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Proceedings Paper

Methods for numerical integration of high-dimensional posterior densities with application to statistical image models
Author(s): Steven M. LaValle; Kenneth J. Moroney; Seth A. Hutchinson
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Paper Abstract

Numerical computation with Bayesian posterior densities has recently received much attention both in the statistics and computer vision communities. This paper explores the computation of marginal distributions for models that have been widely considered in computer vision. These computations can be used to assess homogeneity for segmentation, or can be used for model selection. In particular, we discuss computation methods that apply to a Markov random field formation, implicit polynomial surface models, and parametric polynomial surface models, and present some demonstrative experiments.

Paper Details

Date Published: 29 October 1993
PDF: 12 pages
Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162047
Show Author Affiliations
Steven M. LaValle, Univ. of Illinois/Urbana-Champaign (United States)
Kenneth J. Moroney, Univ. of Illinois/Urbana-Champaign (United States)
Seth A. Hutchinson, Univ. of Illinois/Urbana-Champaign (United States)

Published in SPIE Proceedings Vol. 2032:
Neural and Stochastic Methods in Image and Signal Processing II
Su-Shing Chen, Editor(s)

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