一种改进的FREAK算法的图像特征点匹配

摘要

FREAK algorithm has the defects of not having scale invariance and single feature point matching strategy,and being prone to unsatisfactory results.Based on SIFT and RANSAC algorithm,an improved FREAK algorithm is proposed:SFREAK (SIFT and FREAK).First of all,in the generation of Gauss differential Pyramid image,the feature points are detected with scale invariance;then the feature points are described with FREAK descriptor,obtaining binary descriptor;finally,in the process of feature points matching using Hamming distance matching for coarse matching,the matching points are purified with RANSAC algorithm,and the feature points of two images are matched.The experimental results show that the proposed algorithm can effectively solve the FREAK not having scale invariance in image scaling,and SFREAK algorithm for feature point matching accuracy rate reached 95.7％,increased by 61.9％ compared with FREAK.Therefore,compared with the traditional SIFT algorithm and FREAK algorithm,the improved algorithm shows better robustness.%由于FREAK算法存在不具备尺度不变性的缺陷,特征点匹配策略单一容易出现匹配效果不理想的问题,鉴于SIFT和RANSAC算法思想,本文提出一种改进的FREAK算法:SFREAK (SIFT and FREAK).首先,生成高斯差分金字塔图像,使检测出来的特征点具有尺度不变性;然后采用FREAK描述符对特征点进行描述,获得二进制描述子;最后,在特征点匹配过程中通过Hamming距离匹配进行粗匹配,并结合RANSAC算法对匹配点对进行提纯,实现两幅图像的特征点匹配.实验结果表明,本文提出的改进算法有效解决了FREAK不具备尺度不变性的缺陷,在图像发生尺度变化时,SFREAK算法特征点匹配准确率达到95.7％,相比于FREAK提高了61.9％.另外,本文改进的算法与传统SIFT、FREAK算法相比,表现出更好的鲁棒性.