Abstract
This paper proposes a neural network model, which performs deterministic, parallel pattern matching. The pattern is recognized by matching an input pattern containing noise and a template constructed through learning. In this paper, it is assumed that no feature detection has already been accomplished. We consider a circuit structure which merely compares a template with the noisy feature distribution pattern that appears at the output of the feature detector. This pattern matching method is based on our previous work using neural networks to determine the correspondence between stereo images. The input from the left eye, for example, corresponds to the feature distribution pattern, and the input from the right eye corresponds to the template. The difference in the positions of features in the template and the input pattern corresponds to the binocular parallax. The proposed model is composed of a pattern matching layer, a minimum distance detection layer (which selects the template close to the input feature distribution pattern, and feeds it back to the pattern matching layer for pattern matching) and the recognition layer which classifies the matched template. The results of our computer simulation indicated that perfect matching was achieved for inputs having feature distribution patterns corresponding to those used during learning. Since sequences of labels or character strings can be considered to be feature distribution patterns, the proposed network can be applied to a wide range of problems related to pattern matching.