Iterative Weighted Correlation Registration Algorithm for Feature Point Sets
图象特征点集配准的加权相关迭代算法

Image registration is a fundamental object recognition method in computer vision. It aims to find a best match of an object image in an image to be processed. In this paper, we concentrate on image registration from image feature point sets. A new method is proposed which is based on the conventional correlation measure of two point sets which was introduced by Umeyama. The traditional Procrustes analysis method is used to normalize the point sets. The novelty of the proposed method is by introducing a weight matrix into Umeyama's correlation measure the limitation of the traditional method, which requires the dimensions of both point sets to be the same, is released. The proposed method can register two point sets with geometrical distortion and different dimensions. Point sets registration results are given in the paper. When the dimensions of both point sets are the same, both of the proposed method and the traditional method work well. But when the dimensions are different, only the proposed method can register point sets precisely.