Petia Radeva, Ricardo Toledo, Craig Von Land, & Juan J. Villanueva. (1998). 3D Dynamic Model of the Coronary Tree..
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S. Gonzalez, & A. Martinez. (1997). Fundamentos de la Vision aplicada a la Robotica Autonoma..
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X. Varona. (2001). Seguimiento visual robusto en entornos complejos, Tesis..
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M. Bressan, & Jordi Vitria. (2002). Feature Subset Selection in an ICA Space.
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David Guillamet, & Jordi Vitria. (2002). Non-negative Matrix Factorization for Face Recognition..
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Petia Radeva, M. Bressan, A. Tovar, & Jordi Vitria. (2002). Bayesian Classification for Inspection of Industrial Products..
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Cristina Cañero, E Fernandez-Nofrerias, J. Mauri, & Petia Radeva. (2002). Modelling the Acquisition Geometry of a C-arm Angiography System for 3D Reconstruction..
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David Guillamet, & Jordi Vitria. (2002). Classifying Faces with Non-negative Matrix Factorization..
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Daniel Ponsa, & Xavier Roca. (2002). Unsupervised Parameterisation of Gaussian Mixture Models.
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Petia Radeva, M. Bressan, A. Tovar, & Jordi Vitria. (2002). Bayesian Classification for Inspection of Industrial Products..
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David Rotger, Petia Radeva, J. Mauri, & E Fernandez-Nofrerias. (2002). Internal and External Coronary Vessel Images Registration..
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X. Varona, Jordi Gonzalez, Xavier Roca, & Juan J. Villanueva. (2003). Appearance Tracking for Video Surveillance.
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Jordi Gonzalez, X. Varona, Xavier Roca, & Juan J. Villanueva. (2003). Automatic Keyframing of Human Actions for Computer Animation.
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Jordi Gonzalez, X. Varona, Xavier Roca, & Juan J. Villanueva. (2003). A Human Action Comparison Framework for Motion Understanding.
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Fernando Vilariño, & Petia Radeva. (2003). Cardiac Segmentation with Discriminant Active Contours. (211–217). IOS Press.
Abstract: Dynamic tracking of heart moving is one relevant target in medical imag- ing and can be helpful for analyzing heart dynamics in the study of several cardiac diseases. For this aim, a previous segmentation problem of such structures is stated, based on certain relevant features (like edges or intensity levels, textures, etc.) Clas- sical active models have been used, but they fail when overlapping structures or not well-defined contours are present. Automatic feature learning systems may be a pow- erful tool. Discriminant active contours present optimal results in this kind of problem. They are a kind of deformable models that converge to an optimal object segmenta- tion that dynamically adapts to the object contour. The feature space is designed from a filter bank in order to guarantee the search and learning of the set of relevant fea- tures for optimal classification on each part of the object. Tracking of target evolution is obtained through the whole set of images, using information from the actual and previous stages. Feedback systems are implemented to guarantee the minimum well- separable classification set in each segmentation step. Our implementation has been proved with several series of Magnetic Resonance with improved results in segmenta- tion in comparison to previous methods.
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