International Journal of Image Processing | |
A comparison of SIFT, PCA-SIFT and SURF | |
Luo Juan1  Oubong Gwun1  | |
[1] $$ | |
关键词: SIFT; PCA-SIFT; SURF; robust detectors; | |
DOI : | |
来源: Computer Science Journals | |
【 摘 要 】
This paper compares three robust feature detection methods, they are, Scale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA) -SIFT and Speeded Up Robust Features (SURF). Lowe presented SIFT [1], which was successfully used in recognition, stitching and many other applications because of its robustness. Yan Ke [2] gave a change of SIFT by using PCA to normalize the gradient patch instead of histogram. H. Bay [3] presented a faster method for SURF, which used Fast-Hessian detector. The performance of the three methods is compared for scale changes, rotation , blur, illumination changes and affine transformations, all of which uses repeatability as an evaluation measurement. Additionally, RANSAC is used to reject the inconsistent matches [4]. SIFT presents its stability in most situation except rotation and illumination changes. SURF is the fastest one with good performance as the same as SIFT, PCA-SIFT shows its advantages in rotation, blur and illumination changes.
【 授权许可】
Unknown
【 预 览 】
Files | Size | Format | View |
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RO201912040511088ZK.pdf | 2543KB | download |