bayesImageS: Bayesian Methods for Image Segmentation using a Potts Model

Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. This package implements Bayesian image analysis using the hidden Potts model with external field prior. Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, and approximate Bayesian computation (ABC).

Version: 0.3-3
Depends: R (≥ 2.14.0)
Imports: Rcpp (≥ 0.10.2)
LinkingTo: Rcpp, RcppArmadillo
Suggests: PottsUtils, coda, knitr
Published: 2016-11-03
Author: Matt Moores [aut, cre], Kerrie Mengersen [aut]
Maintainer: Matt Moores <M.T.Moores at warwick.ac.uk>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README
In views: Bayesian
CRAN checks: bayesImageS results

Downloads:

Reference manual: bayesImageS.pdf
Vignettes: Scalable Bayesian inference for the inverse temperature of a hidden Potts model
Package source: bayesImageS_0.3-3.tar.gz
Windows binaries: r-devel: bayesImageS_0.3-3.zip, r-release: bayesImageS_0.3-3.zip, r-oldrel: bayesImageS_0.3-3.zip
OS X Mavericks binaries: r-release: bayesImageS_0.3-3.tgz, r-oldrel: bayesImageS_0.3-3.tgz

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