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Proceedings Paper

Simultaneous object estimation and image reconstruction in a Bayesian setting
Author(s): Kenneth M. Hanson
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Paper Abstract

Suppose that it is desired to estimate certain parameters associated with a model of an object that is contained within a larger scene and that only indirect measurements of the scene are available. The optimal solution is provided by a Bayesian approach, which is founded on the posterior probability density distribution. The complete Bayesian procedure requires an integration of the posterior probability over all possible values of the image exterior to the local region being analyzed. In the presented work, the full treatment is approximated by simultaneously estimating the reconstruction outside the local region and the parameters of the model within the local region that maximize the posterior probability. A Monte Carlo procedure is employed to evaluate the usefulness of the technique in a signal-known-exactly detection task in a noisy four-view tomographic reconstruction situation.

Paper Details

Date Published: 1 June 1991
PDF: 12 pages
Proc. SPIE 1452, Image Processing Algorithms and Techniques II, (1 June 1991); doi: 10.1117/12.45382
Show Author Affiliations
Kenneth M. Hanson, Los Alamos National Lab. (United States)

Published in SPIE Proceedings Vol. 1452:
Image Processing Algorithms and Techniques II
Mehmet Reha Civanlar; Sanjit K. Mitra; Robert J. Moorhead II, Editor(s)

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