N-dimensional Gaussian filter



Creates an image of a Gaussian with arbitrary covariance matrix. The dimensionality and size of the filter is determined by dims (eg dims=[10 10] creates a 2D filter of size 10×10).

If mu==[], it is calculated to be the center of the n-dim image. C can be a full nxn covariance matrix, or an nx1 vector of variance. In the latter case C is calculated as C=diag(C).

If C=[]; then C=(dims/6).^2, ie it is transformed into a vector of variances such that along each dimension the variance is equal to (siz/6)^2.


G = filterGauss( dims, [mu], [C], [show] )

dims – n element vector of dimensions of final Gaussian
mu – [] n element vector specifying the mean
C – [] nxn cov matrix, nx1 set of vars, or variance
show – [0] figure to use for optional display

G – image of the created Gaussian

Steerable 2D Gaussian derivative filter

Image Denoising using Scale Mixtures of Gaussians in the Wavelet Domain


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SKU: P2018F222_filter Category: