图象特征点集配准的加权相关迭代算法

Iterative Weighted Correlation Registration Algorithm for Feature Point Sets

图象配准是计算机视觉中目标识别的一种基本方法 .其目的是在待识别图象中寻找与模型图象的最佳匹配 .该文以传统的 U meyam a点集相关度量为基础 ,结合 Procrustes正规化方法 ,通过引入加权矩阵 ,以得到新的相关度量函数 ,进而提出了一种图象特征点集匹配的新方法 ,解决了传统方法要求点集维数相同的缺点 .经过迭代运算 ,对存在几何失真 ,且维数不同的两点集可得到精确配准 .文中给出的点集配准结果说明 ,当两点集维数相同时 ,该方法不仅与传统的点集相关法一样 ,均可达到精确配准 。

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.