Authors:
Zoltán Pusztai
1
and
Levente Hajder
2
Affiliations:
1
MTA SZTAKI and Eötvös Loránd University Budapest, Hungary
;
2
MTA SZTAKI, Hungary
Keyword(s):
Quantitative Comparison, Feature Points, Matching.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Geometry and Modeling
;
Image and Video Analysis
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
;
Pattern Recognition
;
Software Engineering
;
Stereo Vision and Structure from Motion
;
Tracking and Visual Navigation
Abstract:
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.