Share Email Print
cover

Proceedings Paper

Estimation of random model parameters via linear systems with granulometric inputs
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

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)

© SPIE. Terms of Use
Back to Top