Abstract
This paper describes a neural network architecture that has been developed to perform deformation tolerant object recognition from grey-scale images. It uses a form of deformable template matching, generating new templates in a self-organising manner. The results demonstrate the network‘s ability to build classes when no suitable classes are available. The amount of deformation allowed within a class can be controlled to allow the network to be applied to a wide range of applications. Results are presented for a set of generated images which allow the effects of the selection of the major network parameters to be shown.
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