Corner detection using arc length-based angle estimator

Abstract. We present a corner-detection method named arc length-based angle estimator (AAE). Different from most of the existing approaches, AAE focuses on employing angle detection for finding corners, because angle is an important measure for discrete curvature. AAE provides a new robust solution to the estimation of the K-cosine. In AAE, the K-cosine estimation issue in the x, y space is considered as the problem of the slope estimations in the s, x and s, y spaces, where s is the arc length. Then, weighted least square fitting is employed to address such a slope estimation issue. Experimental results demonstrate that AAE can achieve promising performance in comparison with some recent state-of-the-art approaches under two commonly used evaluation metrics, namely average repeatability and localization error criteria.