disMat | R Documentation |
Compute KL and ALK dissimiarlity matrices for the given stochastic textured surface images.
disMat(imgs, nb, cp=1e-3, subsample = c(1, .5), standardize = TRUE, keep.fits = FALSE, verbose=FALSE)
imgs |
a 3-dimensional array containing all images. |
nb |
the size of the neighborhood. It must be a 1-length or 3-length vector of positive integer(s). If the former, it is the same with a 3-length vector with the same elements. |
cp |
the minimal value for the |
subsample |
the portion of pixels in the given image |
standardize |
if |
keep.fits |
if |
verbose |
if set to |
the KL and AKL dissimilarity matrices.
Anh Bui
Bui, A.T. and Apley, D.W. (2019b) "An exploratory analysis approach for understanding variation in stochastic textured surfaces", Computational Statistics & Data Analysis, 137, 33-50.
## generate images: the first two are similar, the third is different with the other two phi1 <- c(.6, .6, .5) phi2 <- c(.35, .35, .3) imgs <- array(0, c(100,100,3)) for (j in 1:dim(imgs)[3]) imgs[,,j] <- sarGen(phi1 = phi1[j], phi2 = phi2[j], m = 100, n = 100, border = 50) ## compute KL and AKL dissimilarity matrices disMat(imgs = imgs, nb = 1)
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