计算机工程与应用2018，Vol.54Issue(3)：200-205,232,7.DOI:10.3778/j.issn.1002-8331.1610-0004
改进的SIFT算法图像匹配研究
Research on image matching based on improved SIFT algorithm
冯文斌 1刘宝华2
作者信息
- 1. 燕山大学 河北省并联机器人与机电系统实验室,河北 秦皇岛 066004
- 2. 燕山大学 机械工程学院,河北 秦皇岛 066004
- 折叠
摘要
Abstract
For the feature descriptors'dimensions based on the SIFT algorithm are too high, resulting to low speed, low matching rate and other issues, a kind of hierarchical radial partition method is proposed to construct feature descriptor. The feature point neighborhood is divided into 8 regions, counting 8 directions'gradient direction histogram in each region to get a 64 dimensions descriptor, which the dimensions of feature descriptors are reduced by 50％. At the same time, because the Mahalanobis distance considering the correlation between the feature descriptor vectors, using the two-direction matching method of Mahalanobis distance instead of Euclidean distance when matching, the RANSAC method is used to eliminate the mismatch points. The theoretical analysis and simulation results show that improved SIFT algorithm retains SIFT algorithm for fuzzy, compression, rotation and scaling invariance advantages, improves the matching speed, and the average rate of true-match increases of 10％～15％.关键词
尺度不变特征变换/特征描述子/马氏距离/欧氏距离/随机抽样一致性Key words
Scale-Invariant Feature Transform(SIFT)/feature descriptor/Mahalanobis distance/Euclidean distance/Random Sample Consensus(RANSAC)分类
信息技术与安全科学引用本文复制引用
冯文斌,刘宝华..改进的SIFT算法图像匹配研究[J].计算机工程与应用,2018,54(3):200-205,232,7.
