Chinese Optics Letters, Vol. 7, Issue 3, 201 (2009)
Spectral feature matching based on partial least squares
Weidong Yan, Zheng Tian, Lulu Pan, and Mingtao Ding
- School of Science, Northwestern Polytechnical University, Xi'an 7100722 State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101
We investigate the spectral approaches to the problem of point pattern matching, and present a spectral feature descriptors based on partial least square (PLS). Given keypoints of two images, we define the position similarity matrices respectively, and extract the spectral features from the matrices by PLS, which indicate geometric distribution and inner relationships of the keypoints. Then the keypoints matching is done by bipartite graph matching. The experiments on both synthetic and real-world data corroborate the robustness and invariance of the algorithm.