自适应邻域测试的图像误匹配点剔除算法

针对图像特征点暴力匹配与比率测试得到的匹配点对在数量与正确率不能兼顾的情况，提出了一种基于自适应邻域测试的误匹配点对剔除算法。对特征点进行暴力匹配与高阈值的比率测试得到初始匹配点集，对初始匹配点对中的每个匹配特征点进行自适应邻域测试，测试出初始匹配点集中明显的误匹配点对并将之剔除，达到只剔除误匹配而不会误剔除正确匹配的效果。实验结果表明，在保证正确率不降低的前提下，该算法获取的匹配点对数量比原算法多3成以上，并且该算法对图像旋转、尺度缩放具有较好通用性。

Research on Algorithm of Eliminating Mismatching Points Based on Adaptive Neighborhood Test

Aiming at the situation that the matching pair of image feature point brute-match and ratio test can’t take into account both the quantity and the correct rate, an algorithm based on adaptive neighborhood test is proposed to eliminate the mismatched pair. Firstly, the ratio of violent matching and high threshold of feature points is tested to obtain the initial matching point set, then adaptive neighborhood testing is performed on each matching feature point in the initial matching point pair, it tests out the obvious mismatch points in the initial matching points and removes them, achieves the effect of removing only false matches without mistakenly removing the correct match. The experimental results show that the number of matching pairs obtained by this algorithm is more than 30% higher than the original algorithm, on the premise that the correct rate is not reduced, and the algorithm has good versatility for image rotation and scaling.