Image registration algorithm with hypergraph constraint and improved normalized cross correlation method

被引：1
|
作者：
Zhu M. [1 ,2 ]
Yao Q. [1 ]
Tang J. [1 ]
Zhang Y. [1 ]
机构：
[1] School of Electronics and Information Engineering, Anhui University, Hefei
[2] Key Laboratory of Polarization Imaging Detection Technology in Anhui Province, Hefei
来源：
Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | 2019年 / 41卷 / 03期
关键词：
Affine invariance; Hypergraph; Image registration; Mahalanobis distance;
D O I：
10.11887/j.cn.201903009
中图分类号：
学科分类号：
摘要：
In order to improve the accuracy and adaptability of image registration algorithm, the hypergraph constraint and the improved NCC(normalized cross correlation) were applied to image registration. The proposed algorithm used the Hessian-Affine detection affine invariant region instead of the fixed window to improve the NCC method and obtained the initial matching point pairs. The similarity degrees between the hyperedges of hypergraph were calculated by Martensitic distance, and the matching scores of the matching pairs calculated by hypergraph constraint were used to sort the matching pairs. The transformation matrix was initialized with some matching points of higher matching scores, and was circularly updated by filtering matching pairs to get the optimal transformation matrix, which was used to implement registration. Experimental results show that the proposed method has better performance in matching and rejecting mismatch, and it also has better registration performance in different types of image registration. © 2019, NUDT Press. All right reserved.
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页码：50 / 55
页数：5
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