.selfsim

Returns the self-similarity of an image matrix as the degree to which the slope of the log-log power spectrum falls with power according to the value of 2.

internal

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.

Stefan Mayer

imagefluency

Image Statistics Based on Processing Fluency

.selfsim function

<dl><dt>img</dt>
<dd>A matrix of numeric values or integer values, preferably
by square size.</dd>
<dt>full</dt>
<dd>logical. Should the full frequency range be used for
interpolation?</dd>
<dt>raw</dt>
<dd>logical. Should the raw value of the regression slope be returned?</dd>
<dt>logplot</dt>
<dd>logical. Should the log-log power spectrum of the image be
plotted?</dd></dl>

Arguments

.selfsim — .selfsim

<dl>

<dt>img</dt>
<dd>A matrix of numeric values or integer values, preferably
by square size.</dd>


<dt>full</dt>
<dd>logical. Should the full frequency range be used for
interpolation?</dd>


<dt>raw</dt>
<dd>logical. Should the raw value of the regression slope be returned?</dd>


<dt>logplot</dt>
<dd>logical. Should the log-log power spectrum of the image be
plotted?</dd>

</dl>

.selfsim: .selfsim

Description

Usage

Value

Arguments