SegmentCV: Chan-Vese segmentation

In imagerExtra: Extra Image Processing Library Based on 'imager'

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")

Example output

Loading required package: imager
Loading required package: magrittr

Attaching package: ‘imager’

The following object is masked from ‘package:magrittr’:

    add

The following objects are masked from ‘package:stats’:

    convolve, spectrum

The following object is masked from ‘package:graphics’:

    frame

The following object is masked from ‘package:base’:

    save.image

The computation stopped because the number of iteration reached maxiter.

imagerExtra documentation built on May 2, 2019, 1:44 p.m.