Name
Abstract | ||
|---|---|---|
An active contour model, called snake, can adapt to object boundary in an image. A snake is defined as an energy minimizing spline guided by external constraint forces and influenced by image forces that pull it toward features such as lines or edges. The traditional snake model fails to locate object contours that appear in complex background. In this paper, we present an improved snake model associated with new regional similarity energy and a gravitation force field to attract the snake approaching the object contours efficiently. Experiment results show that our snake model works successfully for convex and concave objects in a variety of complex backgrounds. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.cviu.2006.08.007 | Computer Vision and Image Understanding |
Keywords | Field | DocType |
new regional similarity energy,object contour,snake,complex background,experiment result,image segmentation,concave object,locating object contour,active contour model,traditional snake model,improved snake model,snake model,edge detection,image force,energy minimization,force field | Active contour model,Spline (mathematics),Computer vision,Similitude,Edge detection,Image processing,Regular polygon,Image segmentation,Artificial intelligence,Convex analysis,Mathematics | Journal |
Volume | Issue | ISSN |
105 | 2 | Computer Vision and Image Understanding |
Citations | PageRank | References |
17 | 0.71 | 18 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frank Y. Shih | 1 | 1103 | 89.56 |
| Kai Zhang | 2 | 18 | 1.07 |