Corner detection based on gradient correlation matrices of planar curves

An efficient and novel technique is developed for detecting and localizing corners of planar curves. This paper discusses the gradient feature distribution of planar curves and constructs gradient correlation matrices (GCMs) over the region of support (ROS) of these planar curves. It is shown that the eigen-structure and determinant of the GCMs encode the geometric features of these curves, such as curvature features and the dominant points. The determinant of the GCMs is shown to have a strong corner response, and is used as a “cornerness” measure of planar curves. A comprehensive performance evaluation of the proposed detector is performed, using the ACU and localization error criteria. Experimental results demonstrate that the GCM detector has a strong corner position response, along with a high detection rate and good localization performance.