fitBinImage: Data pre-processing and binary image analysis
In BayesBD: Bayesian Inference for Image Boundaries

Description Usage Arguments Value References See Also Examples

This function can be used to analyze a binary image in .png or .jpeg, or an image represented as a list object in the format of par2obs.

1 2	fitBinImage(image, gamma.fun = NULL, center = NULL, inimean = NULL, nrun, nburn, J, ordering, mask = NULL, slice, outputAll)

`image`	This may be a string representing the path to a .png or .jpeg file, or a list object in the same format as par2obs output, with intensity, r.obs, theta.obs, and center the required list contents.
`gamma.fun`	This is a function, like triangle2 or ellipse, denoting the true boundary. It is optional and only used when the image input refers to a .png or .jpeg file.
`center`	This is required if the image input refers to a .png or .jpeg file, otherwise it is unused.
`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.

output

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.
`image`	the input image passed to fitBinImage.
`obs`	the processed image data passed to BayesBDbinary.

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

par2obs

## Not run: 
set.seed(12345)
gamma.fun = ellipse(a = 0.35, b = 0.25)
bin.obs = par2obs(m = 100, pi.in = 0.5, pi.out = 0.2,
 design = 'J', center = c(0.5,0.5), gamma.fun)
bin.fit = fitBinImage(image = bin.obs, nrun=1000,
 nburn=1000, J=10, ordering='I', slice = FALSE, outputAll=FALSE)
par(mfrow = c(1,3))
plotBD(bin.fit, 1)
plotBD(bin.fit, 2)
plotBD(bin.fit, 3)

## End(Not run)