A NEURAL NETWORK APPROACH TO ROBUST SHAPE CLASSIFICATION

A neural network approach for the classification of closed planar shapes is described. The primary foci are the development of an effective representation for planar shapes which may be used in conjunction with neural nets, the selection of a suitable neural network structure, and determining training methods to increase the degree of robustness in classification. A three layer perception using backpropagation is initially trained with contour sequences of noisefree reference shapes and its generalization capability is demonstrated. The network is then gradually retrained with increasingly noisy data to improve the robustness of the classifier. The advantages and improvement in robustness using this extended training scheme are shown and typical classification results are presented. © 1990.