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Journal of Electronic Imaging

Automatic programming of binary morphological machines by design of statistically optimal operators in the context of computational learning theory
Author(s): Junior Barrera; Edward R. Dougherty; Nina Sumiko Tomita
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Paper Abstract

Representation of set operators by artificial neural networks and design of such operators by inference of network parameters is a popular technique in binary image analysis. We propose an alternative to this technique: automatic programming of morphological machines (MMachs) by the design of statistically optimal operators. We propose a formulation of the procedure for designing set operators that extends the one stated by Dougherty for binary image restoration, show the relation of this new formulation with the one stated by Haussler for learning Boolean concepts in the context of machine learning theory (which usually is applied to neural networks), present a new learning algorithm for Boolean concepts represented as MMach programs, and give some application examples in binary image analysis.

Paper Details

Date Published: 1 January 1997
PDF: 14 pages
J. Electron. Imaging. 6(1) doi: 10.1117/12.260010
Published in: Journal of Electronic Imaging Volume 6, Issue 1
Show Author Affiliations
Junior Barrera, Univ. de Sao Paulo (Brazil)
Edward R. Dougherty, Texas A&M Univ. (United States)
Nina Sumiko Tomita, Univ. de Sao Paulo (Brazil)


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