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.目前,对于图象间的变换为相似变换的情形已有闭合公式.本文则分别运用最小二乘和矩阵伪逆两种方法,对图象间的变换为仿射变换的情形进行了研究,并给出了简单的闭合公式.实验表明这种方法精确、稳定、受噪声影响小.

This work investigates the image registration from feature point sets. Image registration is a fundamental object recognition method in computer vision and it aims to find best matches between two or more point sets when there are geometric distortions, point measurement errors and contamination present. Up to now > closed form solution has been developed only when the geometric distortion is similarity transformation. This paper concentrates on image registration from feature point sets when the geometric distortion between the two images is affine transformation and gives closed form solution for the transformation parameters that minimize the root-mean-squared residual error of the image points by the linear least-squares techniques and the pseudo-inverse of matrix respectively. In order to give the simple closed form solution, the image points are represented by homogeneous coordinates and the theories of matrix are used. The algorithms are evaluated on both synthetic and real world images and the experiment results show that the methods given in this paper are accurate, stable and are only affected slightly by noise.