Name
Abstract | ||
|---|---|---|
Many studies have been made in the past for optimization using covariance matrices of feature points. We first describe how to compute the covariance matrix of a feature point from the gray levels by integrating existing methods. Then, we experimentally examine if thus computed covariance matrices really reflect the accuracy of the feature points. To test this, we do subpixel tem- plate matching and compute the homography and the fundamental matrix. Our conclusion is rather surprising, pointing out impor- tant elements often overlooked. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1109/ICCV.2001.937640 | Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference |
Keywords | Field | DocType |
covariance matrices,feature extraction,image matching,covariance matrices,feature points,gray levels,image features,optimization,template matching | Computer vision,Covariance function,Estimation of covariance matrices,Pattern recognition,Computer science,Rational quadratic covariance function,Covariance intersection,Artificial intelligence,CMA-ES,Covariance matrix,Matérn covariance function,Covariance | Conference |
Volume | Issue | ISBN |
2 | 1 | 0-7695-1143-0 |
Citations | PageRank | References |
46 | 3.30 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yasushi Kanazawa | 1 | 143 | 13.12 |
| Kenichi Kanatani | 2 | 1468 | 320.07 |