An Improved Algorithm of KPCA-SIFT for Image Registration

摘要：

In this paper, an improved SIFT algorithm with Kernel Principal Component Analysis (KPCA-SIFT) is presented for image registration. Gaussian kernel function is applied to the PCA to extract the principal component for reduced-dimension processing of SIFT descriptor to each feature point. The matched keypoints are selected through similar measure, and the Euclidean distance is replaced by linear combination of cityblock and chessboard distances. The experiments show that this algorithm is robust to image changes in scale, noise and rotation with higher matching accuracy.

关键词： Image Registration Scale Invariant Feature transform Principal Component Analysis Kernel PCA

作者: LU Xuan-min HE Zhao WANG Jun-ben

作者单位: School of Electronic and InformationNorthwestern Polytechnical UniversityXian,Shaanxi Province,Chin School of Electronic and Information Northwestern Polytechnical University Xian,Shaanxi Province,Ch

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-4

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）