large pathology datasets. A comparison of the impact of two different patch sizes at two different magnifications is made. The results obtained in our in-house dataset demonstrate that ACTrans outperforms existing methods, particularly with the UNI model at lower resolutions and larger patch sizes. In this setting, ACTrans achieves an average F1 score of 0.687, a precision of 0.755, a recall of 0.667, and an AUC of 0.812. Furthermore, these approaches enhance interpretability when displaying the attention weights. This method can potentially advance breast cancer diagnostics by leveraging the rich information within H\&E-stained WSIs.

}, author = {Lauren Jimenez and Carlos Hernandez and Ver{\'o}nica Vilaplana} } @conference {cPina25a, title = {Hierarchical Cell-to-Patch Graphs for Context-Aware Cell Classification in Digital Pathology}, booktitle = {21st European Congress on Digital Pathology}, year = {2025}, month = {06/2025}, publisher = {The European Society of Digital and Integrative Pathology}, organization = {The European Society of Digital and Integrative Pathology}, address = {Barcelona}, abstract = {

Cell classification in digital pathology requires spatial context beyond individual cell morphology, particularly for distinguishing tumor vs. normal cells in H\&E-stained images. While traditional segmentation and models like CellNuc-DETR achieve high detection accuracy, their reliance on small image patches limits classification performance by excluding tissue-level context. We propose a hierarchical cell-to-patch graph framework that integrates fine-grained cell features with global spatial information using graph neural networks (GNNs) to enhance classification accuracy while maintaining computational efficiency.

}, url = {https://www.ecdp2025.org/ecdp2025-program.pdf}, author = {Oscar Pina and Lauren Jimenez and Eduard Dorca and Ver{\'o}nica Vilaplana} } @conference {cLazaro25, title = {Verifying Key Performance Indicators across Multiple 6G-EWOC Demonstrations}, booktitle = {European Conference on Networks and Communications (EuCNC) }, year = {2025}, month = {06/2025}, publisher = {IEEE}, organization = {IEEE}, address = {Poznan, Poland}, abstract = {

Testing, measuring, and validating Key Performance Indicators (KPIs) are crucial tasks across SNS-JU projects. These activities foster collaboration at the TMV Working Group, enabling the sharing of methods, definitions, target values, and best practices within the 6G vision. Thanks to collaboration of multiple agents, objectives can be different although the main goal of the WG is to develop and mature 6G technology. The 6G-EWOC project aims to describe how KPIs will be verified in different categories through multiple demonstrations. These categories include data rate and capacity in several technologies, sensing, positioning and localization, and energy efficiency. By addressing these different KPIs, the 6G-EWOC project ensures comprehensive evaluation and validation, contributing to the progress of the 6G vision.

}, url = {European\%20Conference\%20on\%20Networks\%20and\%20Communications\%20(EuCNC)}, author = {Jos{\'e} Antonio L{\'a}zaro and Casas, J. and Santiago Royo and Ruiz-Hidalgo, J. and F{\`a}brega, J.M. and Ricardo Mart{\'\i}nez and Renaudier, J. and Mardoyan, H. and Jensen, J.B. and Altab{\'a}s, J.A. and Pablo Garc{\'\i}a-G{\'o}mez and Jordi Riu and Baskopoulos, P. and Kalavrouziotis, D. and Syrivelis, D. and Op de Beeck, C. and Theodoropoulou, E. and Lyberopoulos, G. and Thor, J.} } @conference {cJimeneza, title = {Breast Cancer Molecular Subtyping from H\&E Whole Slide Images using Foundation Models and Transformers}, booktitle = {Deep Breast Workshop on AI and Imaging for Diagnostic and Treatment Challenges in Breast Care, MICCAI 2024}, year = {2024}, month = {10/2024}, abstract = {

This study tackles the challenge of classifying breast cancer molecular subtypes using H\&E-stained Whole Slide Images (WSI), avoiding the cost and labor limitations of the commonly used immunohistochemistry. We leverage the Attention-Challenging Multiple Instance Learning framework and introduce a variant, ACTrans, which utilizes a transformer aggregator for improved performance. We also compare two publicly available foundation feature extractors pre-trained on large pathology datasets. A comparison of the impact of two different patch sizes at two different magnifications is made. The results obtained in our in-house dataset demonstrate that ACTrans outperforms existing methods, particularly with the UNI model at lower resolutions and larger patch sizes. In this setting, ACTrans achieves an average F1 score of 0.687, a precision of 0.755, a recall of 0.667, and an AUC of 0.812. Furthermore, these approaches enhance interpretability when displaying the attention weights. This method can potentially advance breast cancer diagnostics by leveraging the rich information within H\&E-stained WSIs.

}, author = {Lauren Jimenez and Carlos Hernandez and Ver{\'o}nica Vilaplana} } @conference {cHernandezb, title = {Bridging Domains in Melanoma Diagnostics: Predicting BRAF Mutations and Sentinel Lymph Node Positivity with Attention-Based Models in Histological Images}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (DEF-AI-MIA workshop)}, year = {2024}, month = {06/2024}, abstract = {

Whole Slide Images (WSIs) have significantly advanced the field of pathology by providing highly detailed views of tissue samples. Integrating Deep Learning (DL) into this area of research particularly through transformer-based foundational models has marked a new era in automated image analysis. These foundational models are adept at extracting features from WSIs an essential step in their analysis process. The subsequent application of weakly supervised learning techniques combines these features to predict critical biomarkers such as BRAF mutations and sentinel lymph node (SLN) biopsy positivity which are vital in guiding patient treatment strategies. However the limited availability of labelled datasets in pathology hinders the usefulness of DL models. Domain adaptation strategies adeptly overcome this hurdle enabling model knowledge transfer between different tissue types thus addressing data scarcity. Our study employs a form of domain adaptation by fine-tuning two DINOv2 models one pre-trained on natural images and the other on WSI of colorectal cancer from the TCGA dataset adapting them for melanoma analysis. We also incorporate a comparison with features extracted by a third DINOv1 model trained solely on WSIs of breast cancer. With this approach we find some notable success in detecting BRAF mutations. Nonetheless predicting SLN positivity presents a more intricate challenge largely due to the indirect correlation between local histopathological features in WSIs of primary tumours and lymph node metastasis manifestation.\ 

}, author = {Carlos Hernandez and Lauren Jimenez and Ver{\'o}nica Vilaplana} } @conference {cLozano23, title = {Optical Phased Array Antenna Apodization for Lidar in Autonomous Vehicles}, booktitle = {XIII Reuni{\'o}n OptoElectr{\'o}nica (OPTOEL 2023)}, year = {2023}, month = {06/2023}, publisher = {OPTOEL}, organization = {OPTOEL}, address = {Sevilla, Spain}, abstract = {

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

}, url = {https://www.optoel2023.es/94299/section/44205/optoel-2023.html}, author = {Jos{\'e} Lozano and Humberto Jim{\'e}nez and Sergio Torres and Pau Biosca and Bernat Fontanet and Jorge Pinazo and Adolfo Ler{\'\i}n and Federico Dios and Casas, J. and Jos{\'e} Antonio L{\'a}zaro} } @conference {cGene-Mola23, title = {Video-Based Fruit Detection and Tracking for Apple Counting and Mapping}, booktitle = {IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)}, year = {2023}, month = {11/2023}, doi = {10.1109/MetroAgriFor58484.2023.10424135}, author = {Gen{\'e}-Mola, Jordi and Felip-Pom{\'e}s, M. and Net-Barn{\'e}s, F. and Morros, J.R. and Miranda, J. C. and Arn{\'o} Satorra, J. and As{\'\i}n Jones, L. and Lordan Sanahuja, J. and Ruiz-Hidalgo, J. and Gregorio, Eduard} } @conference {cPinazo22, title = {Perception in the era of Autonomous Vehicles}, booktitle = {Photonics 4 Smart Cities, SCEWC 2022}, year = {2022}, month = {11/2022}, publisher = {Photonics21}, organization = {Photonics21}, address = {Barcelona}, url = {https://www.fotonica21.org/photonics-4-smart-cities}, author = {Jorge Pinazo and Adolfo Ler{\'\i}n and Francesc Xavier de Gibert and {\'A}lvaro Moliner and Daniel Sevilla and Antonio Jurado and Iv{\'a}n R{\'\i}os and Rodrigo Jerez and Jaime Santiago and {\'A}lvaro Linuesa and Antonio Cano and Federico Dios and Adolfo Comer{\'o}n and Casas, J. and Jos{\'e} Antonio L{\'a}zaro} } @conference {cPuig-Sitjes21a, title = {Real-time detection of overloads on the PFCs of W7-X}, booktitle = {European Conference on Plasma Diagnostics (ECPD 2021)}, year = {2021}, month = {06/2021}, publisher = {CLPU}, organization = {CLPU}, address = {online}, abstract = {

Wendelstein 7-X (W7-X) is the leading experiment on the path of demonstrating that stellarators are a feasible concept for a future power plant. One of its main goals is to prove quasi-steady-state operation in a reactor-relevant parameter regime. To this end, the surveillance and protection of the water-cooled plasma facing components (PFCs) against overheating is fundamental to guarantee a safe steady-state high-heat-flux operation. The fast reaction times required to prevent damage to the device makes imperative to fully automate the analysis of the thermographic images of the PFCs to detect the thermal events in real-time and timely interrupt operation through the interlock system if a critical event is detected. This imaging system, being the last line of defense, must be based on highly reliable image and risk analysis techniques to guarantee the integrity of the device under all circumstances. During the past operation phases, W7-X was equipped with inertially-cooled test divertor units and although the system still required manual supervision, several concepts to deal with the intrinsic 3D geometry of stellarators were put under test. With the experience gained, a new PFC protection system has been designed based on advanced image segmentation techniques. In order to achieve steady-state operation, however, a feedback control system able to prevent unnecessary interruptions of plasma operation is needed. The feedback control shall be guided by the image analysis, requiring a high-level scene understanding by means of computer vision and machine learning algorithms. Based on this analysis, the feedback control can then take the most effective counter measures to prevent the PFCs overloading by acting on the heating systems, the strike-line shape and positioning or on plasma detachment. In this work, we present the design of the real-time image analysis system for the protection of the PFCs of W7-X through the Interlock, and its validation results based on the analysis of the extensive existing experimental data. Additionally, we present the roadmap towards a feedback control system for the protection of the device in long high-performance plasmas.

}, url = {https://www.clpu.es/en/ECPD2021_Schedule/}, author = {Puig-Sitjes, A. and Jakubowski, M. and Gao, Y. and Drewelow, P. and Niemann, H. and Fellinger, J. and Moncada, V. and Pisano, F. and Belafdil, C. and Mitteau, R. and Aumeunier, M.-H. and Cannas, B. and Casas, J. and Salembier, P. and Clemente, R. and Dumke, S. and Winter, A. and Laqua, H. and Bluhm, T. and Brandt, K.} } @article {aPuig-Sitjes21, title = {Real-time detection of overloads on the plasma-facing components of Wendelstein 7-X}, journal = {Applied sciences (Basel)}, volume = {11}, year = {2021}, month = {12/2021}, chapter = {1}, issn = {2076-3417}, doi = {10.3390/app112411969}, url = {http://hdl.handle.net/2117/361558}, author = {Puig-Sitjes, A. and Jakubowski, M. and Naujoks, D. and Gao, Y. and Drewelow, P. and Niemann, H. and Felinger, J. and Casas, J. and Salembier, P. and Clemente, R.} } @conference {cPuig-Sitjes21, title = {Spatio-temporal Detection and Tracking of Thermal Events on the Plasma Facing Components of Wendelstein 7-X}, booktitle = {4th IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis}, year = {2021}, month = {11/2021}, publisher = {iaea.org}, organization = {iaea.org}, address = {Shanghai (online)}, abstract = {

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

}, url = {https://conferences.iaea.org/event/251/contributions/20704/}, author = {Puig-Sitjes, A. and Casas, J. and Salembier, P. and Vizcarro, D. and Clemente, R. and Jakubowski, M. and Gao, Y. and W7-X Team} } @conference {cPuig-Sitjes20, title = {Strategy for the real-time detection of thermal events on the plasma facing components of Wendelstein 7-X}, booktitle = {31st Symposium on Fusion Technology (SOFT2020)}, year = {2020}, month = {09/2020}, address = {Dubrovnik, Croatia}, abstract = {

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

}, url = {http://hdl.handle.net/21.11116/0000-0007-7FF5-7}, author = {Puig-Sitjes, A. and Jakubowski, M. and Fellinger, J. and Drewelow, P. and Gao, Y. and Niemann, H. and Sunn-Pedersen, T. and K{\"o}nig, R. and Naujoks, D. and Winter, A. and Laqua, H. and Dumke, S. and Moncada, V. and Belafdil, C. and Mitteau, R. and Aumeunier, M.-H. and Pisano, F. and Aymerich, E. and Cannas, B. and Kocsis, G. and Szepesi, T. and Cseh, G. and Szabolics, T. and Casas, J. and Morros, J.R. and Salembier, P. and Clemente, R. and Cobos, M. and I. Caminal and Palacios Corral, A. and Moreno Punzano, A. and Quiceno Lopera, S.} } @article {aRoisman18, title = {Differential expression of long non-coding RNAs related to proliferation and histological diversity in follicular lymphomas}, journal = {British Journal of Haematology}, volume = {184}, year = {2019}, month = {Feb 2019}, pages = {373-383}, issn = {ISSN:1365-2141}, doi = {DOI: 10.1111/bjh.15656}, author = {A. Roisman and A. Navarro and G. Clot and G. Castellano and B. Gonzalez-Farre and P. P{\'e}rez-Galan and A. Esteve and M. Dabad and S. Heath and M. Gut and Bosio, M. and P. Bellot and Salembier, P. and Albert Oliveras and I. Slavusky and L. Magnano and H. Horn and A. Rosenwald and G. Ott and M. Aymerich and A. L{\'o}pez-Guillermo and P. Jares and J.I. Mart{\'\i}n-Subero and E. Campo and L. Hern{\'a}ndez} } @article {aKuijf, title = {Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge}, journal = {IEEE Transactions on Medical Imaging}, year = {2019}, month = {03/2019}, abstract = {

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

}, keywords = {brain, Evaluation and performance, Magnetic resonance imaging (MRI), segmentation}, issn = {0278-0062}, doi = {10.1109/TMI.2019.2905770}, author = {Hugo Kuijf and Matthijs Biesbroek and Jeroen de Bresser and Rutger Heinen and Simon Andermatt and Mariana Bento and Matt Berseth and Mikhail Belyaev and Jorge Cardoso and Adri{\`a} Casamitjana and Louis Collins and Mahsa Dadar and Achileas Georgiou and Mohsen Ghafoorian and Dakai Jin and April Khademi and Jesse Knight and Hongwei Li and Xavier Llado and Miguel Luna and Qaiser Mahmood and Richard McKinley and Alireza Mehrtash and Sebastien Ourselin and Bo-yong Park and Hyunkin Park and Sang Hyun Park and Simon Pezold and Elodie Puybareau and Leticia Rittner and Carole Sudre and Sergi Valverde and Ver{\'o}nica Vilaplana and Rolan Wiest and Yongchao Xu and Ziyue Xu and Guodong Zeng and Jianguo Zhang and Guoyan Zheng and Christoper Chen and Wiesje van der Flier and Frederik Barkhof and Max Viergever and Geert Jan Biessels} } @conference {cBakas19, title = {Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge}, booktitle = {MICCAI - Multimodal Brain Tumor Segmentation Challenge}, year = {2018}, month = {12/2018}, abstract = {

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

}, url = {https://arxiv.org/pdf/1811.02629.pdf}, author = {Spyridon Bakas and Mauricio Reyes and Andras Jakab and Stefan Bauer and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and et. Al.} } @inbook {bCampos, title = {Sentiment concept embedding for visual affect recognition}, booktitle = {Multimodal Behavior Analysis in theWild}, year = {2018}, publisher = {Elsevier}, organization = {Elsevier}, edition = {1}, chapter = {16}, abstract = {

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

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

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

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks from Xavier Giro-i-Nieto
}, author = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Jordi Torres and Chang, Shih-Fu} } @mastersthesis {xJimenez, title = {Class Weighted Convolutional Features for Image Retrieval}, year = {2017}, abstract = {

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

Grade: A with honours (10.0/10.0)

Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or ne-tuning a model every time new images are added to the database is neither ecient nor scalable. Convolutional Neural Networks trained for image classi cation over large datasets have been proven e ective feature extractors when transferred to the task of image retrieval. The most successful approaches are based in encoding the activations of convolutional layers as they convey the image spatial information. Our proposal goes beyond and aims at a local-aware encoding of these features depending on the predicted image semantics, with the advantage of using only of the knowledge contained inside the network. In particular, we employ Class Activation Maps (CAMs) to obtain the most discriminative regions of the image from a semantic perspective. Additionally, CAMs are also used to generate object proposals during an unsupervised re-ranking stage after a rst fast search. Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate that our system is competitive and even outperforms the current state-of-the-art when using o -the-shelf models trained on the object classes of ImageNet.

[Project page]

Class Weighted Convolutional Features for Image Retrieval from Xavier Giro-i-Nieto
}, keywords = {Convolutional Neural Networks, deep learning, image retrieval, Transfer Learning, Visual Instance Search}, author = {Jim{\'e}nez, Albert}, editor = {Xavier Gir{\'o}-i-Nieto and {\'A}lvarez, Jose M} } @conference {cJimenez, title = {Class Weighted Convolutional Features for Visual Instance Search}, booktitle = {28th British Machine Vision Conference (BMVC)}, year = {2017}, month = {09/2017}, address = {London, UK}, abstract = {

Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images.\ In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable.\ Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors when transferred to the task of image retrieval. The most successful approaches are based on encoding the activations of convolutional layers, as they convey the image spatial information. \ Our proposal goes beyond and aims at a local-aware encoding of these features depending on the predicted image semantics, with the advantage of using only of the knowledge contained inside the network.\ In particular, we employ Class Activation Maps (CAMs) to obtain the most discriminative regions from a semantic perspective. Additionally, CAMs are also used to generate object proposals during an unsupervised re-ranking stage after a first fast search.\ Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate that our system is competitive and even outperforms the current state-of-the-art when using off-the-shelf models trained on the object classes of ImageNet.

Class Weighted Convolutional Features for Image Retrieval from Xavier Giro-i-Nieto
}, author = {Jim{\'e}nez, Albert and {\'A}lvarez, Jose M and Xavier Gir{\'o}-i-Nieto} } @article {xCampos, title = {From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction}, journal = {Image and Vision Computing}, year = {2017}, abstract = {

Visual multimedia have become an inseparable part of our digital social lives, and they often capture moments tied with deep affections. Automated visual sentiment analysis tools can provide a means of extracting the rich feelings and latent dispositions embedded in these media. In this work, we explore how Convolutional Neural Networks (CNNs), a now de facto computational machine learning tool particularly in the area of Computer Vision, can be specifically applied to the task of visual sentiment prediction. We accomplish this through fine-tuning experiments using a state-of-the-art CNN and via rigorous architecture analysis, we present several modifications that lead to accuracy improvements over prior art on a dataset of images from a popular social media platform. We additionally present visualizations of local patterns that the network learned to associate with image sentiment for insight into how visual positivity (or negativity) is perceived by the model.

}, doi = {http://dx.doi.org/10.1016/j.imavis.2017.01.011}, url = {http://arxiv.org/abs/1604.03489}, author = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCampos17, title = {Learning to Skip State Updates in Recurrent Neural Networks}, year = {2017}, abstract = {

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

Grade: A with honours (10.0/10.0)

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

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks from Xavier Giro-i-Nieto
}, keywords = {conditional computation, deep learning, machine learning, recurrent neural networks, sequence modeling}, url = {https://imatge-upc.github.io/skiprnn-2017-telecombcn/}, author = {V{\'\i}ctor Campos}, editor = {Jou, Brendan and Chang, Shih-Fu and Xavier Gir{\'o}-i-Nieto} } @conference {cGurrina, title = {LTA 2017: The Second Workshop on Lifelogging Tools and Applications}, booktitle = {ACM Multimedia}, year = {2017}, month = {10/2017}, publisher = {ACM}, organization = {ACM}, address = {Mountain View, California USA}, abstract = {

The organisation of personal data is receiving increasing research attention due to the challenges we face in gathering, enriching, searching, and visualising such data. \ Given the increasing ease with which personal data being gathered by individuals, the concept of a lifelog digital library of rich multimedia and sensory content for every individual is fast becoming a reality. \ The LTA~2017 workshop aims to bring together academics and practitioners to discuss approaches to lifelog data analytics and applications; and to debate the opportunities and challenges for researchers in this new and challenging area.

}, doi = {10.1145/3123266.3132050}, author = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Dimiccoli, M. and Dang-Nguyen, Duc-Tien and Joho, H.} } @conference {cFernandez, title = {More cat than cute? Interpretable Prediction of Adjective-Noun Pairs}, booktitle = {ACM Multimedia 2017 Workshop on Multimodal Understanding of Social, Affective and Subjective Attributes}, year = {2017}, month = {10/2017}, publisher = {ACM SIGMM}, organization = {ACM SIGMM}, address = {Mountain View, CA (USA)}, abstract = {

The increasing availability of affect-rich multimedia resources has bolstered interest in understanding sentiment and emotions in and from visual content. Adjective-noun pairs (ANP) are a popular mid-level semantic construct for capturing affect via visually detectable concepts such as {\textquoteleft}{\textquoteleft}cute dog" or {\textquoteleft}{\textquoteleft}beautiful landscape". Current state-of-the-art methods approach ANP prediction by considering each of these compound concepts as individual tokens, ignoring the underlying relationships in ANPs. This work aims at disentangling the contributions of the {\textquoteleft}adjectives{\textquoteright} and {\textquoteleft}nouns{\textquoteright} in the visual prediction of ANPs. Two specialised classifiers, one trained for detecting adjectives and another for nouns, are fused to predict 553 different ANPs. The resulting ANP prediction model is more interpretable as it allows us to study contributions of the adjective and noun components.

}, doi = {10.1145/3132515.3132520}, author = {Fern{\`a}ndez, D{\`e}lia and Woodward, Alejandro and V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Chang, Shih-Fu} } @conference {cCampos, title = {Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks}, booktitle = {NIPS Time Series Workshop 2017}, year = {2017}, month = {08/2017}, address = {Long Beach, CA, USA}, abstract = {

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

Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks from Xavier Giro-i-Nieto
}, url = {https://imatge-upc.github.io/skiprnn-2017-telecombcn/}, author = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Jordi Torres and Chang, Shih-Fu} } @mastersthesis {xFernandez, title = {Clustering and Prediction of Adjective-Noun Pairs for Affective Computing}, year = {2016}, abstract = {

Student: D{\`e}lia Fern{\`a}ndez

Advisors: V{\'\i}ctor Campos (UPC), Brendan Jou (Columbia University), Xavier Gir{\'o}-i-Nieto (UPC) and Shih-Fu Chang (Columbia University)

Grade: A+ (10.0/10.0) - Best Master Thesis award (Class 2016)

One of the main problems in visual affective computing is overcoming the affective gap between low-level visual features and the emotional content of the image. One rising method to capture visual affection is through the use of Adjective-Noun Pairs (ANP), a mid-level affect representation. This thesis addresses two challenges related to ANPs: representing ANPs in a structured ontology and improving ANP detectability. The first part develops two techniques to exploit relations between adjectives and nouns for automatic ANP clustering. The second part introduces and analyzes a novel deep neural network for ANP prediction. Based on the hypothesis of a different contribution of the adjective and the noun depending of the ANP, the novel network fuses the feature representations of adjectives and nouns from two independently trained convolutional neural networks.

}, author = {Fern{\`a}ndez, D{\`e}lia}, editor = {V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Chang, Shih-Fu} } @unpublished {xFernandeza, title = {Is a {\textquotedblleft}happy dog{\textquotedblright} more {\textquotedblleft}happy{\textquotedblright} than {\textquotedblleft}dog{\textquotedblright}? - Adjective and Noun Contributions for Adjective-Noun Pair prediction}, journal = {NIPS Women in Machine Learning Workshop}, year = {2016}, month = {12/2016}, address = {Barcelona}, abstract = {

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

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

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

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

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

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

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

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

}, author = {Fern{\`a}ndez, D{\`e}lia and V{\'\i}ctor Campos and Jou, Brendan and Xavier Gir{\'o}-i-Nieto and Chang, Shih-Fu} } @conference {cGurrin, title = {LTA 2016 - The First Workshop on Lifelogging Tools and Applications}, booktitle = {ACM Multimedia}, year = {2016}, month = {10/2016}, publisher = {ACM}, organization = {ACM}, address = {Amsterdam, The Netherlands}, abstract = {

The organisation of personal data is receiving increasing research attention due to the challenges that are faced in gathering, enriching, searching and visualising this data. Given the increasing quantities of personal data being gathered by individuals, the concept of a lifelog digital library of rich multimedia and sensory content for every individual is fast becoming a reality. The LTA2016 lifelogging workshop at ACM MM 2016 aims to bring together academics and practitioners to discuss approaches to lifelog data analytics and the applications of same, and to debate the opportunities and challenges for researchers in this new and challenging area.

[Workshop web page]

[Workshop proceedings]

[UPCommons]

}, keywords = {lifelogging, Personal digital archives, Personal information management}, doi = {http://dx.doi.org/10.1145/2964284.2980534}, url = {http://lta2016.computing.dcu.ie/}, author = {Gurrin, Cathal and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Dimiccoli, M. and Johansen, H. and Joho, H. and Singh, Vivek K} } @conference {cCampos-Camunez, title = {Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction}, booktitle = {1st International Workshop on Affect and Sentiment in Multimedia}, year = {2015}, month = {10/2015}, publisher = {ACM}, organization = {ACM}, address = {Brisbane, Australia}, abstract = {

Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose. In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting. Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.

Acceptance rate at the ASM workshop{\textquoteright}15: 56\% (9/16) [source]

Diving deep into sentiment: Understanding fine-tuned CNNs for visual sentiment prediction from Xavier Giro

Visual Sentiment Detection by Deep CNNs $\#$acmmm15 @DocXavi @mad_astronaut @brendanjou - great paper/analysis! pic.twitter.com/RcGchcPA9f

{\textemdash} Damian Borth (@damianborth) October 30, 2015
}, doi = {10.1145/2813524.2813530}, url = {http://arxiv.org/abs/1508.05056}, author = {V{\'\i}ctor Campos and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Jou, Brendan} } @conference {cMcGuinnessa, title = {Insight DCU at TRECVID 2015}, booktitle = {TRECVID 2015 Workshop}, year = {2015}, month = {11/2015}, publisher = {NIST}, organization = {NIST}, address = {Gaithersburg, MD, USA}, abstract = {

Insight-DCU participated in the instance search (INS), semantic indexing (SIN), and localization tasks (LOC) this year.

In the INS task we used deep convolutional network features trained on external data and the query data for this year to train our system. We submitted four runs, three based on convolutional network features, and one based on SIFT/BoW. F A insightdcu 1 was an automatic run using features from the last convolutional layer of a deep network with bag-of-words encoding and achieved 0.123 mAP. F A insightdcu 2 modi ed the previous run to use re-ranking based on an R-CNN model and achieved 0.111 mAP. I A insightdcu 3, our interactive run, achieved 0.269 mAP. Our SIFT-based run F A insightdcu 2 used weak geometric consistency to improve performance over the previous year to 0.187 mAP. Overall we found that using features from the convolutional layers improved performance over features from the fully connected layers used in previous years, and that weak geometric consistency improves performance for local feature ranking.

In the SIN task we again used convolutional network features, this time netuning a network pretrained on external data for the task. We submitted four runs, 2C D A insightdcu.15 1..4 varying the top-level learning algorithm and use of concept co-occurance. 2C D A insightdcu.15 1 used a linear SVM top-level learner, and achieved 0.63 mAP. Exploiting concept co-occurance improved the accuracy of our logistic regression run 2C D A insightdcu.15 3 from 0.058 mAP to 0.6 2C D A insightdcu.15 3.

Our LOC system used training data from IACC.1.B and features similar to our INS run, but using a VLAD encoding instead of a bag-of-words. Unfortunately there was problem with the run that we are still investigating.

Note: UPC and NII participated only in the INS task of this submission.

}, url = {http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.15.org.html}, author = {Kevin McGuinness and Mohedano, Eva and Amaia Salvador and Zhang, ZhenXing and Marsden, Mark and Wang, Peng and Jargalsaikhan, Iveel and Antony, Joseph and Xavier Gir{\'o}-i-Nieto and Satoh, Shin{\textquoteright}ichi and O{\textquoteright}Connor, N. and Smeaton, Alan F.} } @mastersthesis {xCampos-Camunez, title = {Layer-wise CNN Surgery for Visual Sentiment Prediction}, year = {2015}, abstract = {

Advisors: Amaia Salvador (UPC), Brendan Jou (Columbia University) and Xavier Gir{\'o}-i-Nieto (UPC)

Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose. In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting. Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.

Layer-wise CNN Surgery for Visual Sentiment Prediction from Xavier Giro

}, author = {V{\'\i}ctor Campos}, editor = {Amaia Salvador and Jou, Brendan and Xavier Gir{\'o}-i-Nieto} } @conference {cJimenez12, title = {Registration of Multi-Modal Neuroimaging Datasets by Considering the Non-Overlapping Field of View into the NMI Calculation}, booktitle = {IEEE International Symposium on Biomedical Imaging, ISBI 2012}, year = {2012}, address = {Barcelona, Spain}, author = {Jim{\'e}nez, X and Figueiras, F and Marqu{\'e}s, F. and Salembier, P. and Herance, R and Rojas, S and Mill{\'a}n, O and Pareto, D and Domingo Gispert, J} } @inbook {bDescamps09, title = {How does digital cinema compress images?}, booktitle = {Applied signal processing}, year = {2009}, pages = {361{\textendash}410}, isbn = {978-0-387-74534-3}, url = {http://cataleg.upc.edu/record=b1353617~S1*cat}, author = {Descamps, A. and De Vleeschouwer, C. and Jacques, L. and Marqu{\'e}s, F.}, editor = {Dutoit, T. and Marqu{\'e}s, F.} } @conference {cVilaplana06, title = {Face Recognition Using Groups of Images in Smart Room Scenarios}, booktitle = {International Conference on Image Processing}, year = {2006}, pages = {2071{\textendash}2074}, author = {Ver{\'o}nica Vilaplana and Mart{\'\i}nez, C. and Javier, C. and Marqu{\'e}s, F.} } @conference {cAnton05, title = {Collaborative Network Space: Infrastructure and Learning Application}, booktitle = {IEEE Region 8 EUROCON 2005 Conference: Computer as a tool.}, year = {2005}, pages = {803{\textendash}806}, isbn = {1-4244-0050-3}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=01630054}, author = {Anton, M. and Vall-Llosera, M. and Jordi Torres and Romeu, J. and Jofre, L. and Sole, F. and Marqu{\'e}s, F. and Sabate, F. and Berenguer, J.} } @conference {cMarques05, title = {El concepto NetCampus}, booktitle = {3es Jornadas de la C{\'a}tedra Telef{\'o}nica-UPC}, year = {2005}, pages = {15{\textendash}20}, url = {https://catedratelefonica.upc.edu/documents/llibres/docs/el_espacio_innovador_y_la_red.pdf}, author = {Marqu{\'e}s, F. and Jofre, L. and Sole, F. and Sabate, F. and Berenguer, J. and Romeu, J. and Jordi Torres} } @book {eJofre05, title = {El "Espacio Innovador" y la red}, year = {2005}, url = {http://www.upc.edu/web/CatedraTelefonicaUPC}, author = {Jofre, L. and Sole, F. and Sabate, F. and Berenguer, J. and Marqu{\'e}s, F. and Romeu, J. and Jordi Torres} } @book {eJofre04, title = {El {\textquoteright}Profesional Innovador{\textquoteright} y la red}, year = {2004}, url = {http://catedratelefonica.upc.edu/documents/llibres/docs/jornada_2004_catedra_telf_upc.pdf}, author = {Jofre, L. and Sole, F. and Sabate, F. and Marqu{\'e}s, F. and Romeu, J. and Jordi Torres} } @conference {cJofre04, title = {Una Enginyeria per a la Societat del Coneixement}, booktitle = {II Congr{\'e}s d{\textquoteright}Enginyeria en Llengua Catalana}, year = {2004}, url = {http://www.eicc.cat/celc/formacio.htm}, author = {Jofre, L. and Sole, F. and Sabate, F. and Marqu{\'e}s, F. and Romeu, J. and Jordi Torres} } @conference {cGasull02, title = {Oil Spills Detection in SAR Images using Mathematical Morphology}, booktitle = {11th European Signal Processing Conference (EUSIPCO 2002)}, year = {2002}, pages = {25{\textendash}28}, author = {Gasull, A. and F{\'a}bregas, F.X. and Jim{\'e}nez, J. and Marqu{\'e}s, F. and Moreno, V. and Herrero, M.} } @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.} } @article {aSalembier97, title = {Segmentation-based video coding system allowing the manipulation of objects}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {7}, number = {1}, year = {1997}, pages = {60{\textendash}74}, issn = {1051-8215}, doi = {10.1109/76.554418}, author = {Salembier, P. and Marqu{\'e}s, F. and M. Pard{\`a}s and Morros, J.R. and Corset, I. and Jeannin, S. and Bouchard, L. and Meyer, F. and Marcotegui, B.} } @conference {cMarques96, title = {A segmentation-based coding system allowing manipulation of objects}, booktitle = {IEEE International Conference on Image Processing, ICIP 1996}, year = {1996}, pages = {145{\textendash}175}, address = {Lausanne, Switzerland}, abstract = {

We present a coding scheme that achieves, for each image in the sequence, the best segmentation in terms of rate-distortion theory. It is obtained from a set of initial regions and a set of available coding techniques. The segmentation combines spatial and motion criteria. It selects at each area of the image the most adequate criterion for defining a partition in order to obtain the best compromise between cost and quality. In addition, the proposed scheme is very suitable for addressing content-based functionalities.

}, isbn = {0-7803-3258-X}, doi = {10.1109/ICIP.1996.560741}, author = {Marqu{\'e}s, F. and Salembier, P. and M. Pard{\`a}s and Morros, J.R. and Corset, I. and Jeannin, S. and Marcotegui, B. and Meyer, F.} } @article {pSalembier96a, title = {Segmented Picture Coding Method and System and Corresponding Decoding Method and System}, number = {964009161-}, year = {1996}, type = {Invention patent}, author = {Salembier, P. and Marqu{\'e}s, F. and Corset, I. and Bouchard, L. and Jeannin, S. and M. Pard{\`a}s and Morros, J.R. and Meyer, F. and Marcotegui, B.} } @conference {cMarques94c, title = {Interleaved segmentation and motion estimation by means of morphological tools}, booktitle = {Workshop on Image Analysis and Synthesis in Image Coding}, year = {1994}, address = {Berlin, Germany}, author = {Marqu{\'e}s, F. and Bouchard, L. and Corset, I. and Jeannin, S. and Morros, J.R. and M. Pard{\`a}s and Salembier, P. and Torres, L.} } @conference {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 {cSalembier91, title = {Adaptive morphological multiresolution decomposition}, booktitle = {SPIE Image Algebra and Mathematical Morphology}, year = {1991}, pages = {620{\textendash}631}, address = {San Diego, USA}, author = {Salembier, P. and Jaquenoud, L} }