关键词: 尺度不变特征变换（SIFT）特征点匹配, 图像配准, 欧氏距离, 双阈值

Abstract: Since the SIFT matching algorithm and the SIFT combined with the RANSAC matching algorithm both exist the mismatching problem in varies degree, a double threshold matching algorithm based on local SIFT feature points is proposed. This paper designs the iteration criteria of the variable step size to obtain the double threshold of the SIFT, where the large threshold matching obtains a set of sparse precision matching, and the small threshold matching obtains a set of intensive matching in which mismatching may exists. Then the deformation constraint model is established based on the precise matching, which is the basis of removing the mismatching from the intensive matching. The transformation matrix is estimated by these correct matching points between the two images. To reduce the required time and increase efficiency of the algorithm, the most sharply changing region is extracted by analysing the changes of the image texture, which represents the whole image to do the matching operation. The experimental results indicate that the proposed matching algorithm has advantages of high accuracy, stability and rapidity in the situation that the affine changes of translation, rotation etc exist in the images.

Key words: Scale Invariant Feature Transform（SIFT） feature point matching, image registration, Euclidean distance, double threshold