Name
Abstract | ||
|---|---|---|
The Mumford-Shah functional and related algorithms for image segmentation involve a tradeoff between a two-dimensional image structure and one-dimensional parametric curves (contours) that surround objects or distinct regions in the image.We propose an alternative functional that is independent of parameterization; it is a geometric functional given in terms of the surfaces representing the data and image in the feature space. The Γ-convergence technique is combined with the minimal surfaces theory to yield a global generalization of the Mumford-Shah segmentation function. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/s10851-008-0119-9 | Journal of Mathematical Imaging and Vision |
Keywords | Field | DocType |
Image segmentation,Measure-based metric,Geometric functional,Gamma-convergence,Minimal surfaces | Computer vision,Feature vector,Mathematical optimization,Scale-space segmentation,Feature detection (computer vision),Image texture,Segmentation,Segmentation-based object categorization,Image segmentation,Geometric design,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
33 | 3 | 0924-9907 |
Citations | PageRank | References |
1 | 0.35 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vladimir Kluzner | 1 | 14 | 1.82 |
| Gershon Wolansky | 2 | 15 | 4.16 |
| Yehoshua Y. Zeevi | 3 | 610 | 248.69 |