A constrained regularization approach to robust corner detection

This paper presents a method of optimal boundary smoothing for curvature estimation and a method of corner detection for consistent representation of objects for computer vision applications. The existing methods for curvature estimation have a common problem in determining a unique smoothing factor. We propose a constrained regularization (CR) approach to overcome that problem. The curvature function computed on the preprocessed boundary, which is obtained by the CR approach, gives consistent corner detection results. Ideal corners rarely exist for a real boundary. They are often rounded due to the smoothing effects of the preprocessing. In addition, a human recognizes both sharp corners and slightly rounded segments as corners. Hence, we establish a criterion, called “corner sharpness”, which is qualitatively similar to a human´s capability to detect corners