BayesBDnormal: Bayesian boundary estimation for continuous intensity images

In BayesBD: Bayesian Inference for Image Boundaries

Description

Estimate boundaries in a continuous intensity image.

Usage

BayesBDnormal(obs, inimean, nrun, nburn, J, ordering_mu, 
ordering_sigma, 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_mu	Indicates which Gaussian distribution has larger mean intensity: "I", the Gaussian distribution inside the boundary; "O", the Gaussian distribution outside the boundary; "N", no ordering information is available.
ordering_sigma	Indicates which Gaussian distribution has larger intensity variance: "I", the Gaussian distribution inside the boundary; "O", the Gaussian 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,

musig.smp	posterior samples of μ_1, μ_2, σ_1, and σ_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.

See Also

fitContImage

BayesBD documentation built on May 1, 2019, 10:17 p.m.