A multiresolution approach to texture segmentation using neural networks

Abstract

The authors introduce a texture segmentation algorithm that combines texture information at a low resolution level and local edge information at a high resolution to obtain an accurate segmentation. An entropy-based criterion for determining an optimum segmentation scale is proposed. A set of features consistent with the scaling model is described. It is used with a neural network to perform a low-resolution segmentation. Also described is a procedure for resolving the ambiguity in the boundary location resulting from the low-resolution segmentation process. This procedure makes use of a set of morphological filters and edges extracted at a higher resolution. The utility and accuracy of the method are demonstrated with a relatively complex example. The major limitation of the method is that the training time of the neural network classifier increases with the number of nodes in the network.<>