Name
Abstract | ||
|---|---|---|
The reconstruction of geometry or, in particular, the shape of objects is a common issue in image analysis. Starting from a variational formulation of such a problem on a shape manifold we introduce a regularization technique incorporating statistical shape knowledge. The key idea is to consider a Riemannian metric on the shape manifold which reflects the statistics of a given training set. We investigate the properties of the regularization functional and illustrate our technique by applying it to region-based and edge-based segmentation of image data. In contrast to previous works our framework can be considered on arbitrary (finite-dimensional) shape manifolds and allows the use of Riemannian metrics for regularization of a wide class of variational problems in image processing.
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Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s11263-007-0103-7 | International Journal of Computer Vision |
Keywords | DocType | Volume |
variational formulation,regularization technique,common issue,image data,statistical shape analysis · variational methods · regularization theory · image segmentation · shape recognition,shape manifold,variational problem,image processing,statistical shape knowledge,image analysis,regularized reconstruction,riemannian metrics,a priori knowledge,variational method,image segmentation | Journal | 79 |
Issue | ISSN | Citations |
2 | 0920-5691 | 4 |
PageRank | References | Authors |
0.53 | 18 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthias Fuchs | 1 | 47 | 3.24 |
| Otmar Scherzer | 2 | 346 | 52.10 |