Abstract
A method for improving the generalization capability for optical
pattern recognition by use of a Gaussian-synapse neuron model is
discussed. By the dispersive effect of the Gaussian function the
input images are blurred and then fed into a multilayer neural network
for learning and recognition. The effectiveness of this method is
demonstrated in two-dimensional shift- and scale-invariant optical
pattern recognition.
© 2000 Optical Society of America
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