Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (4): 1133-1137.DOI: 10.11772/j.issn.1001-9081.2019091588

• Virtual reality and multimedia computing • Previous Articles     Next Articles

Local feature point matching algorithm with anti-affine property

QIU Yunfei, LIU Xing   

  1. School of Software Engineering, Liaoning Technical University, Huludao Liaoning 125105, China
  • Received:2019-09-18 Revised:2019-10-18 Online:2020-04-10 Published:2019-10-28
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China(71771111).


邱云飞, 刘兴   

  1. 辽宁工程技术大学 软件学院, 辽宁 葫芦岛 125105
  • 通讯作者: 刘兴
  • 作者简介:邱云飞(1976-),男,辽宁阜新人,教授,博士,CCF会员,主要研究方向:数据挖掘、情感分析;刘兴(1995-),男,辽宁沈阳人,硕士研究生,主要研究方向:数据挖掘、图像处理。
  • 基金资助:

Abstract: In order to solve the problems that the existing local feature matching algorithm has poor matching effect and high time cost on affine images,and RANdom SAmple Consensus(RANSAC)algorithm cannot obtain a good parameter model on affine image matching,Affine Accelerated KAZE(A-AKAZE)algorithm with anti-affine property was proposed and the vector field consistency was used to screen interior points. Firstly,the scale space was constructed by using the nonlinear function,then the feature points were detected by Hessian matrix,and the appropriate areas were selected as the feature sampling windows with the feature points as the centers. Secondly,the feature sampling windows were projected on longitude and latitude to simulate the influence of different angles on the image,and then the Affine Modified-Local Difference Binary(A-MLDB)descriptors with anti-affine property were extracted from the projection region. Finally,the interior points were extracted by the vector field consistency algorithm. Experimental results show that the correct matching rate of A-AKAZE algorithm is more than 20% higher than that of AKAZE algorithm,is about 15% higher than that of AKAZE+RANSAC algorithm,is about 10% higher than that of Affine Scale-Invariant Feature Transform(ASIFT)algorithm, and is 5% higher than that of ASIFT+RANSAC algorithm;at the same time,A-AKAZE algorithm has the matching speed much higher than AKAZE+RANSAC,ASIFT and ASIFT+RANSAC algorithms.

Key words: affine invariant, Accelerated KAZE (AKAZE) algorithm, vector field consistency, Modified-Local Difference Binary (MLDB) descriptor, image matching

摘要: 针对现有局部特征匹配算法对具有仿射性的图像匹配效果欠佳、耗时较长,以及随机采样一致性(RANSAC)算法对仿射性图像匹配得不到较好的参数模型等问题,提出一种具有抗仿射性的A-AKAZE(Affine Accelerated KAZE)算法,并用向量场一致性来筛选内点。首先利用非线性函数构建尺度空间,然后借助Hessian矩阵检测特征点,并以特征点为中心选取合适的区域作为特征采样窗口;再把特征采样窗口在经纬度上进行投影以模拟不同角度对图像的影响,随后在投影区域中提取具有抗仿射性的A-MLDB(Affine Modified-Local Difference Binary)描述符;最后利用向量场一致性算法提取内点。实验结果表明:A-AKAZE算法的正确匹配率相较于AKAZE算法提高了20%以上,与AKAZE+RANSAC算法相较提升了15%左右,与ASIFT(Affine Scale-Invariant Feature Transform)算法相比提高了10%左右,相比ASIFT+RANSAC算法提高了5%;而且该算法的匹配速度远高于AKAZE+RANSAC、ASIFT和ASIFT+RANSAC算法。

关键词: 仿射不变, AKZAE算法, 向量场一致性, MLDB描述符, 图像匹配

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