Name
Abstract | ||
|---|---|---|
A gradient image describes the differences of neighboring pixels in the image. Extracting edges only depending on a gradient image will results in noised and broken edges. Here, we propose a two-stage edge extraction approach with contextual-filter edge detector and multiscale edge tracker to solve the problems. The edge detector detects most edges and the tracker refines the results as well as reduces the noised or blurred influence; moreover, the extracted results are nearly thinned edges which are suitable for most applications. Based on six wavelet basis functions, qualitative and quantitative comparisons with other methods show that the proposed approach extracts better edges than the other wavelet-based edge detectors and Canny detector extract. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1016/j.imavis.2004.11.005 | Image Vision Comput. |
Keywords | Field | DocType |
multiscale edge tracker,gradient image,better edge,contextual-filter edge detector,two-stage edge extraction approach,wavelet-based multiresolution edge detection,wavelet transform,wavelet-based edge detector,broken edge,edge detector,edge extraction,edge tracking,edge detection,canny detector extract,extracting edge | Computer vision,Canny edge detector,Deriche edge detector,Image gradient,Pattern recognition,Edge detection,Marr–Hildreth algorithm,Artificial intelligence,Detector,Mathematics,Wavelet,Wavelet transform | Journal |
Volume | Issue | ISSN |
23 | 4 | Image and Vision Computing |
Citations | PageRank | References |
27 | 3.66 | 17 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming-Yu Shih | 1 | 72 | 8.65 |
| Din-Chang Tseng | 2 | 326 | 32.31 |