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Communication dans un congrès
Occlusion Detection in Dense Stereo Estimation with Convex Optimization
Abstract : In this paper, we propose a dense two-frame stereo algorithm which handles occlusion in a variational framework. Our method is based on a new regularization model which includes both a constraint on the occlusion width and a visibility constraint in nonoccluded areas. The minimization of the resulting energy functional is done by convex relaxation. A post-processing then detects and fills the occluded regions. We also propose a novel dissimilarity measure that combines color and gradient comparison with a variable respective weight, to benefit from the robustness of the comparison based on local variations while avoiding the fattening effect it may generate.
Littérature citée [17 références]
https://hal-enpc.archives-ouvertes.fr/hal-01700678
Contributeur : Pascal Monasse <>
Soumis le : lundi 5 février 2018 - 10:07:43
Dernière modification le : mardi 16 mars 2021 - 15:44:41
Archivage à long terme le : : jeudi 3 mai 2018 - 05:40:36
Contributeur : Pascal Monasse <>
Soumis le : lundi 5 février 2018 - 10:07:43
Dernière modification le : mardi 16 mars 2021 - 15:44:41
Archivage à long terme le : : jeudi 3 mai 2018 - 05:40:36
Fichier
papier_icip_version3.pdf
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