An Improved ORB Feature Point Matching Algorithm

被引：7
|
作者：
Zhang, Yang [1 ]
Li, Chao [1 ]
Cao, Chuqing [1 ]
Gao, Yunfeng [2 ]
机构：
[1] HIT Wuhu Robot Technol Res Inst, Wuhu, Anhui, Peoples R China
[2] Harbin Inst Technol, Sch Mechatron Engn, Harbin, Heilongjiang, Peoples R China
来源：
PROCEEDINGS OF 2018 THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND ARTIFICIAL INTELLIGENCE (CSAI 2018) / 2018 THE 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND MULTIMEDIA TECHNOLOGY (ICIMT 2018) | 2018年
关键词：
ORB; BRISK; scale invariance; feature point matching;
D O I：
10.1145/3297156.3297184
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
ORB, a very fast binary descriptor based on BRIEF, which is rotation invariant and resistant to noise. To solve the problem that the traditional ORB feature descriptor does not have scale invariance, an improved ORB feature point matching algorithm which borrowing the idea of Binary Robust Invariant Scalable Keypoints (BRISK) algorithm to uniformly sample the extracted feature points is proposed in this paper. The experimental results show that the algorithm not only inherits the advantages of the high speed from ORB algorithm but also has good scale invariance. It is suitable for applications requiring high real-time and large scale changes.
引用
收藏
页码：207 / 211
页数：5
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