A comparison of SIFT, PCA-SIFT and SURF

International Journal of Image Processing

Luo Juan¹  Oubong Gwun¹ 

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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