关键词: 随机抽样一致性（RANSAC）算法, 特征匹配, 结构相似, 匹配点提纯

Abstract: This paper proposes an improved RANSAC algorithm based on structural similarity to improve the speed and accuracy of traditional RANSAC（Random Sample Consensus） algorithm. Firstly, BRISK（Binary Robust Invariant Scalable Keypoints） algorithm is used to detect and describe feature points. The initial match set is obtained by hamming distance feature matching. Then, false match is eliminated by structural similarity constraint. Finally, the new match set is taken as the input of RANSAC to calculate the transformation matrix. The algorithm can obtain the transformation model quickly because it has purified matched points after the initial matching. Experiments show that the number of iterations and run time are obviously less than the traditional algorithm. Therefore, the proposed algorithm outperforms the traditional RANSAC algorithm in terms of both speed and accuracy.

Key words: Random Sample Consensus（RANSAC） algorithm, feature matching, structural similarity, purify matched points