Robust corner detection using the eigenvector-based angle estimator

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).