Segmentation convexe multi-région de données sur les surfaces

Abstract : In this paper, we address the problem of segmenting data defined on a manifold into a set of regions with uniform properties. In particular, we propose a numerical method when the manifold is represented by a triangular mesh. Based on recent image segmentation models, our method minimizes a convex energy and then enjoys significant favorable properties: it is robust to initialization and avoid the problem of the existence of local minima present in many variational models. The contributions of this paper are threefold: firstly we adapt the convex image labeling model to manifolds; in particular the total variation formulation. Secondly we show how to implement the proposed method on triangular meshes, and finally we show how to use and combine the method in other computer vision problems, such as 3D reconstruction. We demonstrate the efficiency of our method by testing it on various data.
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Submitted on : Thursday, October 14, 2010 - 11:27:08 AM
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Amael Delaunoy, Emmanuel Prados, Ketut Fundana, Anders Heyden. Segmentation convexe multi-région de données sur les surfaces. RFIA 2010 - 17ème Congrès de Reconnaissance des Formes et Intelligence Artificielle, Jan 2010, Caen, France. ⟨inria-00526301⟩

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