Self-adaptive image feature matching algorithm based on gridmotion statistics

被引：0
|
作者：
Liu C. [1 ]
Ai Z. [1 ]
Zhao L. [1 ]
机构：
[1] School of Control and Computer Engineering, North China Electric Power University, Beijing
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2020年 / 48卷 / 01期
关键词：
Error matching elimination; Feature point; Grid division; Grid motion statistics; Self-adaptive;
D O I：
10.13245/j.hust.200107
中图分类号：
O212 [数理统计];
学科分类号：
摘要：
In order to solve the problem that the performance of grid motion statistics(GMS) algorithm depends on the number of feature points and there exists mismatching concentration when feature points are detected less, an adaptive feature matching algorithm based on grid motion statistics was proposed with the idea of consistency constraints.By introducing grid division into the detection image, the self-adaptive threshold was set for each grid region to detect feature points in turn.Then, the rotated BRIEF (rBRIEF) was used to describe feature points and feature point matching was completed based on hamming distance.Finally, the GMS algorithm was adopted to eliminate the initial mismatching, and random sample consensus algorithm was used to select the accurate matching points.Experimental results show that the algorithm can effectively eliminate mismatching points, and improve matching quality with high real-time performance, and also has good robustness for image matching with low texture structure. © 2020, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：37 / 40and54
页数：4017
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