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Varas D, Alfaro M, Marqués F. Multiresolution hierarchy co-clustering for semantic segmentation in sequences with small variations. In ICCV - International Conference on Computer Vision. 2015.
Suau X, Casas J, Ruiz-Hidalgo J. Multi-resolution illumination compensation for foreground extraction. In 16th International Conference on Image Processing. 2009. pp. 3225–3228.  (2.16 MB)
Marqués F. Multiresolution image segmentation based on camporend random fields: Application to image coding. Gasull A. Universitat Politècnica de Catalunya (UPC); 1992.
Giró-i-Nieto X, Martos M. Multiscale annotation of still images with GAT. In Proceedings of the 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications. Capri, Italy: ACM; 2012.  (6.08 MB)
Arbelaez P, Pont-Tuset J, Barron J, Marqués F, Malik J. Multiscale Combinatorial Grouping. In Computer Vision and Pattern Recognition (CVPR). 2014.  (2.8 MB)
Pont-Tuset J, Arbelaez P, Barron JT, Marqués F, Malik J. Multiscale Combinatorial Grouping for Image Segmentation and Object Proposal Generation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2017;38(7):128-140.
Calderero F, Eugenio F, Marcello J, Marqués F. Multispectral Cooperative Partition Sequence Fusion for Joint Classification and Hierarchical Segmentation. Geoscience and Remote Sensing Letters, IEEE. 2012;9:1012-1016.
Ramon E, Escur J, Giró-i-Nieto X. Multi-View 3D Face Reconstruction in the Wild using Siamese Networks. In ICCV 2019 Workshop on 3D Face Alignment in the Wild Challenge Workshop (3DFAW). Seoul, South Corea: IEEE/CVF; 2019.  (227.12 KB)
Navarro S, López-Méndez A, Alcoverro M, Casas J. Multi-view Body Tracking with a Detector-Driven Hierarchical Particle Filter. In 7th International Conference AMDO 2012. Port d'Andratx, Mallorca: Springer; 2012.
Navarro S, López-Méndez A, Alcoverro M, Casas J. Multi-view Body Tracking with a Detector-Driven Hierarchical Particle Filter. In: Perales F, Fisher R, Moeslund T. Lecture Notes in Computer Science: Articulated Motion and Deformable Objects. Berlin / Heidelberg: Springer ; 2012. pp. 82-91.
Maceira M. Multi-view depth coding based on a region representation combining color and depth information. Ruiz-Hidalgo J, Morros JR. Signal Theory and Communications (TSC). Universitat Politècnica de Catalunya (UPC); 2017.  (15.24 MB)
Ruiz-Hidalgo J, Morros JR, Aflaki P, Calderero F, Marqués F. Multiview depth coding based on combined color/depth segmentation. Journal of visual communication and image representation. 2012;23(1):42–52.  (1.81 MB)
Gallego J, Pardàs M. Multiview Foreground Segmentation using 3D Probabilistic Model. In ICIP, IEEE International Conference on Image Processing. 2014.  (2.38 MB)
Salvador J, Casas J. Multi-View Video Representation Based on Fast Monte Carlo Surface Reconstruction. IEEE Transactions on Image Processing. 2013;22(9):3342 - 3352.  (6.16 MB)
Casas J, Puig-Sitjes A, Puig-Folch P. Mutual feedback scheme for face detection and tracking aimed at density estimation in demonstrations. IEE Proceedings-Vision, Image and Signal Processing. 2005;152(3):334–346.
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Bonafonte A, Pardàs M, Mariño J. N-best hypotheses:an approach taking into account coarticulation. In EUROPEAN SIGNAL PROCESSING CONFERENCE. 1992. pp. 379–382.
Casamitjana A, Vilaplana V, Puch S, Aduriz A, Lopez C, Operto G, et al.. NeAT: a nonlinear analysis toolbox for neuroimaging. Neuroinformatics. 2020;.
Digne J, Dimiccoli M, Salembier P, Sabater N. Neighborhood Filters and the Recovery of 3D Information. In Handbook of Mathematical Methods in Imaging. Springer Verlag; 2011. pp. 1203-1229.
Digne J, Dimiccoli M, Sabater N, Salembier P. Neighborhood Filters and the Recovery of 3D Information. In Handbook of Mathematical Methods in Imaging. 2nd ed. Springer Verlag; 2015. pp. 1645-1673.
Bellot P, Olsen C, Salembier P, Oliveras A, Meyer PE. NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference. BMC Bioinformatics. 2015;16.  (851.49 KB)
Dominguez A, Mas-Montserrat D, Bustamante C, Giró-i-Nieto X, Ioannidis AG. Neural ADMIXTURE: rapid population clustering with autoencoders. In 2021 Society for Molecular Biology & Evolution meeting (SMBEv2021). Virtual; 2021.  (1.76 MB)
Dominguez A, Mas-Montserrat D, Bustamante C, Giró-i-Nieto X, Ioannidis AG. Neural ADMIXTURE: rapid population clustering with autoencoders. Nature Computational Science. 2023;.
Pardàs M, Sayrol E. A new approach to active contours for tracking. In IEEE International Conference on Image Processing. 2000.
Gimeno D, Torres L, Casas J. A new approach to texture coding using stochastic vector quantization. In IEEE International Conference on Image Processing'94. 1994. pp. 119–123.
Torres L, Casas J. New approaches to texture coding in segmentation and feature-based image coding schemes. In International Conference on Digital Signal Processing. 1995. pp. 12–17.

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