An Improved SIFT Algorithm for Image Stereo Matching

The high complexity and long computing time of SIFT (scale invariant feature transform) algorithm affect the real-time ability of stereo matching. To solve this problem, an improved feature-points matching algorithm of stereo vision was proposed. The SIFT algorithm was improved in two aspects. First, 24-dimensional feature descriptor instead of 128-dimensional feature descriptor was used to reduce computational complexity. Then the improved BBF search algorithm was used in the process of image matching, so that the feature point matching of the two images can be determined through the minimum priority queue restrictions and the Mahalanobis distance of higher matching accuracy. The classical images and images taken at unknown outdoor environment were used to validate this algorithm. Experimental results show that the proposed algorithm spends 0.01 s to detect 100 feature points, and the average correct matching rate is 89.65%. Compared with the original algorithm, it improves the matching accuracy and reduces the matching time.