Invariant pattern recognition via higher order preprocessing and backprop

Jon P. Davis; William A. Schmidt

doi:10.1117/12.45018

1 August 1991 Invariant pattern recognition via higher order preprocessing and backprop

Jon P. Davis, William A. Schmidt

Proceedings Volume 1469, Applications of Artificial Neural Networks II; (1991) https://doi.org/10.1117/12.45018
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

Higher-order neural networks are a variation of the standard back-propagation neural network, using geometrically motivated nonlinear combinations of scene pixel values as a feature space. The effects of varying feature size (in number of pixels), scene size, number of features, summation-over-scene versus maximum-over-scene, and number of hidden layers, are examined.

Citation Download Citation

Jon P. Davis and William A. Schmidt "Invariant pattern recognition via higher order preprocessing and backprop", Proc. SPIE 1469, Applications of Artificial Neural Networks II, (1 August 1991); https://doi.org/10.1117/12.45018

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