基于点空间特征的两种点匹配算法

Two Point Matching Algorithms Based on Point Spatial Features

通过讨论和分析经典特征向量匹配算法的基本原理和抗噪性能问题,提出2种新的点匹配算法:加权特征向量算法和顺序匹配算法.加权特征向量匹配算法通过对点集距离矩阵进行特征向量分解获得点集中点的特征向量,而后利用特征值对向量加权,通过比较点的加权特征向量相似性来获取匹配关系.顺序匹配算法避免了矩阵分解,直接对距离矩阵的距离向量进行排序,通过较有序的向量来获取匹配关系.这2种算法,解决了经典特征向量匹配算法中抗噪性能差和高斯参数选择的2个问题.实验结果表明,算法切实可行,文中结论正确.

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 intra-set 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.