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.
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A. Pujol, Jordi Vitria, Petia Radeva, Xavier 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..
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A. Pujol, Jose Luis Alba, & Juan J. Villanueva. (2001). Supervised Hausdorff-based measures for face recognition..
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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.
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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, .
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A. Pujol, Juan J. Villanueva, & H. Wechsler. (2000). Automatic View Based Caricaturing. In 15 th International Conference on Pattern Recognition (Vol. 1, pp. 1072–1075).
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A. Pujol, Juan J. Villanueva, & Jose Luis Alba. (2001). Efficient Computation of Face Shape Similarity Using Distance Transform Eigendecomposition and Valleys..
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A. Quingles. (2001). Particio de sòlids.
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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).
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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.
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A. Sanfeliu, & Juan J. Villanueva. (2005). An approach of visual motion analysis. PRL - Pattern Recognition Letters, 26(3), 355–368.
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A. Sanfeliu, Juan J. Villanueva, & Jordi Vitria. (1997). Image Analysis and Pattern Recognition..
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A. Toet, M. Henselmans, M.P. Lucassen, & Theo Gevers. (2011). Emotional effects of dynamic textures. iPER - i-Perception, 969 – 991.
Abstract: This study explores the effects of various spatiotemporal dynamic texture characteristics on human emotions. The emotional experience of auditory (eg, music) and haptic repetitive patterns has been studied extensively. In contrast, the emotional experience of visual dynamic textures is still largely unknown, despite their natural ubiquity and increasing use in digital media. Participants watched a set of dynamic textures, representing either water or various different media, and self-reported their emotional experience. Motion complexity was found to have mildly relaxing and nondominant effects. In contrast, motion change complexity was found to be arousing and dominant. The speed of dynamics had arousing, dominant, and unpleasant effects. The amplitude of dynamics was also regarded as unpleasant. The regularity of the dynamics over the textures’ area was found to be uninteresting, nondominant, mildly relaxing, and mildly pleasant. The spatial scale of the dynamics had an unpleasant, arousing, and dominant effect, which was larger for textures with diverse content than for water textures. For water textures, the effects of spatial contrast were arousing, dominant, interesting, and mildly unpleasant. None of these effects were observed for textures of diverse content. The current findings are relevant for the design and synthesis of affective multimedia content and for affective scene indexing and retrieval.
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A.F. Sole, Antonio Lopez, Cristina Cañero, Petia Radeva, & J. Saludes. (1999). Crease enhancement diffusion.
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A.F. Sole, Antonio Lopez, & G. Sapiro. (2001). Crease Enhancement Diffusion. Computer Vision and Image Understanding, 84(2): 241–248 (IF: 1.298), .
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