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 Scopus citations

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 (US)
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 - Jan 1 1992
Event1992 IEEE International Symposium on Circuits and Systems, ISCAS 1992 - San Diego, United States
Duration: May 10 1992May 13 1992

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
Period5/10/925/13/92

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All Science Journal Classification (ASJC) codes

  • 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