Image Statistics Based on Processing Fluency
person(given = "Stefan", family = "Mayer", email = "stefan@mayer-de.com",
role = c("aut", "cre"), comment = c(ORCID = "0000-0003-0034-7090"))
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.
R.utils, readbitmap, pracma, quadprog, magick, OpenImageR
grid, ggplot2, scales, shiny, testthat, knitr, rmarkdown
'utils.R' 'complexity.R' 'contrast.R' 'imagefluency-package.R'
'imagefluencyApp.R' 'self-similarity.R' 'simplicity.R'
'symmetry.R' 'typicality.R'

R.utils, readbitmap, pracma, magick, OpenImageR
grid, ggplot2, scales, shiny, testthat, mockery, knitr,
rmarkdown

grid, ggplot2, scales, shiny, testthat, mockery, knitr,
rmarkdown, furrr, future, pbmcapply, tictoc, dplyr

Synthesize images into characteristic features for time-series analysis or machine learning applications.  The package was originally intended for monitoring volcanic eruptions in video data by highlighting and extracting regions above the vent associated with plume activity.  However, the functions within are general and have wide applications for image processing, analyzing, filtering, and plotting.

Simon Barthelme [aut, cre],
Antoine Cecchi [ctb]
Fast image processing for images in up to 4 dimensions (two spatial dimensions, one time/depth dimension, one colour dimension). Provides most traditional image processing tools (filtering, morphology, transformations, etc.) as well as various functions for easily analysing image data using R.

Fast image processing for images in up to 4 dimensions (two spatial
dimensions, one time/depth dimension, one colour dimension). Provides most
traditional image processing tools (filtering, morphology, transformations,
etc.) as well as various functions for easily analysing image data using R.