Designing morphological composite operators based on fuzzy systems

Aldo Morales, Sung Jea Ko

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

In this paper, we introduce a method to design gray scale composite morphological operators as fuzzy neural networks. In this structure, synaptic weights are represented by a gray scale structuring element. The proposed method is a two-step procedure. First, a suitable neural topology is found through the basis functions of the composite operators. Second, a learning rule based on the average least mean square is applied where each synaptic weight is found through a back propagation algorithm. One dimensional examples will be shown. This scheme can be easily extended to two dimensions.

Original languageEnglish (US)
Pages (from-to)280-290
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume1902
DOIs
StatePublished - May 21 1993
EventNonlinear Image Processing IV 1993 - San Jose, United States
Duration: Jan 31 1993Feb 5 1993

Fingerprint

fuzzy systems
gray scale
Fuzzy systems
Fuzzy Systems
Composite
operators
Least Mean Square
composite materials
Rule Learning
Backpropagation algorithms
Fuzzy neural networks
Back-propagation Algorithm
Fuzzy Neural Network
Composite materials
Operator
learning
Basis Functions
Mathematical operators
Two Dimensions
topology

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

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Designing morphological composite operators based on fuzzy systems. / Morales, Aldo; Ko, Sung Jea.

In: Proceedings of SPIE - The International Society for Optical Engineering, Vol. 1902, 21.05.1993, p. 280-290.

Research output: Contribution to journalConference article

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