Combining Boundary and Skeleton Information for Convex and Concave Points Detection

Abstract

Basically, detecting convex and concave points on the boundary of an object plays an important role in computer vision, object recognition and image understanding. In this paper a method that combines boundary and skeleton information for detecting these critical points is proposed. Specifically, the method is developed with the aim of obtaining high performance and efficiency, and producing a more robust method in detecting concave and convex points with minimum cost of computation. Furthermore, for faster execution of the proposed method, the detection of convex and concave points can be run concurrently. In order to evaluate the performance of the proposed method, the results of the proposed method are compared with three related convex and concave points detection methods. The experimental results have shown that the proposed method provides better output and detection rate.