| bayesImageS {bayesImageS} | R Documentation |
Package bayesImageS
Description
Bayesian methods for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. This package implements 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-MCMC and ABC-SMC).
Author(s)
M. T. Moores and K. Mengersen with additional code contributed by D. Feng
Maintainer: Matt Moores <mmoores@uow.edu.au>
References
Moores, M. T.; Nicholls, G. K.; Pettitt, A. N. & Mengersen, K. (2020) "Scalable Bayesian inference for the inverse temperature of a hidden Potts model" Bayesian Analysis 15(1), 1–17, DOI: doi: 10.1214/18-BA1130
Moores, M. T.; Drovandi, C. C.; Mengersen, K. & Robert, C. P. (2015) "Pre-processing for approximate Bayesian computation in image analysis" Statistics & Computing 25(1), 23–33, DOI: doi: 10.1007/s11222-014-9525-6
Moores, M. T.; Hargrave, C. E.; Deegan, T.; Poulsen, M.; Harden, F. & Mengersen, K. (2015) "An external field prior for the hidden Potts model, with application to cone-beam computed tomography" Computational Statistics & Data Analysis 86, 27–41, DOI: doi: 10.1016/j.csda.2014.12.001
Feng, D. (2008) "Bayesian Hidden Markov Normal Mixture Models with Application to MRI Tissue Classification" Ph. D. Dissertation, The University of Iowa
See Also
vignette(package="bayesImageS")