A parallel proximal splitting method for disparity estimation from multicomponent images under illumination variation

Abstract : Proximal splitting algorithms play a central role in finding the numerical solution of convex opti- mization problems. This paper addresses the problem of stereo matching of multi-component images by jointly estimating the disparity and the illumination variation. The global formulation being non-convex, the prob- lem is addressed by solving a sequence of convex re- laxations. Each convex relaxation is non trivial and in- volves many constraints aiming at imposing some reg- ularity on the solution. Experiments demonstrate that the method is efficient and provides better results com- pared with other approaches.
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Journal of Mathematical Imaging and Vision, Springer Verlag, 2013, 47 (3), http://link.springer.com/article/10.1007/s10851-012-0361-z?null. <10.1007/s10851-012-0361-z>
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Caroline Chaux, Mireille El Gheche, Joumana Farah, Jean-Christophe Pesquet, Beatrice Pesquet-Popescu. A parallel proximal splitting method for disparity estimation from multicomponent images under illumination variation. Journal of Mathematical Imaging and Vision, Springer Verlag, 2013, 47 (3), http://link.springer.com/article/10.1007/s10851-012-0361-z?null. <10.1007/s10851-012-0361-z>. <hal-00733454>

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