fitContImage: Data pre-processing and continuous 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 continuous image in .png or .jpeg format, or an image represented as a list object in the format of parnormobs.

1 2	fitContImage(image, gamma.fun = NULL, center = NULL, inimean = NULL, nrun, nburn, J, ordering_mu, ordering_sigma, 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_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.

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,

`musig.smp`	posterior samples of μ_1, μ_2, σ_1, and σ_2.
`coef.smp`	posterior samples of Fourier basis function coefficients.
`image`	the input image passed to fitContImage.
`obs`	the processed image data passed to BayesBDnormal.

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

parnormobs

## Not run: 
set.seed(12345)
gamma.fun = ellipse(a = 0.35, b = 0.25)
norm.obs = parnormobs(m = 100, mu.in = 4, mu.out = 1,
 sd.in = 1.5, sd.out = 1, design = 'J',
 center = c(0.5,0.5), gamma.fun)
norm.samp = fitContImage(image = norm.obs, nrun = 1000, nburn = 0,
 J = 10,ordering_mu = "I",ordering_sigma = "I", slice = FALSE, outputAll = FALSE)
par(mfrow = c(1,3))
plotBD(norm.samp, 1)
plotBD(norm.samp, 2)
plotBD(norm.samp, 3)

## End(Not run)