Name
Abstract | ||
|---|---|---|
This paper presents a new variational formulation for detecting interior and exterior boundaries of desired object(s) in color images. The classical level set methods can handle changes in topology, but can not detect interior boundaries. The Chan-Vese model can detect the interior and exterior boundaries of all objects, but cannot detect the boundaries of desired object(s) only. Our method combines the advantages of both methods. In our algorithm, a discrimination function on whether a pixel belongs to the desired object(s) is given. We define a modified Chan-Vese functional and give the corresponding evolution equation. Our method also improves the classical level set method by adding a penalizing term in the energy functional so that the calculation of the signed distance function and re-initialization can be avoided. The initial curve and the stopping function are constructed based on that discrimination function. The initial curve locates near the boundaries of the desired object(s), and converges to the boundaries efficiently. In addition, our algorithm can be implemented by using only simple central difference scheme, and no upwind scheme is needed. This algorithm has been applied to real images with a fast and accurate result. The existence of the minimizer to the energy functional is proved in the Appendix A. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.imavis.2006.12.013 | Image Vision Comput. |
Keywords | Field | DocType |
chan-vese model,interior boundary,exterior boundary,signed distance function,initial curve locates,upwind scheme,desired objects,discrimination function,variational formulation,chan–vese model,initial curve,color image,classical level set method,simple central difference scheme,active contours,discriminant function,distance function,active contour,level set method | Computer vision,Finite difference,Level set method,Signed distance function,Level set,Artificial intelligence,Upwind scheme,Pixel,Energy functional,Real image,Mathematics | Journal |
Volume | Issue | ISSN |
25 | 9 | Image and Vision Computing |
Citations | PageRank | References |
16 | 0.83 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling Pi | 1 | 33 | 2.66 |
| Chaomin Shen | 2 | 161 | 12.57 |
| Fang Li | 3 | 187 | 9.99 |
| Jinsong Fan | 4 | 36 | 2.38 |