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fastGLCM  

'GLCM' Texture Features
View on CRAN: Click here


Download and install fastGLCM package within the R console
Install from CRAN:
install.packages("fastGLCM")

Install from Github:
library("remotes")
install_github("cran/fastGLCM")

Install by package version:
library("remotes")
install_version("fastGLCM", "1.0.3")



Attach the package and use:
library("fastGLCM")
Maintained by
Lampros Mouselimis
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2022-08-15
Latest Update: 2022-09-25
Description:
Two 'Gray Level Co-occurrence Matrix' ('GLCM') implementations are included: The first is a fast 'GLCM' feature texture computation based on 'Python' 'Numpy' arrays ('Github' Repository, ). The second is a fast 'GLCM' 'RcppArmadillo' implementation which is parallelized (using 'OpenMP') with the option to return all 'GLCM' features at once. For more information, see "Artifact-Free Thin Cloud Removal Using Gans" by Toizumi Takahiro, Zini Simone, Sagi Kazutoshi, Kaneko Eiji, Tsukada Masato, Schettini Raimondo (2019), IEEE International Conference on Image Processing (ICIP), pp. 3596-3600, .
How to cite:
Lampros Mouselimis (2022). fastGLCM: 'GLCM' Texture Features. R package version 1.0.3, https://cran.r-project.org/web/packages/fastGLCM. Accessed 30 Oct. 2026.
Previous versions and publish date:
(2026-07-09 07:37), 1.0.0 (2022-08-15 10:50), 1.0.1 (2022-09-04 16:30), 1.0.2 (2022-09-25 19:50)
Other packages that cited fastGLCM R package
View fastGLCM citation profile
Other R packages that fastGLCM depends, imports, suggests or enhances
Complete documentation for fastGLCM
Functions, R codes and Examples using the fastGLCM R package
Some associated functions: compute_elapsed_time . fastGLCM_Rcpp . fastglcm . inner_elapsed_time . plot_multi_images . 
Some associated R codes: RcppExports.R . fastGLCM.R . module.R .  Full fastGLCM package functions and examples
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