@mastersthesis {dGeleta21, title = {Unsupervised learning with applications in genomic}, volume = {BSc Data Science Engineering}, year = {2021}, abstract = {

In this study we show the power of variational autoencoders (VAEs) for a variety of tasks relating to the interpretation and compression of genomic data. The unsupervised setting allows for detecting and learning of granular population structure and inferring of new informative latent factors, opening up an avenue for applications in dimensionality reduction, data simulation, population classification, imputation, and lossless genomic data compression. The latent spaces of VAEs are able to capture and represent clearly differentiated Gaussian-like clusters of similar genetic composition on a fine-scale with a relatively small number of Single Nucleotide Polymorphisms (SNPs) as input. Furthermore, sequences can be decomposed into latent representations and reconstruction errors (residuals) providing a sparse representation that provides a means for efficient lossless compression.

Identifying genetic clusters can be important when performing genome-wide association studies and provides an alternative to self-reported ethnic labels, which are culturally constructed and vary according to the location and individual. A variety of unsupervised dimensionality reduction methods have been explored in the past for such applications, including PCA, MDS, t-SNE, and UMAP. Our proposed VAE can represent the population structure as a Gaussian-distributed continuous multi-dimensional representation and as classification probabilities providing flexible and interpretable population descriptors.

We train our VAE method with several worldwide whole genome datasets from both humans and canids, and evaluate the performance of the different proposed applications with networks with and without ancestry conditioning. Our experiments show that different population groups have significantly differentiated compression ratios and classification accuracies. Additionally, we analyze the entropy of the SNP data, noting its effect on compression across populations and connect these patterns to historical migrations and ancestral relationships.

}, author = {Geleta, Margarita}, editor = {Mas-Montserrat, Daniel and Ioannidis, Alexander G. and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCreus, title = {Unsupervised skill learning from pixels}, year = {2021}, abstract = {

This work focuses on the self-acquirement of the fundamental task-agnostic knowledge available within an environment. The aim is to discover and learn baseline representations and behaviours that can later be useful for solving embodied visual navigation downstream tasks. Specifically, the presented approach extends the idea of the "Explore, Discover and Learn" (EDL) paradigm to the pixel domain. This way, this work is centered in the representations and behaviours that can be learnt by an agent that only integrates an image capture sensor. Both the agents and the environment that is used in this work run over the Habitat AI simulator, which is developed by Facebook AI, and renders 3D fotorealistic views of the insides of apartments.





}, author = {Creus, Roger}, editor = {Nieto, Juan Jos{\'e} and Xavier Gir{\'o}-i-Nieto} } @conference {cNietoa, title = {Unsupervised Skill-Discovery and Skill-Learning in Minecraft}, booktitle = {ICML 2021 Workshop on Unsupervised Reinforcement Learning (URL)}, year = {2021}, month = {07/2021}, abstract = {

Pre-training Reinforcement Learning agents in a task-agnostic manner has shown promising results. However, previous works still struggle in learning and discovering meaningful skills in high-dimensional state-spaces, such as pixel-spaces. We approach the problem by leveraging unsupervised skill discovery and self-supervised learning of state representations. In our work, we learn a compact latent representation by making use of variational and contrastive techniques. We demonstrate that both enable RL agents to learn a set of basic navigation skills by maximizing an information theoretic objective. We assess our method in Minecraft 3D pixel maps with different complexities. Our results show that representations and conditioned policies learned from pixels are enough for toy examples, but do not scale to realistic and complex maps. To overcome these limitations, we explore alternative input observations such as the relative position of the agent along with the raw pixels.

}, author = {Nieto, Juan Jos{\'e} and Creus, Roger and Xavier Gir{\'o}-i-Nieto} } @conference {cCombaliad, title = {Uncertainty Estimation in Deep Neural Networks for Dermoscopic Image Classification}, booktitle = {CVPR 2020, ISIC Skin Image Analysis Workshop}, year = {2020}, month = {2020}, abstract = {

The high performance of machine learning algorithms for the task of skin lesion classification has been shown over the past few years. However, real-world implementations are still scarce. One of the reasons could be that most methods do not quantify the uncertainty in the predictions and are not able to detect data that is anomalous or significantly different from that used in training, which may lead to a lack of confidence in the automated diagnosis or errors in the interpretation of results.

In this work, we explore the use of uncertainty estimation techniques and metrics for deep neural networks based on Monte-Carlo sampling and apply them to the problem of skin lesion classification on data from ISIC Challenges 2018 and 2019.

Our results show that uncertainty metrics can be successfully used to detect difficult and out-of-distribution samples.

}, author = {Marc Combalia and Ferran Hueto and Susana Puig and Josep Malvehy and Ver{\'o}nica Vilaplana} } @inbook {bBellot19, title = {Unsupervised GRN Ensemble}, booktitle = {Sanguinetti G., Huynh-Thu V. (eds) Methods in Molecular Biology }, volume = {1883}, year = {2019}, pages = {283-302}, publisher = { Springer science, Humana Press}, organization = { Springer science, Humana Press}, address = {New York, NY}, issn = {978-1-4939-8881-5}, doi = {https://doi-org.recursos.biblioteca.upc.edu/10.1007/978-1-4939-8882-2}, author = {P. Bellot and Salembier, P. and Pham, N.C. and Meyer, P. E.} } @conference {cGene-Mola19, title = {Uso de redes neuronales convolucionales para la detecci{\'o}n remota de frutos con c{\'a}maras RGB-D}, booktitle = {Congreso Ib{\'e}rico de Agroingenier{\'\i}a}, year = {2019}, month = {09/2019}, publisher = { Universidad de Zaragoza (UZA)}, organization = { Universidad de Zaragoza (UZA)}, address = {Huesca}, abstract = {

La detecci{\'o}n remota de frutos ser{\'a} una herramienta indispensable para la gesti{\'o}n agron{\'o}mica optimizada y sostenible de las plantaciones frut{\'\i}colas del futuro, con aplicaciones en previsi{\'o}n de cosecha, robotizaci{\'o}n de la recolecci{\'o}n y elaboraci{\'o}n de mapas de producci{\'o}n. Este trabajo propone el uso de c{\'a}maras de profundidad RGB-D para la detecci{\'o}n y la posterior localizaci{\'o}n 3D de los frutos. El material utilizado para la adquisici{\'o}n de datos consiste en una plataforma terrestre autopropulsada equipada con dos sensores Kinect v2 de Microsoft y un sistema de posicionamiento RTK-GNSS. Con este equipo se escanearon 3 filas de manzanos Fuji de una explotaci{\'o}n comercial. El conjunto de datos adquiridos est{\'a} compuesto por 110 capturas que contienen un total de 12,838 manzanas Fuji. La detecci{\'o}n de frutos se realiz{\'o} mediante los datos RGB (im{\'a}genes de color proporcionadas por el sensor). Para ello, se implement{\'o} y se entren{\'o} la red neuronal convolucional de detecci{\'o}n de objetos Faster R-CNN, la cual est{\'a} compuesta por dos m{\'o}dulos: red de propuesta de regiones de inter{\'e}s y red de clasificaci{\'o}n. Ambos m{\'o}dulos comparten las primeras capas convolucionales siguiendo el modelo VGG-16 pre-entrenado con la base de datos ImageNet. Los resultados de test muestran un porcentaje de detecci{\'o}n del 91.4\% de los frutos con un 15.9\% de falsos positivos (F1-score = 0.876). La evaluaci{\'o}n cualitativa de las detecciones muestra que los falsos positivos corresponden a zonas de la imagen que presentan un patr{\'o}n muy similar a una manzana, donde, incluso a percepci{\'o}n del ojo humano, es dif{\'\i}cil de determinar si hay o no manzana. Por otro lado, las manzanas no detectadas corresponden a aquellas que estaban ocultas casi en su totalidad por otros {\'o}rganos vegetativos (hojas o ramas) o a manzanas cortadas por los m{\'a}rgenes de la imagen. De los resultados experimentales se concluye que el sensor Kinect v2 tiene un gran potencial para la detecci{\'o}n y localizaci{\'o}n 3D de frutos. La principal limitaci{\'o}n del sistema es que el rendimiento del sensor de profundidad se ve afectado en condiciones de alta iluminaci{\'o}n

}, keywords = {C{\'a}maras de profundidad, Detecci{\'o}n de frutos, Redes neuronales convolucionales, RGB-D, Rob{\'o}tica agr{\'\i}cola}, doi = {https://doi.org/10.26754/c_agroing.2019.com.3325}, author = {Gen{\'e}-Mola, Jordi and Ver{\'o}nica Vilaplana and Rosell-Polo, Joan R. and Morros, J.R. and Ruiz-Hidalgo, J. and Gregorio, Eduard} } @conference {cIndia18, title = {UPC Multimodal Speaker Diarization System for the 2018 Albayzin Challenge}, booktitle = {IberSpeech 2018}, year = {2018}, month = {11/2018}, address = {Barcelona}, abstract = {

This paper presents the UPC system proposed for the Multimodal Speaker Diarization task of the 2018 Albayzin Challenge. This approach works by processing individually the speech and the image signal. In the speech domain, speaker diarization is performed using identity embeddings created by a triplet loss DNN that uses i-vectors as input. The triplet DNN is trained with an additional regularization loss that minimizes the variance of both positive and negative distances. A sliding window is then used to compare speech segments with enrollment speaker targets using cosine distance between the embeddings. To detect identities from the face modality, a face detector followed by a face tracker has been used on the videos. For each cropped face a feature vector is obtained using a Deep Neural Network based on the ResNet 34 architecture, trained using a\  metric learning triplet loss (available from dlib library). For each track the face feature vector is obtained by averaging the features obtained for each one of the frames of that track. Then, this feature vector is compared with the features extracted from the images of the enrollment identities. The proposed system is evaluated on the RTVE2018 database.

}, keywords = {Face Diarization, Multimodal Systems, Speaker Diarization}, author = {India, M. and Sagastiberri, I. and Palau, P. and Elisa Sayrol and Morros, J.R. and Hernando, J.} } @conference {cIndia, title = {UPC System for the 2016 MediaEval Multimodal Person Discovery in Broadcast TV task}, booktitle = {MediaEval 2016 Workshop}, year = {2016}, month = {10/2016}, address = {Hilversum, The Netherlands}, abstract = {

The UPC system works by extracting monomodal signal segments (face tracks, speech segments) that overlap with the person names overlaid in the video signal. These segments are assigned directly with the name of the person and used as a reference to \ compare against the non-overlapping (unassigned) signal segments. This process is performed independently both on the speech and video signals. A simple fusion scheme is used to combine both monomodal annotations into a single one.

}, author = {India, M. and Mart{\'\i}, G. and Cotillas, C. and Bouritsas, G. and Elisa Sayrol and Morros, J.R. and Hernando, J.} } @conference {cIndia15, title = {UPC System for the 2015 MediaEval Multimodal Person Discovery in Broadcast TV task}, booktitle = {MediaEval 2015 Workshop}, year = {2015}, month = {09/2015}, address = {Wurzen, Germany}, abstract = {

This paper describes a system to identify people in broadcast TV shows in a purely unsupervised manner. The system outputs the identity of people that appear, talk and can be identified by using information appearing in the show (in our case, text with person names). Three types of monomodal technologies are used: speech diarization, video diarization and text detection / named entity recognition. These technologies are combined using a linear programming approach where some restrictions are imposed.

}, author = {India, M. and David Varas and Ver{\'o}nica Vilaplana and Morros, J.R. and Hernando, J.} } @conference {cLidon, title = {UPC-UB-STP @ MediaEval 2015 Diversity Task: Iterative Reranking of Relevant Images}, booktitle = {MediaEval 2015 Workshop}, year = {2015}, month = {09/2015}, abstract = {

This paper presents the results of the UPC-UB-STP team in the 2015 MediaEval Retrieving Diverse Images Task.The goal of the challenge is to provide a ranked list of Flickr photos for a predefined set of queries. Our approach firstly generates a ranking of images based on a query-independent estimation of its relevance. Only top results are kept and iteratively re-ranked based on their intra-similarity to introduce diversity.

UPC-UB-STP @ MediaEval 2015 diversity task iterative reranking of relevant images from Xavier Giro
}, url = {http://ceur-ws.org/Vol-1436/}, author = {Lidon, Aniol and Bola{\~n}os, Marc and Seidl, Markus and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Zeppelzauer, Matthias} } @article {aTochon15, title = {On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images}, journal = {Remote Sensing of Environment}, volume = {159}, year = {2015}, month = {03/2015}, pages = {318-331}, abstract = {

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

}, issn = {0034-4257}, author = {G. Tochon and J.B. F{\'e}eret and Valero, S. and R.E. Martin and D.E. Knapp and Salembier, P. and Chanussot, J. and G. Asner} } @conference {cManchon-Vizuetea, title = {UPC at MediaEval 2014 Social Event Detection Task}, booktitle = {MediaEval 2014 Workshop}, year = {2014}, month = {10/2014}, publisher = {CEUR Workshop Proceedings}, organization = {CEUR Workshop Proceedings}, address = {Barcelona}, abstract = {

This document presents the contribution of the UPC team to the Social Event Detection (SED) Subtask 1 in MediaEval 2014. This contribution extends the solution tested in the previous year with a better optimization of the parameters that determine the clustering algorithm, and by introducing an additional pass that considers the merges of all pairs of mini-clusters generated during the two first passes. Our proposal also addresses the problem of incomplete metadata by generating additional textual tags based on geolocation and natural language processing techniques.

[MediaEval 2014 website]

}, url = {http://ceur-ws.org/Vol-1263/}, author = {Manchon-Vizuete, Daniel and Gris-Sarabia, Irene and Xavier Gir{\'o}-i-Nieto} } @conference {cVentura13a, title = {UPC at MediaEval 2013 Hyperlinking Task}, booktitle = {MediaEval 2013 Workshop}, year = {2013}, month = {10/2013}, publisher = {CEUR Workshop Proceedings Vol-1043}, organization = {CEUR Workshop Proceedings Vol-1043}, address = {Barcelona, Catalonia}, abstract = {

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

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

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

Presentation on MediaEval Search \& Linking task 2013 from Netherlands Institute for Sound and Vision, University of Twente
}, keywords = {Benchmark, Hyperlinking, Video Collections}, url = {http://ceur-ws.org/Vol-1043/mediaeval2013_submission_35.pdf}, author = {Ventura, C. and Tella, Marcel and Xavier Gir{\'o}-i-Nieto} } @conference {cManchon-Vizuete13, title = {UPC at MediaEval 2013 Social Event Detection Task}, booktitle = {MediaEval 2013 Workshop}, year = {2013}, month = {10/2013}, publisher = {CEUR Workshop Proceedings}, organization = {CEUR Workshop Proceedings}, address = {Barcelona, Catalonia}, abstract = {

These working notes present the contribution of the UPC team to the Social Event Detection (SED) task in MediaEval 2013. The proposal extends the previous PhotoTOC work in the domain of shared collections of photographs stored in cloud services. An initial over-segmentation of the photo collection is later refined by merging pairs of similar clusters.

Xavier Gir{\'o}-i-Nieto, "UPC at MediaEval Social Event Detection 2013" from Xavi Gir{\'o}
}, keywords = {Benchmark, Event Detection, Photo Collections}, url = {http://ceur-ws.org/Vol-1043/mediaeval2013_submission_34.pdf}, author = {Manchon-Vizuete, Daniel and Xavier Gir{\'o}-i-Nieto} } @conference {cPont-Tuset12a, title = {Upper-bound assessment of the spatial accuracy of hierarchical region-based image representations}, booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing}, year = {2012}, month = {03/2012}, pages = {865-868}, abstract = {

Hierarchical region-based image representations are versatile tools for segmentation, filtering, object detection, etc.\ The evaluation of their spatial accuracy has been usually performed assessing the final result of an algorithm based on\ this representation.\ Given its wide applicability, however, a direct supervised assessment, independent of any application, would be desirable and fair.

A brute-force assessment of all the partitions represented in the hierarchical structure would be a correct approach,\ but as we prove formally, it is computationally unfeasible.\ This paper presents an efficient algorithm to find the upper-bound performance of the representation and we show\ that the previous approximations in the literature can fail at finding this bound.

}, isbn = {978-1-4673-0044-5}, doi = {10.1109/ICASSP.2012.6288021}, author = {Jordi Pont-Tuset and Marqu{\'e}s, F.} } @article {aCraene08, title = {Unbiased group-wise alignment by iterative central tendency estimations}, journal = {Mathematical modeling of natural phenomena}, volume = {3}, number = {6}, year = {2008}, pages = {2{\textendash}32}, issn = {0973-5348}, url = {http://hdl.handle.net/2117/8140}, author = {de Craene, M. and Macq, B. and Marqu{\'e}s, F. and Salembier, P. and Warfield, S.} } @article {aLandabaso08, title = {A Unified Framework for Consistent 2D/3D Foreground Object Detection}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {18}, number = {8}, year = {2008}, pages = {1040{\textendash}1051}, issn = {1051-8215}, author = {Landabaso, J. and M. Pard{\`a}s} } @phdthesis {dLandabaso08, title = {A Unified Framework for Consistent 2D/3D Foreground Object Detection}, year = {2008}, school = {Universitat Polit{\`e}cnica de Catalunya (UPC)}, type = {phd}, author = {Landabaso, J.}, editor = {M. Pard{\`a}s} } @inbook {bAbad07, title = {UPC Audio, Video and Multimodal Person Tracking Systems in the CLEAR Evaluation Campaign}, booktitle = {Lecture notes in computer science}, volume = {4122}, year = {2007}, pages = {93{\textendash}104}, publisher = {Springer}, organization = {Springer}, address = {Berlin / Heidelberg}, abstract = {

Reliable measures of person positions are needed for computational perception of human activities taking place in a smart-room environment. In this work, we present the Person Tracking systems developed at UPC for audio, video and audio-video modalities in the context of the EU funded CHIL project research activities. The aim of the designed systems, and particularly of the new contributions proposed, is to deal robustly in both single and multiperson localization tasks independently on the environmental conditions. Besides the technology description, experimental results conducted for the CLEAR evaluation workshop are also reported.

}, isbn = {978-3-540-69567-7}, doi = {10.1007/978-3-540-69568-4_6}, author = {Abad, A. and Cristian Canton-Ferrer and Segura, C. and Landabaso, J. and Macho, D. and Casas, J. and Hernando, J. and M. Pard{\`a}s and Nadeu, C.} } @conference {cAbad06, title = {UPC Audio, Video and Multimodal Person Tracking Systems in the CLEAR Evaluation Campaign}, booktitle = {CLEAR{\textquoteright}06 Evaluation Campaign and Workshop - Classification of Events, Activities and Relationships}, year = {2006}, pages = {93{\textendash}104}, isbn = {978-3-540-69567-7}, author = {Abad, A. and Cristian Canton-Ferrer and Cristian Canton-Ferrer and Segura, C. and Landabaso, J. and Macho, D. and Casas, J. and Hernando, J. and M. Pard{\`a}s and Nadeu, C.} } @article {aRuiz-Hidalgo06, title = {On the use of indexing metadata to improve the efficiency of video compression}, journal = {IEEE transactions on circuits and systems for video technology}, volume = {16}, number = {3}, year = {2006}, pages = {410{\textendash}419}, abstract = {

For the last years, video indexing and video compression have been considered as two separate functionalities. However, multimedia content is growing in such a rate that multimedia services will need to consider both the compression and the indexing aspects of the content in order to efficiently manage this audio-visual content. Therefore, it is interesting to study the synergy between the representations of compression and indexing and in particular to find new schemas that allow the possibility to exploit indexing/compression information in order to increase the efficiency of video compression/indexing capabilities. The principal contribution of this paper is to study and develop new techniques where the compression efficiency of video codecs can be improved by the use of indexing metadata where indexing metadata refers to information that has been generated to support indexing capabilities.

}, keywords = {H.264, Indexing Metadata, MPEG-7, Video Coding}, issn = {1051-8215}, author = {Ruiz-Hidalgo, J. and Salembier, P.} } @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} } @article {aComas03, title = {Unbalanced multiple description video coding based on a rate-distortion optimization}, journal = {Eurasip Journal on Applied Signal Processing}, volume = {2003}, number = {1}, year = {2003}, pages = {81{\textendash}90}, issn = {1110-8657}, author = {Comas, D. and Singh, R. and Ortega, A. and Marqu{\'e}s, F.} } @article {aAvrithis03, title = {Unified Access to Heterogeneous Audiovisual Archives}, journal = {Journal of universal computer science}, volume = {9}, number = {6}, year = {2003}, pages = {510{\textendash}519}, abstract = {

In this paper, an integrated information system is presented that offers enhanced search and retrieval capabilities to users of heterogeneous digital audiovisual (a/v) archives. This innovative system exploits the advances in handlings a/v content and related metadata, as introduced by MPEG-4 and worked out by MPEG-7, to offer advanced services characterized by the tri-fold semantic phrasing of the request (query), unified handling and personalized response. The proposed system is targeting the intelligent extraction of semantic information from a/v and text related data taking into account the nature of the queries that users my issue, and the context determined by user profiles. It also provides a personalization process of the response in order to provide end_users with desired information. From a technical point of view, the FAETHON system plays the role of an intermediate access server residing between the end users and multiple heterogeneous audiovisual archives organized according to the new MPEG standards.

}, issn = {0948-6968}, doi = {10.3217/jucs-009-06-0510}, url = {http://www.jucs.org/jucs_9_6}, author = {Avrithis, Y. and Stamou, G. and Wallace, M. and Marqu{\'e}s, F. and Salembier, P. and Xavier Gir{\'o}-i-Nieto and Haas, W. and Vallant, H. and Zufferey, M.} } @conference {cAvrithis03, title = {Unified access to heterogeneous audiovisual archives}, booktitle = {International Conference on Knowledge Management}, year = {2003}, pages = {1{\textendash}2}, address = {Graz, Austria}, author = {Avrithis, Y. and Stamou, G. and Wallace, M. and Marqu{\'e}s, F. and Salembier, P. and Xavier Gir{\'o}-i-Nieto and Haas, W. and Vallant, H. and Zufferey, M.} } @conference {cSayrol00, title = {Una Aproximaci{\'o}n experimental a las Se{\~n}ales y Sistemas Anal{\'o}gicos}, booktitle = {XV Simposium Nacional de la Uni{\'o}n Cient{\'\i}fica Internacional de Radio}, year = {2000}, pages = {329{\textendash}330}, isbn = {978-972-8865-74-0}, author = {Elisa Sayrol and Gasull, A. and Moreno, A. and Salavedra, J. and Vallverdu, F.} } @inbook {bMarques94, title = {UNSUPERVISED HIERARCHICAL IMAGE SEGMENTATION USING COMPOUND RANDOM FIELDS}, booktitle = {A Robust Method for Computing 2D Projective invariants of 3D Object Vertices}, year = {1994}, pages = {125{\textendash}136}, isbn = {981-02-1872-9}, author = {Marqu{\'e}s, F.} } @conference {cSalembier93, title = {Unsupervised morphological segmentation for images}, booktitle = {IEEE Winter Workshop on Nonlinear Signal Processing}, year = {1993}, pages = {21{\textendash}26}, address = {Tampere, Finland}, author = {Salembier, P.} } @conference {cMarques93a, title = {Unsupervised segmentation controlled by morphological contrast ext}, booktitle = {ICASSP}, year = {1993}, pages = {517{\textendash}520}, author = {Marqu{\'e}s, F. and Gasull, A.} } @conference {cCasas92, title = {Una interpretaci{\'o}n colorim{\'e}trica en clasificaciones fuzzy de im{\'a}genes de teledetecci{\'o}n}, booktitle = {V Simposium Nacional de Reconocimiento de Formas y An{\'a}lisis de Im{\'a}genes}, year = {1992}, pages = {281{\textendash}287}, author = {Casas, J. and Torres, L. and Gasull, A.} }