parnormobs: Simulate Gaussian intensity images

In BayesBD: Bayesian Inference for Image Boundaries

Description

The generated data Y ~ N(μ, σ) is the image intensity, where the params (μ, σ) are determined by whether the location X, given by polar coords (r,θ), is inside the boundary or not.

Usage

parnormobs(m, mu.in, mu.out, sd.in, sd.out, design, center, gamma.fun)

Arguments

m	m * m observations will be generated over the unit square centered at (0,0).
mu.in	The mean intensity for pixels inside the image boundary.
mu.out	The mean intensity for pixels outside the image boundary.
sd.in	The standard deviation of intensity for pixels inside the image boundary.
sd.out	The standard deviation of intensity for pixels outside the image boundary.
design	Taking values: 'D' for deterministic (equally-spaced grid) design, 'U' for completely uniformly random, or 'J' for jitteredly random design.
center	a two-dimensional vector of Euclidean coordinates (x,y) of the reference point.
gamma.fun	The function to generate boundaries, see ellipse or triangle2.

Examples

set.seed(2015)
# use ellipse boundary
gamma.fun = ellipse(a = 0.35, b = 0.25)
obs = parnormobs(m = 100, mu.in = 1, sd.in = 1, mu.out = 0, sd.out = 1, design = 'J',
 center = c(0.5,0.5), gamma.fun)
plotBD(obs)

Example output

[1] "Input image is not a compatible image file nor a compatible list object."

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