An Efficient Corner Detector Using Ratio of Center Distance of Symmetric Contour Technique

Abstract

Feature detection is one of the basic research topics in the fields of image processing and corner that has been widely applied on vehicle detection, UAV image matching, camera calibration and so on. Particularly, a fast corner detector is of much benefit to many real-time tasks. A novel discrete curvature estimation method for corner detection based on the ratio of center distances of symmetric contour (RCDSC) is proposed in this paper. Benefiting from calculating the Euclidean distance twice only to estimate the discrete curvature at each point on a contour, RCDSC is much fast compared with other corner detectors. In addition, by choosing a relatively large radius of region of support and employing relative distance instead of absolute distance for constructing corner response function, RCDSC is also much robust to noises and local variations of contour. Extensive experiments exhibit the effectiveness and the efficiency of RCDSC in terms of average repeatability (AR), accuracy (ACU) and localization error (LE).