A neural architecture applied to the enhancement of noisy binary images without prior knowledge

Frank Y. Shih, Jenlong Moh, Henry Bourne

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

Abstract

The authors present the formulation of an improved neural architecture, a modified adaptive resonance theory (ART), for the enhancement of binary images in the presence of noise. The two-layer ART model developed by G. A. Carpenter and S. Grossberg (1987) is further incorporated into a four-layer network. The operation and performance of ART1 in classifying binary input patterns is first investigated. Based on ART1, a noise filtering architecture is devised whereby preestablished recognition categories are used as region or contour detection exemplars in order to fill in the gaps and smooth the contours of a noisy binary image without any prior knowledge of the image itself.

Original languageEnglish (US)
Title of host publicationProc 2 Int IEEE Conf Tools Artif Intell
PublisherPubl by IEEE
Pages699-705
Number of pages7
ISBN (Print)0818620846
StatePublished - Dec 1 1990
EventProceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence - Herndon, VA, USA
Duration: Nov 6 1990Nov 9 1990

Publication series

NameProc 2 Int IEEE Conf Tools Artif Intell

Other

OtherProceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence
CityHerndon, VA, USA
Period11/6/9011/9/90

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Fingerprint Dive into the research topics of 'A neural architecture applied to the enhancement of noisy binary images without prior knowledge'. Together they form a unique fingerprint.

Cite this