Bayesian Methods for Image Segmentation using a Potts Model [R package bayesImageS version 0.7-0]

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 of Moores et al. (2015) <doi:10.1016/j.csda.201412001>. 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 Moores, Pettitt & Mengersen (2020) <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.

Version: 0.7-0

Depends: R (≥ 3.5.0)

Imports: Rcpp (≥ 1.0.12)

LinkingTo: Rcpp, RcppArmadillo

Suggests: mcmcse, coda, PottsUtils, rstan, knitr, rmarkdown, lattice

Published: 2025年10月10日

DOI: 10.32614/CRAN.package.bayesImageS

Author: Matt Moores ORCID iD [aut, cre, cph], Dai Feng [ctb], Kerrie Mengersen ORCID iD [aut, ths]

Maintainer: Matt Moores <mmoores at gmail.com>

BugReports: https://bitbucket.org/Azeari/bayesimages/jira

License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]

URL: https://bitbucket.org/Azeari/bayesimages, https://mooresm.github.io/bayesImageS/