摘要
为了解决光电图像匹配过程中特征点错配率较高的问题,本文提出了一种基于SURF特征点的匹配方法。该算法首先利用最近邻欧氏距离比率法对提取的SURF特征做粗匹配,然后获取特征点对应尺度的邻域灰度统计信息,进而利用Pearson相关系数比得到鲁棒性较强的匹配对。实验表明该方法能够有效提高匹配的准确率,且满足实时性要求。
In order to solve the problem of the high mismatching rate of feature points in course of image matching, a novel matching strategy based on SURF feature points is propose. Euclidean nearest neighbor distance ratio method is used to match the extracted SURF features roughly, and then statistical information of the corresponding gray neighborhood of each feature point is obtained. Then, more robustness matching pairs can be gotten with Pearson correlation coefficient. Experimental results show that this method can effectively improve the matching accuracy and meet real-time requirements.
出处
《激光与红外》
CAS
CSCD
北大核心
2014年第3期347-350,共4页
Laser & Infrared
