This computes the categorical or binary structural similarity index metric on a whole-image scale. The difference between this and the default 2-D method is that this considers the whole image at once and one scale rather than computing the index over a sliding window and downsampling to consider it at other scales.
binssim( x, y, alpha = 1, beta = 1, gamma = 1, c1 = 0.01, c2 = 0.01, method = "Cohen", ... )
x, y | binary or categorical image |
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alpha | normalizing parameter, by default 1 |
beta | normalizing parameter, by default 1 |
gamma | normalizing parameter, by default 1 |
c1 | small normalization constant for the |
c2 | small normalization constant for the |
method | whether to use Cohen's kappa ( |
... | Constants can be passed to the components of the index. |
Structural similarity index.
set.seed(20181207) x <- matrix(sample(1:4, 10000, replace = TRUE), nrow = 100) y <- x for (i in 1:100) y[i, i] <- 1 for (i in 1:99) y[i, i + 1] <- 1 binssim(x, y)#> [1] 0.9806734