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Proceedings Paper

Granulometric estimation of distorted shapes
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Paper Abstract

Assuming a random shape to be governed by a random generator and noise parameter vector, it is essential to optimally estimate the state of the generators given some set of extracted features based on the random shape. If the features used are analytically tied to shape and distortion parameters, the conditional densities involved in this Bayesian estimation problem are of a generalized nature and exist only on the manifold dictated by the particular probe. These generalized densities can be used in a conventional way to calculate the conditional- expectation estimates of the parameters. They may also be used to minimize the mean-square error on the manifold itself, thereby yielding an estimate of shape parameters consistent with the geometrical prior information provided by the observed feature set.

Paper Details

Date Published: 25 March 1996
PDF: 10 pages
Proc. SPIE 2662, Nonlinear Image Processing VII, (25 March 1996); doi: 10.1117/12.235820
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. 2662:
Nonlinear Image Processing VII
Edward R. Dougherty; Jaakko T. Astola; Harold G. Longbotham, Editor(s)

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