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.

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Description file content

Package
OpenImageR
Type
Package
Title
An Image Processing Toolkit
Version
1.1.3
Date
2018-12-09
Depends
R(>= 3.2.3)
Imports
Rcpp (>= 0.12.17), shiny, jpeg, png, tiff, utils, R6
Maintainer
Lampros Mouselimis
BugReports
https://github.com/mlampros/OpenImageR/issues
URL
Description
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, . 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, and (ii) "SLIC Superpixels", Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Suesstrunk, EPFL Technical Report no. 149300, June 2010.
License
GPL-3
Copyright
inst/COPYRIGHTS
SystemRequirements
The package requires a C++11 compiler.
LinkingTo
Rcpp, RcppArmadillo (>= 0.8.0)
Suggests
testthat, knitr, rmarkdown, grid, covr
RoxygenNote
6.1.0
VignetteBuilder
knitr
NeedsCompilation
yes
Packaged
2018-12-09 17:58:02 UTC; lampros
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))
Repository
CRAN
Date/Publication
2018-12-09 18:40:03 UTC

install.packages('OpenImageR')

1.1.3

4 days ago

https://github.com/mlampros/OpenImageR

Lampros Mouselimis

GPL-3

Depends on

R(>= 3.2.3)

Imports

Rcpp (>= 0.12.17), shiny, jpeg, png, tiff, utils, R6

Suggests

testthat, knitr, rmarkdown, grid, covr

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