|
|
A. Pujol, Felipe Lumbreras, X. Varona, & Juan J. Villanueva. (2000). Locating people in indoor scenes for real applications..
|
|
|
|
A. Pujol, Felipe Lumbreras, X. Varona, & Juan J. Villanueva. (1999). Template matching through invariant eigenspace projection..
|
|
|
|
A. Pujol, H. Wechsler, & Juan J. Villanueva. (2001). Learning and Caricaturing the Face Space Using Self-Organization and Hebbian Learning for Face Processing..
|
|
|
|
A. Pujol, Jordi Vitria, Felipe Lumbreras, & Juan J. Villanueva. (2001). Topological principal component analysis for face encoding and recognition. PRL - Pattern Recognition Letters, 22(6-7), 769–776.
|
|
|
|
A. Pujol, Jordi Vitria, Petia Radeva, X. Binefa, Robert Benavente, Ernest Valveny, et al. (1999). Real time pharmaceutical product recognition using color and shape indexing. In Proceedings of the 2nd International Workshop on European Scientific and Industrial Collaboration (WESIC´99), Promotoring Advanced Technologies in Manufacturing..
|
|
|
|
A. Pujol, Jose Luis Alba, & Juan J. Villanueva. (2001). Supervised Hausdorff-based measures for face recognition..
|
|
|
|
A. Pujol, & Juan J. Villanueva. (2002). A supervised Modification of the Hausdorff distance for visual shape classification. International Journal of Pattern Recognition and Artificial Intelligence, 349–359.
|
|
|
|
A. Pujol, & Juan J. Villanueva. (1996). Desarrollo de una interface basada en la utilizacion de redes neuronales aplicadas a la clasificacion de las respuestas electroencefalograficas a estimulos visuales. XIV Congreso anual de la sociedad española de ingenieria biomedica, .
|
|
|
|
A. Pujol, Juan J. Villanueva, & H. Wechsler. (2000). Automatic View Based Caricaturing..
|
|
|
|
A. Pujol, Juan J. Villanueva, & Jose Luis Alba. (2001). Efficient Computation of Face Shape Similarity Using Distance Transform Eigendecomposition and Valleys..
|
|
|
|
A. Pujol, X. Varona, & Joan Serrat. (1997). A machine vision system for the inspection of industrial sieves..
|
|
|
|
A. Quingles. (2001). Particio de sòlids.
|
|
|
|
A. Restrepo, Angel Sappa, & M. Devy. (2005). Edge registration versus triangular mesh registration, a comparative study. Signal Processing: Image Communication 20: 853–868 (IF: 1.264).
|
|
|
|
A. Richichi, O. Fors, M.T. Merino, Xavier Otazu, J. Nuñez, A. Prades, et al. (2006). The Calar Alto lunar occultation program: update and new results. Astronomy and Astrophysics (Section ’Stellar structure and evolution’), 445:1081–1088.
|
|
|
|
A. Ruiz, Joost Van de Weijer, & Xavier Binefa. (2014). Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization. In 25th British Machine Vision Conference.
Abstract: We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection.
|
|
