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SUPERPIXELS3 3-D superpixel over-segmentation of 3-D images
[L,NumLabels] = superpixels3(A,N) computes 3-D superpixels of 3-D image
A using N as the requested number of superpixels. N must be between 1
and the number of pixels in the image A. The first output argument, L, is
a label matrix of type double. The second output argument, NumLabels,
is the actual number of 3-D superpixels that were computed.
[L,NumLabels] = superpixels3(___,Name,Value,...) computes superpixels of A
with Name-Value pairs used to control aspects of the segmentation.
Parameters include:
'Compactness' - Numeric scalar specifying the compactness
parameter of the SLIC algorithm. Compactness
controls the shape of superpixels. A higher
value for compactness makes superpixels more
regularly shaped/square. A lower value makes
superpixels adhere to boundaries better, making
them irregularly shaped. The allowed range of
Compactness is (0 Inf). Typical values for
Compactness are in the range [0.01, 0.1]. If
this parameter is not specified, the default
value is chosen as 0.001 if METHOD is 'slic0'
and 0.05 if METHOD is 'slic'.
'Method' - String specifying the algorithm used to compute
superpixels. Supported options are 'slic' and
'slic0'. When 'Method' is 'slic0', the SLIC0
algorithm is used to adaptively refine
'Compactness' after the first iteration. When
'Method' is 'slic', 'compactness' is constant
during clustering.
Default: 'slic0'
'NumIterations' - Numeric scalar specifying the number of
iterations used in the clustering phase of the
algorithm. For most problems it is not
necessary to adjust this parameter.
Default: 10
Class Support
-------------
The input image A must be a real, non-sparse matrix of the following
classes: int8, uint8, int16, uint16, int32, uint32, single, or double.
Notes
-----
1. When using the 'slic0' method, it is generally not necessary to adjust
'Compactness' parameter. The intention of 'slic0' is to adaptively
refine 'Compactness' automatically and eliminate the need for users
to determine a good value of 'Compactness' for themselves.
References:
-----------
[1] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi,
Pascal Fua, and Sabine Susstrunk, SLIC Superpixels Compared to
State-of-the-art Superpixel Methods, IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274 - 2282,
May 2012.
[2] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi,
Pascal Fua, and Sabine Susstrunk, SLIC Superpixels, EPFL Technical
Report no. 149300, June 2010.
Example
-------
% Compute 3-D superpixels of input volumetric intensity image. Form an
% output image where each pixel is set to the mean color of its
% corresponding superpixel region.
load mri;
D = squeeze(D);
A = ind2gray(D,map);
[L,N] = superpixels3(A, 34);
% Show all xy-planes progressively with superpixel boundaries.
imSize = size(A);
% Create a stack of RGB images to display the boundaries in color.
imPlusBoundaries = zeros(imSize(1),imSize(2),3,imSize(3),'uint8');
for plane = 1:imSize(3)
BW = boundarymask(L(:, :, plane));
% Create an RGB representation of this plane with boundary shown
% in cyan.
imPlusBoundaries(:, :, :, plane) = imoverlay(A(:, :, plane), BW, 'cyan');
end
implay(imPlusBoundaries,5)
% Set color of each pixel in output image to the mean intensity of
% the superpixel region.
% Show the mean image next to the original.
pixelIdxList = label2idx(L);
meanA = zeros(size(A),'like',D);
for superpixel = 1:N
memberPixelIdx = pixelIdxList{superpixel};
meanA(memberPixelIdx) = mean(A(memberPixelIdx));
end
implay([A meanA],5);
See also superpixels, boundarymask, imoverlay, label2idx, implay.
Documentation for superpixels3
doc superpixels3
