Neural Architectures for Mathematical Morphology and Fuzzy Morphology

Jenlong Moh, Frank Shih

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

Abstract

In this paper, we first propose a general neural structure along with a set of training algorithms for basic binary morphological operations. The proposed algorithms can train the neurons in only a single iteration and use simple arithmetic computation in the testing phase. This feature overcomes most of other models with multiple training iterations in speeding up neuron operations. Besides, the concept of fuzzy morphology is incorporated into the design of neural architecture. This is important in interpreting binary images with noise and gray-scale images in which uncertainty are embeded. The proof of proposed algorithms are provided and examples of different operations are illustrated.

Original languageEnglish (US)
Title of host publicationJoint Conference on Information Sciences - Proceedings, Abstracts and Summaries, JCIS 94
Pages308-311
Number of pages4
StatePublished - Dec 1 1994
EventJoint Conference on Information Sciences - Proceedings, Abstracts and Summaries '94 - Pinehurst, NC, United States
Duration: Nov 1 1994Nov 1 1994

Other

OtherJoint Conference on Information Sciences - Proceedings, Abstracts and Summaries '94
CountryUnited States
CityPinehurst, NC
Period11/1/9411/1/94

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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