In our work we focused on biologically relevant parameters as the camouflage is targeted against human observers. In first order statistics we focused i.a. on local luminance, perceptual color difference in CIELAB color space, r.m.s. contrast and entropy. In the transformation-based higher order statistics we focused on spatial frequency distribution, power spectra, orientation bias and quefrency analysis via Fourier transformation and linear feature extraction via Radon Transformation.
This, at first, enables the possibility to parametrize camouflage patterns and textures in a comprehensive way, offering a similarity rating of textures compared to a mean background, but in particular facilitates the calculation of conspicuity maps, in which eye-catching regions of images are highlighted.
In this work we show that the linear combination of those conspicuity maps, gathered on different scales can provide a good value for local conspicuity and therefore directly acts as a useful quantification for camouflage, as drawing as little attention as possible to the camouflaged object quantified by a low conspicuity value results in a good camouflage rating.