Name
Abstract | ||
|---|---|---|
Extraction of object contours from the natural scene is a difficult task because it is hard to distinguish between object contour and texture edge. To overcome this problem, this paper presents a contour extraction method inspired by visual mechanism. Firstly, a biologically motivated surround inhibition process, improved by us, is applied to detect contour elements. Then we utilize visual cortical mechanisms of perceptual grouping to propose a contour grouping model. This model consists of two levels. At low level, a method is presented to compute local interaction between contour elements; at high level, a global energy function is suggested to perceive salient object contours. Finally, contours having high energy are retained while the others, such as texture edge, are removed. Experimental results show our method works well. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-12304-7_65 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
contour grouping model,object contour,texture edge,low level,salient object contour,high energy,contour extraction method,high level,contour element,global energy function | Computer vision,Pattern recognition,Computer science,Salient objects,Object contour,Artificial intelligence,Gradient magnitude,Perception,High energy | Conference |
Volume | Issue | ISSN |
5995 LNCS | PART 2 | 16113349 |
ISBN | Citations | PageRank |
3-642-12303-1 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuan Li | 1 | 0 | 0.34 |
| Jian-Zhou Zhang | 2 | 22 | 5.38 |
| Ping Jiang | 3 | 0 | 0.34 |