This is documentation for Mathematica 8, which was
based on an earlier version of the Wolfram Language.

# ChanVeseBinarize

 ChanVeseBinarize[image]finds a two-level segmentation of image by computing optimal contours around regions of consistent intensity in image. ChanVeseBinarizeuses the foreground pixels of marker as the initial contours.
• ChanVeseBinarize implements an iterative active contour method to achieve a two-level segmentation of an image.
• In ChanVeseBinarize, marker can be given either as an image, a graphics object, or a list of points in the standard image coordinate system, where x runs from 0 to width and y runs from 0 to height, and position corresponds to the bottom-left corner of the image.
• ChanVeseBinarize uses the Euclidean distance between channel vectors to determine the similarity between pixels inside and outside of the contour.
• ChanVeseBinarize iteratively minimizes a functional that is a weighted sum of the contour length, the enclosed area, and the deviation between the image and the two-level segmentation.
• The weights can be specified with the following options:
 "AreaPenalty" 0 penalty associated with the area enclosed by the contour "LengthPenalty" 0.03 contour length penalty "LevelPenalty" {1.0,1.0} penalties for the total pixel deviations in the two segments "TargetColor" Automatic target foreground color
• With the setting , both foreground and background colors can be specified.
• The maximum number of iteration steps is given by the MaxIterations option with default setting 100.
• The Chan-Vese segmentation of an image domain into the two segments and with contour minimizes the following functional of image :
• The Chan-Vese functional is parametrized by the length penalty , the area penalty , and level penalties and .
• The Chan-Vese algorithm partitions image such that the first segment will differ as little as possible from constant and the second segment will deviate as little as possible from constant . If constants and are not specified, one assumes c1=Mean[f] in , and c2=Mean[f] in .
• The contour between the two resulting segments and will exhibit a short length for , and for the area of will tend to be small or for tend to be large.
• The weighting coefficients , , , and are accessible through the following options.
Binary segmentation of a satellite image:
Binary segmentation of a satellite image:
 Out[1]=
 Scope   (2)
Binary segmentation of a color image:
Use an edge marker to improve the segmentation:
 Options   (5)
Control the area of the segmented region:
Increase the smoothness of the segmented region:
Increase the length penalty when segmenting noisy images:
Specify the foreground color:
Specify both foreground and background target colors:
 Applications   (2)
Chroma key compositing:
Find the precise contour of a coastline in a satellite image:
New in 8