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《Computer Knowledge and Technology》 2019-01
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Improved Image Matching Algorithm Based on SIFT Algorithm

ZHANG Lei;WANG Guo-Ming;College of Computer Science and Engineering,Anhui University of Science &Technology;  
The constraint condition of the traditional SIFT image matching algorithm is relatively single,which leads to the fact that the SIFT algorithm has many similar features in the original image,the problem of mismatched is relatively obvious,and the mismatched points cannot be effectively eliminated.To solve this problem,an image matching method based on SIFT operator fusion vision dictionary and adding vector constraints is proposed.First,k-mean method is used to cluster the feature point set of SIFT,and then the k-nearest neighbor graph structure is generated to complete the initial matching.Then,on the basis of the Euclidean distance constraint,the constraint condition of the traditional SIFT algorithm was improved by adding the vector similarity constraint,and the matching point was screened according to the correlation coefficient to complete the accurate matching.Experimental results show that the algorithm is effective.
【Fund】: 面向事件的知识处理研究(gxyq2017007)安徽高校优秀青年人才支持计划项目
【CateGory Index】: TP391.41
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