Corner detection using a mixture model of edge orientation

Corners are important image features, whose detection has proved elusive since they are associated with the locations of multiple edge orientation. In this paper we explicitly model the multiple orientations by using a mixture model based on von Mises edge-direction distributions. In order to infer the parameters of the mixture model, a bank of Gabor filters is employed. The resulting mixture model of the local contour orientation captures angular information at edges, corners and T-junctions in a single process. A criterion can then be developed which isolates corners as points of multiple edge orientations. We demonstrate the effectiveness of our method on natural images and compare the quantitative performance with a number of popular alternatives in the literature.