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Proceedings Paper

Granulometric estimation of shape parameters
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Paper Abstract

Assuming a random shape to be governed by a random parameter vector, a basic problem is to estimate the value of the parameter vector given some set of random features based on the random shape. The present paper considers this Bayesian estimation problem as one involving conditional densities of the random parameters conditioned by granulometric moments generated by linear granulometries. The conditional densities are interpreted as generalized functions and from these the optimal conditional-expectation estimates of the parameters given the granulometric moments are found.

Paper Details

Date Published: 11 August 1995
PDF: 12 pages
Proc. SPIE 2568, Neural, Morphological, and Stochastic Methods in Image and Signal Processing, (11 August 1995); doi: 10.1117/12.216346
Show Author Affiliations
Sinan Batman, Rochester Institute of Technology (United States)
Edward R. Dougherty, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 2568:
Neural, Morphological, and Stochastic Methods in Image and Signal Processing
Edward R. Dougherty; Francoise J. Preteux; Sylvia S. Shen, Editor(s)

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