homogeneity: Homogeneity Metric for a GLCM

In sbarkerclarke-phd/CoOccurR: Generalized Application of Co-Occurrence Matrices and Haralick Texture

Homogeneity Metric for a GLCM

Description

Calculate the homogeneity feature or metric for a gray-level co-occurrence matrix. For definition and application, see Lofstedt et al. (2019) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pone.0212110")}.

Usage

homogeneity(x, ...)

## Default S3 method:
homogeneity(x, ...)

## S3 method for class 'matrix'
homogeneity(x, ...)

## S3 method for class 'FitLandDF'
homogeneity(x, nlevels, ...)

Arguments

x	gray-level co-occurrence matrix
...	additional parameters
nlevels	desired number of discrete gray levels

Value

homogeneity metric of x

Examples

## calculate homogeneity of arbitrary GLCM
# define arbitrary GLCM
x <- matrix(1:16, nrow = 4)

# normalize
n_x <- normalize_glcm(x)

# calculate homogeneity
homogeneity(n_x)

## calculate homogeneity of arbitrary fitness landscape
# create fitness landscape using FitLandDF object
vals <- runif(64)
vals <- array(vals, dim = rep(4, 3))
my_landscape <- fitscape::FitLandDF(vals)

# calculate homogeneity of fitness landscape, assuming 2 discrete gray levels
homogeneity(my_landscape, nlevels = 2)

## confirm value of homogeneity for fitness landscape
# extract normalized GLCM from fitness landscape
my_glcm <- get_comatrix(my_landscape, discrete = equal_discrete(2))

# calculate homogeneity of extracted GLCM
homogeneity(my_glcm)  # should match value of above homogeneity function call

sbarkerclarke-phd/CoOccurR documentation built on April 5, 2024, 1:48 p.m.