sufficientStat

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

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
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.

Matt Moores

bayesImageS

Bayesian Methods for Image Segmentation using a Potts Model

Dai Feng

Kerrie Mengersen

sufficientStat function

<dl><dt>labels</dt>
<dd>A matrix of pixel labels.</dd>
<dt>neighbors</dt>
<dd>A matrix of all neighbors in the lattice, one row per pixel.</dd>
<dt>blocks</dt>
<dd>A list of pixel indices, dividing the lattice into independent blocks.</dd>
<dt>k</dt>
<dd>The number of unique labels.</dd></dl>

Arguments

Calculate the sufficient statistic of the Potts model for the given labels. — sufficientStat

<dl>

<dt>labels</dt>
<dd>A matrix of pixel labels.</dd>


<dt>neighbors</dt>
<dd>A matrix of all neighbors in the lattice, one row per pixel.</dd>


<dt>blocks</dt>
<dd>A list of pixel indices, dividing the lattice into independent blocks.</dd>


<dt>k</dt>
<dd>The number of unique labels.</dd>

</dl>

sufficientStat: Calculate the sufficient statistic of the Potts model for the given labels.

Description

Usage

Value

Arguments