Convexification of high-dimension multi-label problems
Résumé
This paper is concerned with the problem of relaxing non convex functionals used in image processing into convex problems. We review most of the recently introduced convexification methods, and we propose a new one based on a probabilistic approach, which has the advantages of being intuitive and flexible. We investigate in detail the connections between the solution of the relaxed functional with a minimizer of the original one. As a case of study, we illustrate our theoretical analysis with numerical experiments, namely for the optical flow problem.
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