Granulometric estimation of distorted shapes

Sinan Batman; Edward R. Dougherty

doi:10.1117/12.235820

25 March 1996 Granulometric estimation of distorted shapes

Sinan Batman, Edward R. Dougherty

Proceedings Volume 2662, Nonlinear Image Processing VII; (1996) https://doi.org/10.1117/12.235820
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States

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.

Citation Download Citation

Sinan Batman and Edward R. Dougherty "Granulometric estimation of distorted shapes", Proc. SPIE 2662, Nonlinear Image Processing VII, (25 March 1996); https://doi.org/10.1117/12.235820

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