1 July 1990 Theory of morphological neural networks
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Proceedings Volume 1215, Digital Optical Computing II; (1990) https://doi.org/10.1117/12.18085
Event: OE/LASE '90, 1990, Los Angeles, CA, United States
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.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jennifer L. Davidson, Jennifer L. Davidson, Gerhard X. Ritter, Gerhard X. Ritter, } "Theory of morphological neural networks", Proc. SPIE 1215, Digital Optical Computing II, (1 July 1990); doi: 10.1117/12.18085; https://doi.org/10.1117/12.18085
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