Name
Abstract | ||
|---|---|---|
Active Contour can describe targets accurately and has been widely used in image segmentation and target tracking. Its main drawback is huge computation that is still not well resolved. In this paper, by analyzing curve gradient flow, the evolution of active contour is divided into two steps: global translation and local deformation. When the curve is far away from the object, the curve just does the translation motion. This method can optimize the curve evolving path and efficiency, and then the computation cost is largely reduced. Our experiments show that our method can segment and track object effectively. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10817-4_86 | ICIRA |
Keywords | Field | DocType |
separate global translation,active contour,curve gradient flow,local deformation,active contour method,huge computation,main drawback,translation motion,target tracking,computation cost,global translation,image segmentation,gradient flow | Active contour model,Drawback,Computer vision,Image segmentation,Artificial intelligence,Deformation (mechanics),Balanced flow,Mathematics,Computation | Conference |
Volume | ISSN | Citations |
5928 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 16 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linlin Zhu | 1 | 28 | 5.98 |
| Baojie Fan | 2 | 41 | 10.48 |
| Y. Tang | 3 | 243 | 33.69 |