Methods for Numerical integration of high-dimensional posterior densities with application to statistical image models

Steven M. Lavalle, Kenneth J. Moroney, Seth A. Hutchinson

Research output: Contribution to journalConference article

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 formulation, implicit polynomial surface models, and parametric polynomial surface models, and present some demonstrative experiments.

Original languageEnglish (US)
Pages (from-to)292-303
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume2032
DOIs
StatePublished - Oct 29 1993
EventNeural and Stochastic Methods in Image and Signal Processing II 1993 - San Diego, United States
Duration: Jul 11 1993Jul 16 1993

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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