基于特征的匹配算法是图像配准的重要内容，针对传统SIFT匹配法存在的重复匹配、多对一匹配、正确率不高等问题，本文提出了基于标准化欧式距离的双向特征匹配算法。该算法首先通过SIFT算法对特征点进行提取，然后用标准化欧氏距离对特征描述符进行度量，接着采用双向特征匹配算法对特征点进行匹配，最后以RANSAC算法对匹配对进行提纯。实验结果表明，使用标准化欧氏距离进行双向匹配，具有更高的准确率。

Feature based matching algorithm is important in part of image registration. In view of repeated matching, many-to-one matching and low accuracy of traditional SIFT matching method, a bidirectional feature matching algorithm based on standardized euclidean distance is proposed in this paper. Firstly, the feature points are extracted by SIFT algorithm. Then the feature descriptors are measured by normalized Euclidean Distance and the feature points are matched by bidirectional feature matching algorithm. Finally, the matching pairs are purified by RANSAC algorithm. The experimental results show that the use of standardized Euclidean Distance for bidirectional matching has higher accuracy.