Quantitative Comparison of Affine Invariant Feature Matching

被引：3
作者：
Pusztai, Zoltan [1 ,2 ]
Hajder, Levente [1 ]
机构：
[1] MTA SZTAKI, Machine Percept Res Lab, Kende Utca 13-17, H-1111 Budapest, Hungary
[2] Eotvos Lorand Univ Budapest, Budapest, Hungary
来源：
PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 6 | 2017年
关键词：
Quantitative Comparison; Feature Points; Matching;
D O I：
10.5220/0006263005150522
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
It is a key problem in computer vision to apply accurate feature matchers between images. Thus the comparison of such matchers is essential. There are several survey papers in the field, this study extends one of those: the aim of this paper is to compare competitive techniques on the ground truth (GT) data generated by our structured-light 3D scanner with a rotating table. The discussed quantitative comparison is based on real images of six rotating 3D objects. The rival detectors in the comparison are as follows: Harris-Laplace, Hessian-Laplace, Harris-Affine, Hessian-Affine, IBR, EBR, SURF, and MSER.
引用
收藏
页码：515 / 522
页数：8
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