Name
Abstract | ||
|---|---|---|
The soft additive segmentation model attempts to solve the problem related to the segmentation of overlapping objects with additive intensity value. An issue in optimizing the soft additive segmentation functional is that a high-order nonlinear partial differential equation needs to be solved, which, for most standard algorithms, involves high computational cost. In this paper, we propose a fast and efficient numerical algorithm to optimize the soft additive segmentation model. We reformulate the original minimization problem into a sequence of simpler minimization problems that can be solved efficiently by using the augmented Lagrangian method. Numerical tests on real and synthetic cases are presented to demonstrate the efficiency of our algorithm. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1137/120863617 | SIAM JOURNAL ON IMAGING SCIENCES |
Keywords | Field | DocType |
image segmentation,Euler's elastica,level set methods,Mumford-Shah segmentation model,additive model,overlapping objects | Standard algorithms,Mathematical optimization,Scale-space segmentation,Additive model,Segmentation,Segmentation-based object categorization,Image segmentation,Minification,Augmented Lagrangian method,Mathematics | Journal |
Volume | Issue | ISSN |
5 | 3 | 1936-4954 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tze Siong Lau | 1 | 19 | 3.41 |
| Andy M. Yip | 2 | 232 | 20.65 |