[1]王卫兵,白小玲,徐倩. SURF 和RANSAC 的特征图像匹配[J].哈尔滨理工大学学报,2018,(01):117-121.[doi:10. 15938 /j. jhust. 2018. 01. 021]
 WANG Wei-bing,BAI Xiao-ling,XU Qian. Features Image Matchingof SURF and RANSAC[J].哈尔滨理工大学学报,2018,(01):117-121.[doi:10. 15938 /j. jhust. 2018. 01. 021]
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 SURF 和RANSAC 的特征图像匹配()
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2018年01期
页码:
117-121
栏目:
材料科学与工程
出版日期:
2018-02-25

文章信息/Info

Title:
 Features Image Matchingof SURF and RANSAC
文章编号:
1007- 2683( 2018) 01- 0117- 05
作者:
 王卫兵1 白小玲1 徐倩2
 ( 1. 哈尔滨理工大学计算机科学与技术学院,黑龙江哈尔滨150080;
2. 黑龙江电力有限公司哈尔滨供电公司,黑龙江哈尔滨150000)
Author(s):
 WANG Wei-bing1 BAI Xiao-ling1 XU Qian2
 ( 1. School of Computer Science and Technology,Harbin University of Science and Technology,Harbin 150080,China;
2. Heilongjiang Electric Power Ltd. Harbin District,Harbin 150000,China)
关键词:
 特征提取 加速鲁棒特征 随机采样一致性
Keywords:
feature matching speed up robust features random sample consensus
分类号:
TP391. 4
DOI:
10. 15938 /j. jhust. 2018. 01. 021
文献标志码:
A
摘要:
 针对图像匹配过程中存在匹配运行时间长、匹配正确率低的问题,采用随机采样一致
性( random sample consensus,RANSAC) 算法优化加速鲁棒特征( speed up robust features,SURF) 的
方法,提出一种适应性强的优化匹配算法。首先使用SURF 算子进行特征检测和特征描述,再使用
邻近算法对特征点进行预匹配,最后使用随机采样一致性( RANSAC) 算法优化匹配结果。在相同
的实验环境中通过对尺度不变特征变换( scale invariant feature transform,SIFT) 算法、SURF 算法和
提出的优化算法进行比较,优化算法较SIFT 算法和SURF 算法分别减少匹配点对数38 对和18
对,剔除了误匹配点,提高了匹配正确率并减少了算法的运行时间。
Abstract:
 Aiming at the problem of long running time and low matching accuracy in image matching process,
random sample consensus ( RANSAC) algorithm is used to optimize the speed-up of robust features ( SURF)
optimization algorithm. As a result,an adaptable algorithm is proposed to optimize image matching. Firstly,the
SURF operator is used for feature detection and feature description. Then the neighbor algorithm is used to prematch
the feature points. Finally,the random sampling consistency ( RANSAC) algorithm is used to optimize the
matching results. The scale invariant feature transform ( SIFT) algorithm,SURF algorithm,and the proposed
optimization algorithm are compared in the same experimental environment. Compared with the SIFT algorithm and
the SURF algorithm,the optimization algorithm reduces the number of matching point pairs to 38 and 18 pairs,
excluding mismatched points,improving the matching accuracy,and reducing the running time of the algorithm.

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更新日期/Last Update: 2018-05-24