Neural network representation and implementation of gray scale morphological operators

Author

Ko, Sung-Jea ; Morales, Aldo

Author_Institution

Dept. of Electr. & Comput. Eng., Michigan Univ., Dearborn, MI, USA

Volume

1

fYear

1992

fDate

10-13 May 1992

Firstpage

105

Abstract

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

Keywords

filtering and prediction theory; fuzzy set theory; image recognition; learning (artificial intelligence); least squares approximations; neural nets; averaged least-mean square; fuzzy set theory; gray scale morphological operators; gray-scale structuring element; learning algorithms; neural network implementation; overall equality index; synaptic weights; Artificial neural networks; Computer networks; Concurrent computing; Educational institutions; Fuzzy neural networks; Fuzzy sets; Least squares approximation; Morphology; Neural networks; Shape measurement;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1992. ISCAS '92. Proceedings., 1992 IEEE International Symposium on

Conference_Location

San Diego, CA

Print_ISBN

0-7803-0593-0

Type

conf

DOI

10.1109/ISCAS.1992.230003

Filename

230003