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Proceedings Paper

Estimation of random model parameters via linear systems with granulometric inputs
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Paper Abstract

Morphological granulometries have been used to successfully discriminate textures in the context of classical feature-based classification. The features are typically the granulometric moments resulting from the pattern spectrum of the random image. This paper takes a different approach and uses the granulometric moments as inputs to a linear system that has been derived by classical optimization techniques for linear filters. The output of the system in a set of estimators that estimate the parameters of the model governing the distribution of the random set. These model parameters are assumed to be random variables possessing a prior distribution, so that the linear filter estimates these random variables based on granulometric moments. The methodology is applied to estimating the primary grain and intensity of a random Boolean model.

Paper Details

Date Published: 4 October 2000
PDF: 5 pages
Proc. SPIE 4121, Mathematical Modeling, Estimation, and Imaging, (4 October 2000); doi: 10.1117/12.402446
Show Author Affiliations
Yoganand Balagurunathan, Texas A&M Univ. (United States)
Edward R. Dougherty, Texas A&M Univ. (United States)

Published in SPIE Proceedings Vol. 4121:
Mathematical Modeling, Estimation, and Imaging
David C. Wilson; Hemant D. Tagare; Fred L. Bookstein; Francoise J. Preteux; Edward R. Dougherty, Editor(s)

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