Name
Abstract | ||
|---|---|---|
Active contour methods are widely used for efficient contour detection. This paper proposes a novel contribution for the Harris based Vector Field Convolution (HVFC) method, using the orientation information of feature points in the image by analyzing the gradient information in the small neighborhood. Based on the orientation information, relevant edges are emphasized and an improved edge map is used in the iterative process. The main advantage of the introduced Directional HVFC (DHVFC) method is the ability of exploiting orientation information for increased contour detection accuracy even in case of high curvature boundaries and strong background clutter. The quantitative and qualitative evaluation and comparison with other state-of-the-art methods show that the additional directional information increases the detection performance. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7025957 | Image Processing |
Keywords | Field | DocType |
clutter,convolution,edge detection,gradient methods,image denoising,DHVFC method,Harris based vector field convolution method,active contour method,direction selective vector field convolution,directional HVFC method,edge map,efficient contour detection,gradient orientation information,high curvature boundaries,iterative process,strong background clutter,Active Contour,Direction Selectivity,Harris based Vector Field Convolution | Active contour model,Computer vision,Curvature,Pattern recognition,Iterative and incremental development,Clutter,Computer science,Vector field convolution,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
1522-4880 | 1 | 0.35 |
References | Authors | |
11 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrea Manno-Kovacs | 1 | 13 | 3.02 |
| Manno-Kovacs, A. | 2 | 1 | 0.35 |