29 October 1993 Methods for numerical integration of high-dimensional posterior densities with application to statistical image models
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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.
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Steven M. LaValle, Steven M. LaValle, Kenneth J. Moroney, Kenneth J. Moroney, Seth A. Hutchinson, Seth A. Hutchinson, } "Methods for numerical integration of high-dimensional posterior densities with application to statistical image models", Proc. SPIE 2032, Neural and Stochastic Methods in Image and Signal Processing II, (29 October 1993); doi: 10.1117/12.162047; https://doi.org/10.1117/12.162047
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