Paper Info
Title
Robust corner detection using the eigenvector-based angle estimator.
Abstract
Based on eigenvector calculation, we proposed a new angle estimator for corner detection (EAE).To improve the performance of EAE on uniform scaling, we further proposed the enhance version of EAE (WEAE).Compared with some state-of-art corner detection methods, the proposed detectors are both effective and efficient. Angle is an intuitive and important property for representing corners. This fact motivates us to present a novel angle-based corner detector, named Eigenvector-based Angle Estimator (EAE). EAE estimates the angle of each point in a contour via computing the eigenvectors of the covariance matrix of boundary points over a small Region of Support (RoS). Since EAE is sensitive to uniform scaling due to the fixed RoS, an enhanced version of EAE named Weighted EAE (WEAE) is proposed. WEAE achieves robustness to uniform scaling by weighting the boundary points using their distances from the target point. Experimental results demonstrate that EAE and WEAE can efficiently achieve promising performance in comparisons with several recent state-of-the-art approaches under two commonly used evaluation metrics, namely, Average Repeatability (AR) and Localization Error (LE).
Year
DOI
Venue
2017
10.1016/j.jvcir.2017.01.020
J. Visual Communication and Image Representation
Keywords
Field
DocType
Eigenvector,Angle estimation,Curvature,Corner detection,Repeatability,Localization error
Weighting,Corner detection,Robustness (computer science),Artificial intelligence,Detector,Scaling,Eigenvalues and eigenvectors,Mathematical optimization,Pattern recognition,Algorithm,Covariance matrix,Mathematics,Estimator
Journal
Volume
Issue
ISSN
45
C
1047-3203
Citations 
PageRank 
References 
0
0.34
31
Authors
6
Name
Order
Citations
PageRank
shizheng zhang100.68
Dan Yang2667.49
sheng huang3358.26
Xiaohong Zhang411821.88
liyun tu5112.87
zemin ren6102.52