###
DOI:
计算机系统应用英文版:2016,25(5):147-152
本文二维码信息
码上扫一扫!
基于改进ORB和对称匹配的图像特征点匹配
(广东工业大学 应用数学学院, 广州 510520)
Image Keypoints Matching Based on Improved ORB and Symmetrical Match
(Guangdong University of Technology, Guangzhou 510520, China)
摘要
图/表
参考文献
相似文献
本文已被:浏览 1339次   下载 3074
Received:September 06, 2015    Revised:October 14, 2015
中文摘要: 由于ORB算法所提取的特征点不具有尺度不变性,直接匹配会导致较多的错误发生,本文结合SURF和双向匹配算法的思想,提出了改进的ORB算法:SSORB.首先使用不同尺寸盒状滤波模板与积分图像生成多尺度空间,并从中检测出稳定的极值点,使得所提取出来的特征点具备尺度不变的特性;然后使用ORB描述子对特征点进行描述,得到旋转不变的二进制描述子;由于误匹配的存在,在Hamming距离的基础上进一步使用双向匹配来消除误匹配,提高匹配精度.实验结果表明,SSORB有效地解决了ORB不具备尺度不变性的缺陷,在保留ORB算法快速优越性的同时提高了匹配准确度.
中文关键词: 特征点匹配  改进ORB  SURF  双向匹配
Abstract:Given the ORB algorithm has no scale invariance and exists more false match in image keypoints matching, this paper combines SURF and bilateral matching algorithm to improve ORB. The improved ORB algorithm is named SSORB(scale invariance and symmetrical matching ORB). First, we generate multi-scale space by different sizes of box filter template convoluting with integral image. Second, we detect the stable extreme points in multi-space, making the extracted points invariant to scale. Third, we describe these points with ORB descriptor making them invariant to rotation. At last, according to the Hamming distance match these points. Due to the existence of error match, bilateral matching is used to improve the match accuracy by eliminating the false match. The experimental results show that the SSORB algorithm effectively solves the scale invariant defects of the ORB algorithm, meanwhile, keeps the speed advantage of ORB and improves the matching accuracy.
文章编号:     中图分类号:    文献标志码:
基金项目:
引用文本:
陈天华,王福龙,张彬彬.基于改进ORB和对称匹配的图像特征点匹配.计算机系统应用,2016,25(5):147-152
CHEN Tian-Hua,WANG Fu-Long,ZHANG Bin-Bin.Image Keypoints Matching Based on Improved ORB and Symmetrical Match.COMPUTER SYSTEMS APPLICATIONS,2016,25(5):147-152