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

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3 Author(s)
Shih, F.Y. ; Dept. of Comput. & Inf. Sci., New Jersey Inst. of Technol., Newark, NJ, USA ; Moh, J. ; Bourne, H.

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

Published in:

Tools for Artificial Intelligence, 1990.,Proceedings of the 2nd International IEEE Conference on

Date of Conference:

6-9 Nov 1990