Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICCV.2009.5459174 | ICCV |
Keywords | DocType | Volume |
computer vision,convex programming,image reconstruction,image segmentation,mesh generation,minimisation,3D reconstruction,computer vision problem,convex energy minimization,convex image labeling model,convex multiregion segmentation,image segmentation,manifold,total variation formulation,triangular mesh | Conference | 2009 |
Issue | ISSN | Citations |
1 | 1550-5499 | 8 |
PageRank | References | Authors |
0.47 | 25 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amaël Delaunoy | 1 | 106 | 5.14 |
| Ketut Fundana | 2 | 43 | 6.09 |
| Emmanuel Prados | 3 | 450 | 20.47 |
| anders heyden | 4 | 820 | 109.50 |