BayesBDbinary: Bayesian boundary estimation for binary images In BayesBD: Bayesian Inference for Image Boundaries

Description

Estimate boundaries in a binary image. This function may be used directly with list objects with the format of par2obs output.

Usage

 1 BayesBDbinary(obs, inimean, nrun, nburn, J, ordering, mask, slice, outputAll)

Arguments

 obs The noisy observation which is a list with the following required elements: intensity: observed intensity at each pixel. theta.obs, r.obs: the location of the pixel at which the intensity is observed, using polar coordinates with respect to a reference point. center: the reference point for polar coords (theta.obs, r.obs). inimean a constant to specify the initial mean functions in the Bayesian estimation. nrun the number of MCMC samples to keep for estimation. nburn the number of initial MCMC samples to discard. J truncation number of the Gaussian process kernel. The number of eigenfunctions is 2J + 1. ordering Indicates which Bernoulli distribution has larger success probability: "I", the Bernoulli distribution inside the boundary; "O", the Bernoulli distribution outside the boundary; "N", no ordering information is available. mask Logical vector (same length as obs\$intensity) to indicate region of interest. Should this data point be included in the analysis? slice boolean where TRUE means that slice sampling will be used to sample Fourier basis function coefficients and FALSE means that Metropolis-Hastings will be used instead. outputAll boolean controlling the amount of output produced, see value below.

Value

If outputAll is FALSE,

 estimate Posterior mean estimate of image boundary at theta values. theta A grid of 200 values on [0,2π] at which to retrun the estimated boundary. lower, upper The lower and upper bounds of a 95\% uniform credible band for the image boundary.

If outputAll is TRUE, same as above, and additionally,

 pi.smp posterior samples of π_1 and π_2. coef.smp posterior samples of Fourier basis function coefficients.

References

Li, M. and Ghosal, S.(2015) "Bayesian Detection of Image Boundaries." arXiv 1508.05847.