An Improved RANSAC Algorithm Based on Similar Structure Constraints

An improved RANSAC algorithm based on structural similarity was proposed to improve the speed and accuracy of traditional RANSAC (Random Sample Consensus) algorithm and to reduce iterations and runtime. Firstly, BRISK (Binary Robust Invariant Scalable Keypoints) algorithm was used to extract and describe feature points. The initial match set is obtained by hamming distance feature matching. Then, mismatches are eliminated by similar structure constraints. 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 matching points after the initial matching. Experiments show that the number of iterations and runtime of this algorithm are obviously less than the number of iterations and runtime of the traditional algorithm. So the proposed method outperforms traditional RANSAC (Random Sample Consensus) algorithm significantly both in iterations and speed.