Corners play important roles in high level image understanding. They are the main features in many 2D or 3D image models associated with image understanding algorithms. The Bayesian corner detection method inputs a sequence of row-column pairs along an arc and outputs the corner positions and the corner included angles that maximize the a posteriori probability. Experiments on artificially generated sequences permit the measurement of errors of the estimated corner positions and included angles versus different noise perturbations, angles and line lengths respectively.