Proximal splitting methods for depth estimation

Abstract : Stereo matching is an active area of research in image processing. In a recent work, a convex programming approach was developed in order to generate a dense disparity field. In this paper, we address the same estimation problem and propose to solve it in a more general convex optimization framework based on proximal methods. More precisely, unlike previous works where the criterion must satisfy some restrictive conditions in order to be able to numerically solve the minimization problem, this work offers a great flexibility in the choice of the involved criterion. The method is validated in a stereo image coding framework, and the results demonstrate the good performance of the proposed parallel proximal algorithm.
Type de document :
Communication dans un congrès
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2011, Prague, Czech Republic. pp.853-856, 2011


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Contributeur : Mounir Kaaniche <>
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Dernière modification le : jeudi 9 février 2017 - 15:02:48
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  • HAL Id : hal-00733840, version 1

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Mireille El Gheche, Jean-Christophe Pesquet, Farah Joumana, Mounir Kaaniche, Beatrice Pesquet-Popescu. Proximal splitting methods for depth estimation. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2011, Prague, Czech Republic. pp.853-856, 2011. <hal-00733840>

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