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Deformation invariant visual object recognition: Experiments with a self-organising neural architecture

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Abstract

A self-organising neural network architecture for grey-scale visual object rcognition is presented. The network is composed of three processing layers with an architecture designed to give deformation tolerance. The processing layers involve feature extraction, sub-pattern detection and classification. Training is generally performed on-line in an unsupervised manner, classes being created when objects are presented that cannot be classified. The results given show the effect of the two discrimination parameters when the network is applied to two very different sets of images, namely hand written numerals and hand gestures images. The sensitivity of the network to the parameters that govern the size of detectable patterns and the areas over which they are detected is also tested. The robustness of the network to the order of image presentation is also demonstrated. The results show that parameter choice is not critical and heuristically chosen parameters provide near optimum performance.

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Authors and Affiliations

  1. University of Wales, Bangor, SEECS, Dean Street Bangor, LL57 1UT, Gwynedd, UK

    D. S. Banarse & A. W. G. Duller

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  1. D. S. Banarse
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  2. A. W. G. Duller
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Correspondence to A. W. G. Duller.

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Banarse, D.S., Duller, A.W.G. Deformation invariant visual object recognition: Experiments with a self-organising neural architecture. Neural Comput & Applic 6, 79–90 (1997). https://doi.org/10.1007/BF01414005

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