Name
Abstract | ||
|---|---|---|
In local invariant features, the detected interest points using scale-space representations are considered the most robust to many variations in the imaging conditions. The existing approaches extract interest points at scale-space differential singularities. In these approaches, all detected interest points are considered equally robust without taking in consideration the variable sensitivities of these interest points with respect to noise effects. In this paper, we analyze the robustness of detected interest points at scale-space singularities against noise effects. Also, we propose a novel quantitative stability measure of these interest points. The evaluation results show the effectiveness of the proposed stability measure in estimating the robustness of the detected interest points to noise effects. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICPR.2008.4760978 | 19TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOLS 1-6 |
Keywords | Field | DocType |
robustness,scale space,databases,interest point detection,edge detection,noise,noise measurement,stability analysis,feature extraction | Computer vision,Pattern recognition,Noise measurement,Interest point detection,Computer science,Edge detection,Scale space,Robustness (computer science),Feature extraction,Invariant (mathematics),Artificial intelligence,Gravitational singularity | Conference |
ISSN | Citations | PageRank |
1051-4651 | 0 | 0.34 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alaa E. Abdel-hakim | 1 | 122 | 9.75 |
| Aly A. Farag | 2 | 2147 | 172.03 |