Neural network representation and implementation of gray scale morphological operators

Sung-Jea Ko, Aldo Morales

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper we introduce a neural network implementation of gray scale operators. In this structure, synaptic weights are represented by a gray scale structuring element. Two learning algorithms are used to train the fuzzy morphological neural networks. The first algorithm utilizes the overall equality index. The second algorithm is based on the averaged least-mean square. It is shown that the LMS based algorithm is simpler and more robust.

Original languageEnglish
Title of host publication1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages105-108
Number of pages4
ISBN (Electronic)0780305930
DOIs
Publication statusPublished - 1992 Jan 1
Externally publishedYes
Event1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992 - San Diego, United States
Duration: 1992 May 101992 May 13

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume1
ISSN (Print)0271-4310

Conference

Conference1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992
CountryUnited States
CitySan Diego
Period92/5/1092/5/13

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ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Ko, S-J., & Morales, A. (1992). Neural network representation and implementation of gray scale morphological operators. In 1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992 (pp. 105-108). [230003] (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 1). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS.1992.230003