NDLI: Image processing using cellular neural networks based on multi-valued and universal binary neurons

Content Provider	IEEE Xplore Digital Library
Author	Aizenberg, I. Aizenberg, N. Bregin, T. Butakov, C. Farberov, E.
Copyright Year	2000
Description	Author affiliation: Neural Network Technol. Ltd., Bnei-Brak, Israel (Aizenberg, I.)
Abstract	Multi-valued neurons (MVNs) and universal binary neurons (UBNs) are neural processing elements with complex-valued weights and high functionality. It is possible to implement an arbitrary mapping described by a partially-defined multiple-valued function on a single MVN, and an arbitrary mapping described by a partially-defined or fully-defined Boolean function (which does not have to be a threshold function) on a single UBN. Rapidly-converging learning algorithms exist for both types of neurons. Such features of MVNs and UBNs may be used to solve different kinds of problems. One of the most successful applications of MVNs and UBNs is their use as basic neurons in cellular neural networks (CNNs) to solve image processing and image analysis problems.
Starting Page	557
Ending Page	566
File Size	551367
Page Count	10
File Format	PDF
ISBN	0780362780
ISSN	10893555
DOI	10.1109/NNSP.2000.890134
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-12-11
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image processing Cellular neural networks Neurons Neural networks Boolean functions Filters Image recognition Pixel Filtering algorithms Image color analysis
Content Type	Text
Resource Type	Article

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