Name
Abstract | ||
|---|---|---|
This paper presents a new method for corner and circular feature detection in gray-level images. It is based on the application of standard statistical techniques to the distribution of gradient orientations in a circular neighborhood of the prospective feature point. An evaluation using standard procedures and a comparison with other approaches is presented. Results show the robustness of this method as compared to the other corner detectors analyzed. The main novelties are the possibility of detecting points that are centers of circular symmetries, and discriminating between junctions, which are classified into corners (two-edge junctions) and multiple edge junctions. (C) 1997 Elsevier Science B.V. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1016/S0167-8655(97)00085-8 | Pattern Recognition Letters |
Keywords | Field | DocType |
feature detector,circular statistic,image segmentation,feature detection | Computer vision,Scene analysis,Feature detection (computer vision),Pattern recognition,Feature detection,Feature (computer vision),Image segmentation,Robustness (computer science),Artificial intelligence,Gray level,Detector,Mathematics | Journal |
Volume | Issue | ISSN |
18 | 11-13 | 0167-8655 |
Citations | PageRank | References |
2 | 0.43 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. E. Díaz | 1 | 2 | 0.43 |
| Juan Domingo | 2 | 3319 | 258.54 |
| G. Ayala | 3 | 16 | 3.55 |