fitContImage: Data pre-processing and continuous image analysis In BayesBD: Bayesian Inference for Image Boundaries

Description

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.

Usage

 1 2 fitContImage(image, gamma.fun = NULL, center = NULL, inimean = NULL, nrun, nburn, J, ordering_mu, ordering_sigma, mask = NULL, slice, outputAll)

Arguments

 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.

Value

 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.

References

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