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Performs 1-D Perona–Malik diffusion.
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Performs affine curvature motion diffusion.
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Computes both the eigenvalues and the shape index for an arc
consisting of set of points.
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Compute forward, backward and central differencesof an image.
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Computes the between-class variance, the optimal
threshold based on its maximization.
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This is the iterative version of the mipbcv for three
regions; thus it returns two thresholds.
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This function returns the optimal threshold value using
the 2-D Shannon entropy method.
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Compute forward, backward and central differences of an image.
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Performs circular convolution on closed boundaries.
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Calculates the circularity.
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Computes the mean from a histogram for a range of
intensities.
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Provides/returns the commonly used
constants in the basic sciences.
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Computes the 3-D correlation coefficient.
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Simulates an image with correlated pixels.
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Computes the curvature of boundary points.
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Computes the variance from a histogram for a range of
intensities.
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Creates a disk image of a given radius.
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Computes and returns the derivative of a 1-D
Gaussian.
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This function computes an optimal threshold using the
entropy-based criterion function.
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Segments the input image by fuzzy C-means algorithm
and returns the multilevel image at the output. It
requires number of regions as the input.
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This is Fitzgibbon’s algorithm that fits ellipse to a set of
boundary points. The algorithm is provided as it is in his Web
site except that it is renamed .
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Compute forward, backward and central differences of an image.
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Creates 1-, 2-D and 3-D Gaussian functions of a
given size sigma.
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Converts a gray-level image to multilevel images of n
regions, which take on values from 1 to n, using the
thresholds t1,...,tn-1.
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mipstructuretensor Computes the structure tensor of an image.
Simulates a hexagon image.
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Computes 2-D histograms
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The ICM algorithm for 2-D images with two regions (2r)
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The ICM algorithm for 2-D images with multiple regions (mr)
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This is the vectorized version of the ICM algorithm that
updates every pixel in parallel.
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Bins (by summing) image pixels rowwise or
columnwise to create a smaller image
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Adds Gaussian noise to an existing image.
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Computes the histogram.
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Overlays a binary image on a gray-level image and
returns the overlaid image.
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Performs 2-D nonlinear scalar diffusion, in which the
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Performs 3-D nonlinear scalar diffusion. It is the 3-D
version of the function mipisodiffusion2d.
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Computes 2-D histogram of the joint distribution of
two images.
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Segments the input image by K-means clustering
algorithm and returns the multilevel image at the
output. It requires number of regions as the input.
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Computes the Kurita’s criterion function and the
optimal thresholds based on its maximization.
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Performs 1-D linear filtering using the Laplacian.
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Performs windowing on CT images.
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Computes the minimum error function criterion and
the optimal thresholds based on its minimization.
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Gets the intensities of the 2-D neighborhood 4-pixel
or 8-pixel.
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The 3-D version of mipneighborhood2d
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Computes the scatter matrix.
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Creates orthogonal views from a 3-D image
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Computes the statistics of regions given a gray-level and a
binary image in which each region is labeled with a
different label.
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Removes small objects from a binary 2-D or 3-Dimage.
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This function returns the optimal threshold value using
the 2-D Tsallis entropy method.
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Computes and returns the second derivative of a
1-D Gaussian.
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Computes the second derivative Ixx or Iyy of an image
I.
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Compute the second partial derivative Ixy or Iyx
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Creates a 2-D sigmoid function.
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Creates a 1-D sigmoid function.
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Creates a sphere of known size.
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Computes the structure tensor of an image.
miphexagon Simulates a hexagon image.
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Computes the total energy.
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Performs total-variation–based diffusion.
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Calculates the volume properties.
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mip Removes undefined numbers such infs and nans
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mip Segments images via Gaussian/normal mixture
modeling.
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