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Proceedings Paper

Theory of morphological neural networks
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Paper Abstract

The theory of classical artificial neural networks has been used to solve pattern recognition problems in image processing that is different from traditional pattern recognition approaches. In standard neural network theory, the first step in performing a neural network calculation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for non-linearity of the network. This paper presents the fundamental theory for a morphological neural network which, instead of multiplication and summation, uses the non-linear operation of addition and maximum. Several basic applications which are distinctly different from pattern recognition techniques are given, including a net which performs a sieving algorithm.

Paper Details

Date Published: 1 July 1990
PDF: 11 pages
Proc. SPIE 1215, Digital Optical Computing II, (1 July 1990); doi: 10.1117/12.18085
Show Author Affiliations
Jennifer L. Davidson, Iowa State Univ. (United States)
Gerhard X. Ritter, Univ. of Florida (United States)

Published in SPIE Proceedings Vol. 1215:
Digital Optical Computing II
Raymond Arrathoon, Editor(s)

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