Improved SIFT image feature matching algorithm

Abstract

Image matching technology is one of the important research problems in the field of computer vision. Scale invariant feature transform (SIFT) is a widely used algorithm in image matching, but the SIFT algorithm has problems such as long matching time and incorrect image matching. In order to improve the image matching accuracy, this paper proposes an improved SIFT image feature matching algorithm. First, for the detection of image feature points, the original image is subjected to grayscale conversion and binarization processing, and the image contour point information is extracted to remove background interference; secondly, the Even-odd algorithm is used to extract the point information from the original image and the target. The feature point sets extracted from the images are compared to screen out unnecessary background feature point sets, thereby reducing the number of matching feature point sets and reducing the probability of misidentification. The experimental results show that the algorithm can effectively reduce the interference caused by the image background, thereby improving the accuracy of image matching.