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中文摘要：
本文提出了一种基于RANSAC的SIFT匹配优化。采用加权的圆形邻域替代原有SIFT描述子矩形邻域，使得描述子维度降低了25%。根据特征点最近邻与次近邻的距离比越低，其匹配正确率越高的特点，对匹配点按最近邻比率高低进行匹配点排序，并以最优匹配点作为简化的RANSAC算法初始样本数据集，用简化的RANSAC算法进行几何校验，进一步提纯匹配点。实验结果表明本文方法在匹配精度优于RANSAC-SIFT的基础上，匹配速度大约提高了10倍。尤其当匹配点增多时，本文方法在匹配速度上更加有优势。
英文摘要：
An improved SIFT method is proposed. Firstly, the step of describing key points changes the rectangular region of the descriptor into a circular region. By calculating the weighted gradient orientation histogram for each partitioning, a descriptor with 96 dimensions is composed. Secondly, we emphasize that a smaller nearest neighbor ratio threshold leads to a highly accurate matching probability for the matched point but yields a low number of matching points. The matching points are ranked by the distance threshold of the nearest neighbor to the second nearest neighbor. We finally simplify RANSAC by establishing a new dataset based on the optimal matched points. The experimental results demonstrate that our approach enhances computation efficiency （about 10 times） and slightly improves accuracy than other algorithm. When the matching feature points increases, our method has more advantages in matching speed and higher matching accuracy than RANSAC-SIFT.
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