HAVNET: A new neural network architecture for pattern recognition

A new artificial neural network architecture, specifically designed for two-dimensional binary pattern recognition, is introduced. The network employs a unique similarity metric, based on the Hausdorff distance, to determine the degree of match between an input pattern and a learned representation. Use of this metric in the network leads to behaviour that is more consistent with human performance than that generated by similarity metrics currently in use in other artificial neural networks. A detailed description of the architecture, the learning equations, and the recall equations for the network are presented. An extension of the network is also described in which each class of learned objects is represented by multiple two-dimensional aspects. This extension greatly increases the utility of the network for tasks like character recognition and three-dimensional vision. The network is employed on an example pattern recognition task to demonstrate its application, with very good results. Copyright (C) 1996 Elsevier Science Ltd.