Edge Detection by Spherical Separation

We describe an optimization-based method for tackling the classic image processing problem known as edge detection and we formulate it in the form of a classification one. The novelty of the approach is in the use of spherical separation as a classification tool in the image processing framework. Spherical separation consists in separating by means of a sphere two given discrete point-sets in a finite dimensional Euclidean space; in our context the two sets are the edge points and the non-edge points, respectively, in the digital representation of a given image. Assuming that the center of the sphere is fixed, the problem reduces to the minimization of a convex and nonsmooth function of just one variable, which can be effectively solved by means of an “ad hoc” bisection method. The results of our experiments on some edge detection benchmark images are provided.