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: 0.4-0
Depends: R (≥ 2.14.0)
Imports: Rcpp (≥ 0.10.2)
LinkingTo: Rcpp, RcppArmadillo
Suggests: PottsUtils, coda, knitr
Published: 2017-03-21
Author: Matt Moores [aut, cre], Kerrie Mengersen [aut], Dai Feng [ctb]
Maintainer: Matt Moores <M.T.Moores at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: README NEWS
In views: Bayesian, MedicalImaging
CRAN checks: bayesImageS results


Reference manual: bayesImageS.pdf
Vignettes: Bayesian Methods for Image Segmentation
Package source: bayesImageS_0.4-0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: bayesImageS_0.4-0.tgz
OS X Mavericks binaries: r-oldrel: bayesImageS_0.4-0.tgz
Old sources: bayesImageS archive


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