改进的双向SIFT特征匹配算法

以基于图像序列摄像机自标定为基础,针对尺度不变特征转换SIFT算法误匹配率高且运行效率低的问题,提出一种改进的双向SIFT特征匹配算法。在去除误匹配方面,首先采用双向匹配消除部分误匹配点对,然后结合视差梯度约束算法和随机抽样一致性RANSAC算法提纯匹配点对;在提高运行速度方面,首先在初匹配中采用K邻近算法,其次调整视差梯度约束迭代条件,都通过减少迭代次数来降低算法耗时。实验表明,改进后的算法在去除了大部分误匹配的基础上,保留了足够的匹配点对以用于摄像机空间位置和姿态的自动标定,且相较SIFT算法在运行速度上有了较大的改进。

An improved bidirectional SIFT feature matching algorithm

Based on camera self calibration of image sequence, we propose an improved bidirectional SIFT feature matching algorithm to solve the problems of high mismatching rate and low operation efficiency of the SIFT algorithm. To remove the mismatching, two steps are to follow. First, the SIFT bidirectional matching algorithm is leveraged to eliminate part of the mismatching. Second, disparity gradient constraint and the RANSAC algorithm are used to purify the matching points. Regarding the speed improvement, we utilize the K nearest algorithm at the beginning of matching, and disparity gradient constraints are also adjusted, reducing the iteration count to lower the time consumption. Experimental results show that the proposed algorithm removes most of the mismatching and retains enough matching points to automatically calibrate the position and orientation of the cameras in the space, and it can effectively reduce the execution time.