A fuzzy hierarchical neural network for image analysis

An highly parallel neural structure suitable for image analysis is proposed. Each neuron is connected to a windowed area of neurons in the previous layer. The operations involved follow a method for representing and manipulating fuzzy sets, called composite calculus. The local features extracted by the consecutive layers are combined in the output layer in order to separate the output neurons in groups in a self-organizing manner. In this paper we focus our attention on the application of the proposed model to the edge detection based segmentation, reporting results on real images and comparing our results with those obtained by the classical Prewitt-Canny edge operators