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Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. The R package bayesImageS 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.

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Version

Install

install.packages('bayesImageS')

Monthly Downloads

788

Version

0.3-3

License

GPL (>= 2)

Maintainer

Matt Moores

Last Published

November 3rd, 2016

Functions in bayesImageS (0.3-3)

gibbsGMM

Fit a mixture of Gaussians to the observed data.
bayesImageS

Package bayesImageS
swNoData

Simulate pixel labels using the Swendsen-Wang algorithm.
initSedki

Initialize the ABC algorithm using the method of Sedki et al. (2013)
smcPotts

Fit the hidden Potts model using approximate Bayesian computation with sequential Monte Carlo (ABC-SMC).
mcmcPottsNoData

Simulate pixel labels using chequerboard Gibbs sampling.
mcmcPotts

Fit the hidden Potts model using a Markov chain Monte Carlo algorithm.
testResample

Test the residual resampling algorithm.
gibbsNorm

Fit a univariate normal (Gaussian) distribution to the observed data.
sufficientStat

Calculate the sufficient statistic of the Potts model for the given labels.
exactPotts

Calculate the distribution of the Potts model using a brute force algorithm.