Designing morphological composite operators based on fuzzy systems

Aldo Morales; Sung Jea Ko

doi:10.1117/12.144764

Designing morphological composite operators based on fuzzy systems

Aldo Morales, Sung Jea Ko

School of Electrical Engineering

Research output: Contribution to journal › Conference article › peer-review

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 language	English
Pages (from-to)	280-290
Number of pages	11
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	1902
DOIs	https://doi.org/10.1117/12.144764
Publication status	Published - 1993 May 21
Event	Nonlinear Image Processing IV 1993 - San Jose, United States Duration: 1993 Jan 31 → 1993 Feb 5

ASJC Scopus subject areas

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

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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.",

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AB - 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.

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