Fast image stitching algorithm based on improved FAST-SURF

Abstract

With the shortages of the large amounts of calculations in speeded-up robust features (SURF) algorithm, and low efficiency of image stitching, the features from accelerated segment test (FAST) corner points were used to instead of the SURF spots in order to extract the feature points in image overlapping area. The SURF descriptor was used to describe the feature points, by using the descriptor dimensionality reduction method, the adaptive nearest neighbor and nearer neighbor ratio method, and the geometric constraint method, the false matching points were eliminated in order to improve the matching accuracy. After matching, the random sample consensus (RANSAC) algorithm was improved by reducing the number of sample set and rejecting the unreasonable parameter models to obtain the homography matrix. Finally, the image transformation, fusion and stitching were carried out. The experimental results show that the total time of image stitching is reduced by 12% compared with the traditional SURF algorithm, and the stitching efficiency is improved significantly.