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Miptoolbox Library

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