摘要
针对传统SURF的图像匹配算法存在计算数据复杂、耗时长、匹配正确率不佳等问题,提出一种基于改进SURF的图像匹配算法.首先,用传统SURF算法来提取待匹配图像的特征点,再通过圆形区域代替矩形区域将SURF的64维度描述符降到20维度;采用KNN,来双向匹配待匹配图像的特征点,得到双向的初始特征点匹配对集;最后,通过RANSAC算法对初始匹配对集进行双向剔除错误的匹配对.实验的结果表明,本文算法减少了特征点检测时间,提高了匹配正确率,还有较好的鲁棒性.
An improved SURF-based image matching algorithm is proposed for the traditional SURF image matching algorithm which has the problems of complex computing data,time-consuming,and poor matching accuracy.Firstly,the traditional SURF algorithm is employed to extract the feature points of the image to be matched,and then the 64-dimensional descriptor of SURF is reduced to 20 dimensions by replacing the rectangular area with a circular area.Secondly,the KNN algorithm is utilized to bidirectionally match the feature points of the image to be matched,and the matching pair set of bidirectional initial feature points is obtained.Finally,the mismatching pairs of initial matching points are eliminated bidirectionally by the RANSAC algorithm.The experimental results show that the proposed algorithm reduces the detection time,improves the matching accuracy,and has strong robustness.
作者
邹玉英
杨振玲
刘立东
ZOU Yu-Ying;YANG Zhen-Ling;LIU Li-Dong(School of Information Engineering,Chang’an University,Xi’an 710064,China)
出处
《计算机系统应用》
2022年第12期322-328,共7页
Computer Systems & Applications
基金
陕西省自然科学基金(2020JM-220)
关键词
图像匹配
SURF算法
降维
双向匹配
RANSAC
images matching
SURF algorithm
dimensionality reduction
bidirectional matching
RANSAC
作者简介
通信作者:邹玉英,E-mail:1139156512@qq.com
