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http://cimat.repositorioinstitucional.mx/jspui/handle/1008/602| Quadratic Programming for Probabilistic Image Segmentation | |
| Mariano Rivera | |
| Acceso Abierto | |
| Atribución-NoComercial | |
| Visón por Computadora | |
| We present a general framework for image segmentation based on quadratic programing, i.e. by the minimization of a quadratic regularized energy linearly constrained. In particular, we present a new and general derivation of the Quadratic Makov Measure Field models (QMMFs) that can be understood as a procedure for regularizing the model preferences (memberships or likelihood) as well as efficient optimization algorithms. In the QMMFs the uncertainty in the computed regularized probability measure field is controlled by penalizing the Gini’s coefficient and hence it affects the convexity of the QP problem. The convex case is reduced to the solution of a positive definite linear system and, for that case, an efficient Gauss–Seidel scheme is presented. On the other hand, we present a efficient projected Gauss-Seidel with a subspace minimization for optimizing the non–convex case. We demonstrate the proposal capabilities by experiments and numerical comparisons with interactive two-class segmentation as well as in the simultaneous estimation of segmentation and (parametric and non-parametric) generative models. This paper has been submitted to IEEE TRANSACTIONS ON IMAGE PROCESSING, December 2009 | |
| 25-06-2010 | |
| Reporte | |
| Inglés | |
| Investigadores | |
| LENGUAJES ALGORÍTMICOS | |
| Versión publicada | |
| publishedVersion - Versión publicada | |
| Aparece en las colecciones: | Reportes Técnicos - Ciencias de la Computación |
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| I-10-06.pdf | 1.33 MB | Adobe PDF | Visualizar/Abrir |