Image Matching Algorithm based on ORB and K-means Clustering

被引：4

作者：

Zhang, Liye ^{[1
,2
,3
]}

Cai, Fudong ^{[2
]}

Wang, Jinjun ^{[1
]}

Lv, Changfeng ^{[2
]}

Liu, Wei ^{[2
]}

Guo, Guoxin ^{[2
]}

Liu, Huanyun ^{[4
]}

Xing, Yixin ^{[3
]}

机构：

[1] Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian, Peoples R China

[2] Shandong Senter Elect, Elect Power Automat Div, Zibo, Peoples R China

[3] Shandong Univ Technol, Sch Comp Sci & Technol, Zibo, Peoples R China

[4] Jinan Xintongda Elect Technol Co Ltd, Platform Algorithm Dept, Jinan, Peoples R China

来源：

2020 5TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE, COMPUTER TECHNOLOGY AND TRANSPORTATION (ISCTT 2020) | 2020年

关键词：

ORB; K-means; sub-pixel interpolation; image matching; binocular stereo vision;

D O I：

10.1109/ISCTT51595.2020.00088

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

With the rapid development of science and technology, image processing technology plays an important role in the field of computer vision. In order to improve the matching speed and real-time requirements, this paper proposes an image matching algorithm based on ORB and K-means clustering, which can effectively improve the accuracy of image feature point location and the accuracy and efficiency of image feature matching, and reduce the time consumption. The algorithm uses sub-pixel interpolation to optimize the traditional ORB algorithm, which improves the accuracy and characteristics of clustering calculation.

引用

页码：460 / 464

页数：5

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