Abstract：The fundamental theory and the antinoise problem of the traditional eigenvector approach (EA) are discussed and analyzed. Two matching algorithms are proposed, namely weighted eigenvector approach (WEA) and sorting approach (SA). WEA decomposes the intraset distance matrices of point sets and gets the feature vectors of the points. Then, the feature vectors are weighted by the eigenvalues of matrices. The algorithm gets the matching map by comparing the similarity of the weighted feature vectors. Without the decomposition of matrices, SA acquires the characteristics of the point by sorting the distance matrices, and obtains the matching in the same way above. The two algorithms solve the choosing problem of Gauss parameter and have better antinoise ability than EA. Experimental results show the practicability of the algorithm and the better performance than that of EA.
谭志国，孙即祥. 基于点空间特征的两种点匹配算法[J]. 模式识别与人工智能, 2007, 20(3): 325-330.
TAN ZhiGuo, SUN JiXiang. Two Point Matching Algorithms Based on Point Spatial Features. , 2007, 20(3): 325-330.
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