Image matching algorithm combining SIFT with SSDA based on compressed sensing

被引：0

作者：

Xie, Xin ^{[1
]}

Xu, Yin ^{[1
]}

Liu, Qing ^{[2
]}

Xiong, Huandong ^{[1
]}

Hu, Fengping ^{[3
]}

Cai, Tijian ^{[1
]}

机构：

[1] School of Information Engineering, East China Jiaotong University, Nanchang

[2] School of Foreign Languages, Shanghai Normal University, Shanghai

[3] School of Civil Engineering, East China Jiaotong University, Nanchang

来源：

Journal of Information and Computational Science | 2015年 / 12卷 / 16期

基金：

中国国家自然科学基金;

关键词：

Adaptive threshold; Compressed sensing; Scale invariant feature transform; Sequential similarity detection algorithm;

D O I：

10.12733/jics20106922

中图分类号：

学科分类号：

摘要：

Considering the disadvantages of massive calculation and slow speed of traditional Scale Invariant Feature Transform (SIFT) algorithm, we proposed an improved image matching method which combines Compressed Sensing (CS) algorithm. The method works as follows. Firstly, target images and images to be matched are preprocessed and compressed using compressed sensing technology. Then, image feature points are extracted in combination with SIFT algorithm. Finally, Sequential Similarity Detection Algorithm (SSDA) with adaptive threshold is used to fast search of image matching to find an optimal matching position, and then a matching image is obtained. Experimental results demonstrate that the method realizes fast image matching, efficiently overcomes the shortcomings of heavy computation and low efficiency in the process of extracting image features, and guarantees the matching accuracy and efficiency, which meets the real-time requestments in machine vision system. © 2015 by Binary Information Press

引用

页码：6145 / 6153

页数：8

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