Granulometric estimation of distorted shapes Conference Paper uri icon

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.

name of conference

  • Nonlinear Image Processing VII

published proceedings

  • Proceedings of SPIE

author list (cited authors)

  • Batman, S., & Dougherty, E. R.

citation count

  • 0

complete list of authors

  • Batman, Sinan||Dougherty, Edward R

editor list (cited editors)

  • Dougherty, E. R., Astola, J. T., & Longbotham, H. G.

publication date

  • March 1996