GRABCUT Segment image into foreground and background using iterative graph-based segmentation.
BW = GRABCUT(A,L,ROI) segments the image A into
foreground and background regions using GrabCut with the label
matrix L specifying the subregions of the image. ROI is a logical mask
designating the initial region of interest.
BW = GRABCUT(A,L,ROI,FOREMASK,BACKMASK) segments the image A into
foreground and background regions using GrabCut with the label
matrix L specifying the subregions of the image. FOREMASK and BACKMASK
are masks designating pixels in the image as foreground and background,
respectively.
BW = GRABCUT(A,L,ROI,FOREIND,BACKIND) segments the image A into
foreground and background regions using GrabCut with the label
matrix L specifying the subregions of the image. FOREIND and BACKIND
specify the linear indices of the pixels in the image marked as
foreground and background, respectively.
BW = GRABCUT(V,_____) segments the volume V into foreground and
background regions.
BW = GRABCUT(_____,NAME,VALUE) segments the image using name-value
pairs to control aspects of the segmentation.
Parameters include:
'Connectivity' - Scalar value representing the
connectivity of connected components. For
2D images, value must be either 4 or
8 (default). For 3D images, value must be
6, 18, or 26 (default).
'MaximumIterations' - Positive scalar integer value that
determines the maximum number of
iterations performed by the algorithm.
The algorithm could converge to a
solution before reaching the maximum
number of iterations. The default value
is 5.
Class Support
-------------
The input image A is an array of one of the following classes: uint8,
uint16, int16 (grayscale only), single, or double. It must be real,
finite, and nonsparse. L must be a valid label matrix for image A. ROI
must be a logical array where all pixels that define the region of
interest are equal to true. FOREMASK and BACKMASK must be logical
arrays. FOREIND and BACKIND must be vectors of linear indices
identifying pixels in the label matrix L. Output image BW is the same
size as the label matrix L.
Notes
-----
1. The algorithm treats all subregions fully or partially outside the
ROI mask as belonging to the background. An optimal segmentation will
be obtained when the object that is desired is fully contained within
the ROI with a small amount of background pixels surrounding the
object.
2. For double and single images, the range of the image is assumed to
be [0 1]. For uint16, int16, and uint8 images, the range is assumed to
be the full range for the given data type.
3. For grayscale images, the size of L, FOREMASK, and BACKMASK must
match the size of the image A. For color and multi-channel images, L,
FOREMASK, and BACKMASK must be a 2D array with the first two dimensions
identical to the first two dimensions of the image A.
4. A given subregion of the label matrix should not be marked as
belonging to both the foreground mask and the background mask. If a
region of the label matrix contains pixels belonging to both the
foreground mask and background mask, the algorithm effectively treats
the region as unmarked.
5. All subregions outside the region of interest defined by ROI are
assumed to belong to the background. Marking one of these subregions as
belonging to foreground or background mask will have no effect on the
resulting segmentation.
Example 1
---------
% Read in image.
RGB = imread('peppers.png');
% Generate label matrix
L = superpixels(RGB,500);
% Select region of interest
figure;
imshow(RGB)
h = drawpolygon(gca,'Position',[72,105; 1,231; 0,366; 104,359;...
394,307; 518,343; 510,39; 149,72]);
roi = createMask(h,RGB);
% Perform GrabCut
BW = grabcut(RGB,L,roi);
% Create masked image.
maskedImage = RGB;
maskedImage(repmat(~BW,[1 1 3])) = 0;
figure;
imshow(maskedImage)
Example 2
---------
% Load 3D image
load mristack
V = mristack;
% Create 2D mask for initial foreground and background seed points
seedLevel = 10;
fseed = V(:,:,seedLevel) > 75;
bseed = V(:,:,seedLevel) == 0;
figure;
imshow(fseed)
figure;
imshow(bseed)
% Place seed points into empty 3D mask
fmask = zeros(size(V));
bmask = fmask;
fmask(:,:,seedLevel) = fseed;
bmask(:,:,seedLevel) = bseed;
% Create initial region of interest
roi = false(size(V));
roi(10:end-10,10:end-10,:) = true;
% Generate label matrix
L = superpixels3(V,500);
% Perform GrabCut
bw = grabcut(V,L,roi,fmask,bmask);
% Display 3D segmented image
figure;
volshow(bw);
See also SUPERPIXELS, LAZYSNAPPING, WATERSHED, LABELMATRIX, imageSegmenter.
Documentation for grabcut
doc grabcut
