Invariant pattern recognition via higher order preprocessing and backprop
Jon P. Davis, William A. Schmidt · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1991
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.