| SegmentCV {imagerExtra} | R Documentation |
Chan-Vese segmentation
Description
iterative image segmentation with Chan-Vese model
Usage
SegmentCV(im, mu = 0.25, nu = 0, lambda1 = 1, lambda2 = 1,
tol = 1e-04, maxiter = 500, dt = 0.5, initial, returnstep)
Arguments
im |
a grayscale image of class cimg |
mu |
length penalty |
nu |
area penalty |
lambda1 |
fit weight inside the cuve |
lambda2 |
fit weight outside the curve |
tol |
convergence tolerance |
maxiter |
maximum number of iterations |
dt |
time step |
initial |
"interactive" or a grayscale image of class cimg. you can define initial condition as a rectangle shape interactively if initial is "interactive". If initial is a grayscale image of class cimg, pixels whose values are negative will be treated as outside of contour. pixels whose values are non-negative will be treated as inside of contour. checker board condition will be used if initial is not specified. |
returnstep |
a numeric vector that determines which result will be returned. 0 means initial condition, and 1 means the result after 1 iteration. final result will be returned if returnstep is not specified. |
Value
a pixel set or a list of lists of numeric and pixel set
Author(s)
Shota Ochi
References
Pascal Getreuer (2012). Chan-Vese Segmentation. Image Processing On Line 2, 214-224.
Examples
layout(matrix(1:2, 1, 2))
g <- grayscale(dogs)
plot(g, main = "Original")
SegmentCV(g, lambda2 = 15) %>% plot(main = "Binarized")