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imagefluency (version 0.2.5)

imagefluency-package: imagefluency: Image Statistics Based on Processing Fluency

Description

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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) tools:::Rd_expr_doi("10.1037/aca0000187") and Mayer & Landwehr (2018) tools:::Rd_expr_doi("10.31219/osf.io/gtbhw") for the theoretical background of the methods.

Arguments

Author

Maintainer: Stefan Mayer stefan@mayer-de.com (ORCID)

Details

The main functions are:

  • img_contrast to get the visual contrast of an image

  • img_complexity to get the visual complexity of an image (equals 1 minus image simplicity)

  • img_self_similarity to get the visual self-similarity of an image

  • img_simplicity to get the visual simplicity of an image (equals 1 minus image complexity)

  • img_symmetry to get the vertical and horizontal symmetry of an image

  • img_typicality to get the visual typicality of a list of images relative to each other

Other helpful functions are:

  • img_read wrapper function to read images using readbitmap::read.bitmap

  • run_imagefluency to launch a Shiny app for an interactive demo of the main functions

  • rgb2gray to convert images from RGB into grayscale

References

Mayer, S. & Landwehr, J, R. (2018). Quantifying Visual Aesthetics Based on Processing Fluency Theory: Four Algorithmic Measures for Antecedents of Aesthetic Preferences. Psychology of Aesthetics, Creativity, and the Arts, 12(4), 399--431. tools:::Rd_expr_doi("10.1037/aca0000187")

Mayer, S. & Landwehr, J. R. (2018). Objective measures of design typicality. Design Studies, 54, 146--161. tools:::Rd_expr_doi("10.31219/osf.io/gtbhw")

See Also

Useful links: