Corner detection using the MAP technique

This paper describes a corner detection method that obtains maximum a posteriori estimates for the corner location in a given sequence of points. The authors model an ideal corner as the intersection of two ideal line segments. The perturbations on the sample points in a given line segment are assumed to be i.i.d Gaussian random variables of zero mean and variance σ². Further, the perturbations on the points are assumed to be orthogonal to the ideal line. The paper discusses the theory of the corner detector and an algorithm that extends the basic theory to handle multilinear segment arcs. Experiments were conducted according to a specific protocol and performance curves showing the location error versus the noise variance, the included corner angle, and the arc length, are provided. Performance characterization of the corner detector is also performed by plotting the false alarm rate and the misdetect rate versus the context window length and included corner angle. It is shown that the experimental results match the theoretical error propagation