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Proceedings Paper

Adaptive resonance theory and self-organizing morphological kernels
Author(s): John P. Sharpe; Nilgun Sungar; Kristina M. Johnson
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Paper Abstract

In this paper we describe our recent work developing automated methods for generation of kernels or structuring elements for use in the hit-or-miss transform. We show how a neural network algorithm (Fuzzy Adaptive Resonance Theory) generates hit and miss structuring elements that can be used with a fuzzy morphology to detect a class of objects and we illustrate with computer simulations.

Paper Details

Date Published: 30 June 1994
PDF: 9 pages
Proc. SPIE 2300, Image Algebra and Morphological Image Processing V, (30 June 1994); doi: 10.1117/12.179210
Show Author Affiliations
John P. Sharpe, Univ. of Colorado/Boulder (United States)
Nilgun Sungar, California Polytechnic State Univ. (United States)
Kristina M. Johnson, Univ. of Colorado/Boulder (United States)

Published in SPIE Proceedings Vol. 2300:
Image Algebra and Morphological Image Processing V
Edward R. Dougherty; Paul D. Gader; Michel Schmitt, Editor(s)

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