imagefluency: Image Statistics Based on Processing Fluency
Get image statistics based on processing fluency theory. The
functions provide scores for several basic aesthetic principles that
facilitate fluent cognitive processing of images: contrast,
complexity / simplicity, self-similarity, symmetry, and typicality.
See Mayer & Landwehr (2018) <doi:10.1037/aca0000187> and Mayer & Landwehr
(2018) <doi:10.31219/osf.io/gtbhw> for the theoretical background of the methods.
Version: |
0.2.4 |
Depends: |
R (≥ 3.2.3) |
Imports: |
R.utils, readbitmap, pracma, magick, OpenImageR |
Suggests: |
grid, ggplot2, scales, shiny, testthat, mockery, knitr, rmarkdown, furrr, future, pbmcapply, tictoc, dplyr |
Published: |
2022-08-31 |
Author: |
Stefan Mayer
[aut, cre] |
Maintainer: |
Stefan Mayer <stefan at mayer-de.com> |
BugReports: |
https://github.com/stm/imagefluency/issues/ |
License: |
GPL-3 |
URL: |
https://stm.github.io/imagefluency/,
https://github.com/stm/imagefluency/,
https://doi.org/10.5281/zenodo.5614666 |
NeedsCompilation: |
no |
Materials: |
README NEWS |
CRAN checks: |
imagefluency results |
Documentation:
Downloads:
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