Boundary based corner detection and localization using new ´cornerity´ index: a robust approach

In this paper, a novel boundary based corner detection algorithm is proposed. The proposed algorithm is computationally fast and efficient. The proposed method computes an expected point for every point on a boundary curve. An expected point corresponding to a point on the boundary curve is defined to be the geometrical centroid of the symmetrical boundary segment of size 2k+1, for some integer k>0, within the neighborhood of the point in consideration. A new ´cornerity´ index for a point on the boundary curve is defined to be the distance between the point and its corresponding expected point. The larger the cornerity index, the stronger is the evidence that the boundary point is a corner point. A set of rules is worked out to guide the process of locating true corner points. The conducted experiments establish that the proposed approach is invariant to image transformations viz., rotation, translation and scaling.