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

Author

Aizenberg, Igor ; Aizenberg, N. ; Bregin, Taras ; Butakov, Constantine ; Farberov, Elya

Author_Institution

Neural Network Technol. Ltd., Bnei-Brak, Israel

Volume

2

fYear

2000

fDate

2000

Firstpage

557

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

Keywords

Boolean functions; cellular neural nets; convergence; image processing; learning (artificial intelligence); multivalued logic; Boolean function; arbitrary mapping; cellular neural networks; complex-valued weights; image analysis; image processing; multi-valued neurons; neural processing elements; neuron functionality; partially-defined multiple-valued function; rapidly-converging learning algorithms; threshold function; universal binary neurons; Boolean functions; Cellular neural networks; Filtering algorithms; Filters; Image color analysis; Image processing; Image recognition; Neural networks; Neurons; Pixel;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop

Conference_Location

Sydney, NSW

ISSN

1089-3555

Print_ISBN

0-7803-6278-0

Type

conf

DOI

10.1109/NNSP.2000.890134

Filename

890134