Computing a curvature function on a digitized boundary is an ill-posed problem due to the discrete nature of the boundary. The authors use a constrained regularization technique to obtain the optimal smooth boundary before computing the curvature function. A corner sharpness is defined for robust corner point detection. Matching results in the presence of occlusion using a 2-D Hopfield neural network are also shown to produce excellent results using this boundary representation. The human cognition system recognizes both ideal corner points and slightly rounded segments as corner points. A criterion to mimic a human's capability of detecting corner points and to compensate for the smoothing effect of the preprocessing in detecting corner points in the curvature function space is established.