Structure Matrices & Convolution Kernels
The Wolfram Language provides built-in functions for generating standard structure matrices and convolution kernels in any number of dimensions, in a form that can be used directly in image processing, linear algebra, or other applications.
Shape Matrices
DiskMatrix — BoxMatrix — CrossMatrix — DiamondMatrix —
Convolution Kernels
GaussianMatrix — Gaussian and Gaussian derivatives
ShenCastanMatrix — exponential and exponential derivatives
SavitzkyGolayMatrix — Savitzky–Golay smoothing and derivative kernel
GaborMatrix — Gabor kernel
IdentityMatrix ▪ DiagonalMatrix
ConstantArray — constant array
CenterArray — embed an array at the center of another array
SparseArray — arbitrary sparse array
ArrayFlatten — form a matrix from tiles
ArrayReshape — reshape the array to new dimensions
ArrayPad — add padding to an array
ArrayResample — resample an array to new dimensions
ArrayFilter — apply a function to array neighborhood blocks