关键词: 单应矩阵, 估计, 尺度不变特征转换（SIFT）算法, 随机抽样一致性（RANSAC）算法, 特征匹配

Abstract: The robustness and precision of the homography matrix directly determine its application effect. How to use the RANSAC algorithm to estimate the robust and accurate homography matrix is still a hot issue to be studied. For the problem that the traditional RANSAC algorithm has many iterations, long running and low accuracy of homography matrix estimation, on the basis of SIFT feature matching algorithm, RANSAC algorithm is improved by eliminating some of the external points of the image, which is not in conformity with the geometrical characteristics of the image, and quickly discards the unreasonable homography matrix and iterative refinement of the homography matrix and so on, it presents a method for homography matrix estimation based on improved RANSAC algorithm, improves the accuracy and efficiency of the homography matrix estimation. The experimental results show that the method can effectively solve the problems of the traditional RANSAC algorithm, and can quickly and accurately estimate the homography matrix. In addition, the method has better robustness for different views and sizes of images.

Key words: homography matrix, estimation, Scale Invariant Feature Transform（SIFT）, Random Sample Consensus（RANSAC）, feature matching