A texture classifier based on neural network principles

A microprocessor-based system for texture classification and recognition is described. It is able to classify images containing stochastic textures. The maximum number of classes is currently 64. The learning and recognition are based on neural network principles. The topological feature map, a texture map, is created by self-organization. The recognition is based on learning vector quantization. A typical recognition rate for stochastic textures is 80% to 95%. The recognition rate depends on the number of classes and the quality of reference samples. New classes are easily taught by examples. The comparisons between stochastic textures is easy because of the texture map