Keypoint clustering for robust image matching
| Autor: | Onur C. Hamsici, Yuriy Reznik, Chong Lee, John Hyunchul Hong, Sundeep Vaddadi |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Matching (statistics)
business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-invariant feature transform Image processing Pattern recognition RANSAC Scale invariance Scale space Digital image Feature (computer vision) Computer vision Artificial intelligence business Cluster analysis Precision and recall |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| DOI: | 10.1117/12.862359 |
| Popis: | A number of popular image matching algorithms such as Scale Invariant Feature Transform (SIFT)1 are based on local image features. They first detect interest points (or keypoints) across an image and then compute descriptors based on patches around them. In this paper, we observe that in textured or feature-rich images, keypoints typically appear in clusters following patterns in the underlying structure. We show that such clustering phenomenon can be used to: 1) enhance recall and precision performance of the descriptor matching process, and 2) improve convergence rate of the RANSAC algorithm used in the geometric verification stage. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|