ABSTRACT
Artificial neural networks (ANNs) and cellular automata have been successfully employed to solve a variety of computer vision problems [ l , 21. One goal of this chapter is to demonstrate that image algebra provides an ideal environment for precisely expressing current popular neural network models and their computations. Artificial neural networks (ANNs) are systems of dense interconnected simple processing elements. There exist many different types of ANNs designed to address a wide range of problems in the primary areas of pattern recognition, signal processing, and robotics. The function of different types of ANNs is determined primarily by the processing elements' pattern of connectivity, the strengths (weights) of the connecting links, the processing elements' characteristics, and training or learning rules. These rules specify an initial set of weights and indicate how weights should be adapted during use to improve performance.
