OpenImageR: An Image Processing Toolkit

Incorporates functions for image preprocessing, filtering and image recognition. The package takes advantage of 'RcppArmadillo' to speed up computationally intensive functions. The histogram of oriented gradients descriptor is a modification of the 'findHOGFeatures' function of the 'SimpleCV' computer vision platform, the average_hash(), dhash() and phash() functions are based on the 'ImageHash' python library. The Gabor Feature Extraction functions are based on 'Matlab' code of the paper, "CloudID: Trustworthy cloud-based and cross-enterprise biometric identification" by M. Haghighat, S. Zonouz, M. Abdel-Mottaleb, Expert Systems with Applications, vol. 42, no. 21, pp. 7905-7916, 2015, <doi:10.1016/j.eswa.2015.06.025>. The 'SLIC' and 'SLICO' superpixel algorithms were explained in detail in (i) "SLIC Superpixels Compared to State-of-the-art Superpixel Methods", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p. 2274-2282, May 2012, <doi:10.1109/TPAMI.2012.120> and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.

Version: 1.1.8
Depends: R (≥ 3.2.3)
Imports: Rcpp (≥ 0.12.17), graphics, grid, shiny, jpeg, png, tiff, R6
LinkingTo: Rcpp, RcppArmadillo (≥ 0.8.0)
Suggests: testthat, knitr, rmarkdown, covr
Published: 2021-05-04
Author: Lampros Mouselimis ORCID iD [aut, cre], Sight Machine [cph] (findHOGFeatures function of the SimpleCV computer vision platform), Johannes Buchner [cph] (average_hash, dhash and phash functions of the ImageHash python library), Mohammad Haghighat [cph] (Gabor Feature Extraction), Radhakrishna Achanta [cph] (Author of the C++ code of the SLIC and SLICO algorithms (for commercial use please contact the author))
Maintainer: Lampros Mouselimis <mouselimislampros at>
License: GPL-3
Copyright: inst/COPYRIGHTS
OpenImageR copyright details
NeedsCompilation: yes
SystemRequirements: libarmadillo: apt-get install -y libarmadillo-dev (deb), libblas: apt-get install -y libblas-dev (deb), liblapack: apt-get install -y liblapack-dev (deb), libarpack++2: apt-get install -y libarpack++2-dev (deb), gfortran: apt-get install -y gfortran (deb), libjpeg-dev: apt-get install -y libjpeg-dev (deb), libpng-dev: apt-get install -y libpng-dev (deb), libfftw3-dev: apt-get install -y libfftw3-dev (deb), libtiff5-dev: apt-get install -y libtiff5-dev (deb)
Citation: OpenImageR citation info
Materials: README NEWS
CRAN checks: OpenImageR results


Reference manual: OpenImageR.pdf
Vignettes: Gabor Feature extraction
Image segmentation based on Superpixels and Clustering
Functionality of the OpenImageR package
Package source: OpenImageR_1.1.8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: OpenImageR_1.1.8.tgz, r-oldrel: OpenImageR_1.1.8.tgz
Old sources: OpenImageR archive

Reverse dependencies:

Reverse imports: CNVScope, imagefluency, schemr, SuperpixelImageSegmentation
Reverse linking to: SuperpixelImageSegmentation
Reverse suggests: ClusterR


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