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: | 1.3.0 |
| Depends: | R (≥ 3.2.3) |
| Imports: | Rcpp (≥ 0.12.17), graphics, grDevices, grid, shiny, jpeg, png, tiff, R6, lifecycle, tools |
| LinkingTo: | Rcpp, RcppArmadillo (≥ 0.8.0) |
| Suggests: | testthat, knitr, rmarkdown, covr |
| Published: | 2023-07-08 |
| DOI: | 10.32614/CRAN.package.OpenImageR |
| Author: | Lampros Mouselimis
 [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)),
Oleh Onyshchak [cph] (Author of the Python code of the WarpAffine
function) |
| Maintainer: | Lampros Mouselimis <mouselimislampros at gmail.com> |
| BugReports: | https://github.com/mlampros/OpenImageR/issues |
| License: | GPL-3 |
| Copyright: | inst/COPYRIGHTS OpenImageR copyright details |
| URL: | https://github.com/mlampros/OpenImageR |
| 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 |
Documentation:
| Reference manual: | OpenImageR.html , OpenImageR.pdf |
| Vignettes: |
Gabor Feature extraction (source) Image segmentation based on Superpixels and Clustering (source) Functionality of the OpenImageR package (source, R code) Warp Affine using R (source, R code) |
Downloads:
| Package source: | OpenImageR_1.3.0.tar.gz |
| Windows binaries: | r-devel: OpenImageR_1.3.0.zip, r-release: OpenImageR_1.3.0.zip, r-oldrel: OpenImageR_1.3.0.zip |
| macOS binaries: | r-release (arm64): OpenImageR_1.3.0.tgz, r-oldrel (arm64): OpenImageR_1.3.0.tgz, r-release (x86_64): OpenImageR_1.3.0.tgz, r-oldrel (x86_64): OpenImageR_1.3.0.tgz |
| Old sources: | OpenImageR archive |
Reverse dependencies:
| Reverse imports: | CNVScope, fastGLCM, greenR, imagefluency, schemr, streetscape, SuperpixelImageSegmentation |
| Reverse linking to: | fastGLCM, SuperpixelImageSegmentation |
| Reverse suggests: | ClusterR, regtools, VMDecomp |
Linking:
Please use the canonical form https://CRAN.R-project.org/package=OpenImageR to link to this page.