Edge detection scheme using Radial Basis Function networks

Abstract

A new edge detection scheme based on Radial Basis Function networks is proposed. The scheme operates on a two-tiered scheme where in the first stage each pixel in the input image is classified according to its potential for being part of an edge. The second stage then combines these pixels in to true edges in the input image. Both stages use radial basis function networks. The scheme illustrates how input space of edge patterns can be used to train the neural network with minimum parameters. Compared with other neural network paradigms, the proposed scheme is simpler in terms network size, computational requirements and provides better results even in low-contrast images.