CRAN/E | bayesImageS

bayesImageS

Bayesian Methods for Image Segmentation using a Potts Model

Installation

About

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 of Moores et al. (2015) doi:10.1016/j.csda.2014.12.001. Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC), and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to doi:10.1007/978-3-030-42553-1_6 for an overview and also to doi:10.1007/s11222-014-9525-6 and doi:10.1214/18-BA1130 for further details of specific algorithms.

Citation bayesImageS citation info
bitbucket.org/Azeari/bayesimages
mooresm.github.io/bayesImageS/
Bug report File report

Key Metrics

Version 0.6-1
R ≥ 3.5.0
Published 2021-04-11 1104 days ago
Needs compilation? yes
License GPL-2
License GPL-3
License File
CRAN checks bayesImageS results

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Maintainer

Maintainer

Matt Moores

mmoores@gmail.com

Authors

Matt Moores

aut / cre

Dai Feng

ctb

Kerrie Mengersen

aut / ths

Material

README
NEWS
Reference manual
Package source

In Views

Bayesian
MedicalImaging

Vignettes

Bayesian Methods for Image Segmentation
mcmcPotts
mcmcPottsNoData
swNoData

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

bayesImageS archive

Depends

R ≥ 3.5.0

Imports

Rcpp ≥ 0.10.6

Suggests

mcmcse
coda
PottsUtils
rstan
knitr
rmarkdown
lattice

LinkingTo

Rcpp
RcppArmadillo