Bayesian Methods for Image Segmentation using a Potts Model
Package bayesImageS
Calculate the distribution of the Potts model using a brute force algo...
Get Blocks of a Graph
Get Edges of a Graph
Get Neighbours of All Vertices of a Graph
Fit a mixture of Gaussians to the observed data.
Fit a univariate normal (Gaussian) distribution to the observed data.
Fit a hidden Potts model to the observed data, using a fixed value of ...
Initialize the ABC algorithm using the method of Sedki et al. (2013)
Fit the hidden Potts model using a Markov chain Monte Carlo algorithm.
Simulate pixel labels using chequerboard Gibbs sampling.
Fit the hidden Potts model using approximate Bayesian computation with...
Calculate the sufficient statistic of the Potts model for the given la...
Simulate pixel labels using the Swendsen-Wang algorithm.
Test the residual resampling algorithm.
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.
Useful links