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Author  |
A. Pujol; X. Varona; Joan Serrat |
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A machine vision system for the inspection of industrial sieves. |
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1997 |
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VII SNRFAI. |
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ADAS @ adas @ PVS1997 |
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33 |
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A. Quingles |
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Particio de sòlids |
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2001 |
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CVC Technical Report #54 |
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CVC (UAB) |
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Admin @ si @ Qui2001 |
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206 |
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Author |
A. Restrepo; Angel Sappa; M. Devy |
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Edge registration versus triangular mesh registration, a comparative study |
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2005 |
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Signal Processing: Image Communication 20: 853–868 (IF: 1.264) |
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ADAS @ adas @ RSD2005 |
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567 |
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A. Richichi; O. Fors; M.T. Merino; Xavier Otazu; J. Nuñez; A. Prades; U. Thiele; D. Perez-Ramirez; F.J. Montojo |
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The Calar Alto lunar occultation program: update and new results |
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2006 |
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Astronomy and Astrophysics (Section ’Stellar structure and evolution’), 445:1081–1088 |
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CAT @ cat @ RFM2006a |
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589 |
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Author |
A. Ruiz; Joost Van de Weijer; Xavier Binefa |
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Title |
Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization |
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2014 |
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25th British Machine Vision Conference |
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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. |
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Nottingham; UK; September 2014 |
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BMVC |
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LAMP; CIC; 600.074; 600.079 |
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Admin @ si @ RWB2014 |
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2508 |
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Author |
A. Sanfeliu; Juan J. Villanueva |
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An approach of visual motion analysis |
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2005 |
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Pattern Recognition Letters |
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PRL |
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26 |
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3 |
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355–368 |
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IF: 1.138 |
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ISE @ ise @ SaV2005 |
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561 |
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A. Sanfeliu; Juan J. Villanueva; Jordi Vitria |
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Image Analysis and Pattern Recognition. |
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1997 |
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OR;MV |
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BCNPCL @ bcnpcl @ SVV1997 |
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56 |
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Author |
A. Toet; M. Henselmans; M.P. Lucassen; Theo Gevers |
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Title |
Emotional effects of dynamic textures |
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2011 |
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i-Perception |
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iPER |
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2 |
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9 |
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969 – 991 |
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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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2041-6695 |
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ALTRES;ISE |
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Admin @ si @THL2011 |
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1843 |
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Author |
A.F. Sole; Antonio Lopez; Cristina Cañero; Petia Radeva; J. Saludes |
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Crease enhancement diffusion |
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1999 |
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Proceedings of the VIII Symposium Nacional de Reconocimiento de Formas y Analisis de Imagenes |
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ADAS @ adas @ SLC1999 |
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A.F. Sole; Antonio Lopez; G. Sapiro |
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Crease Enhancement Diffusion |
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2001 |
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Computer Vision and Image Understanding, 84(2): 241–248 (IF: 1.298) |
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New York; USA |
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ADAS @ adas @ SLS2001 |
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485 |
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