In this paper we will present a conspicuity quantification model based on anomaly detection. This model extracts numerous local image parameters, in first and higher order (transformation-based) statistics and calculates local conspicuity by a multiscale center-surround comparison, as a point in an image draws attention to it, if it significantly differs from its surroundings in one or more relevant parameters. This is also biologically substantiated, as many parts of the visual system calculate center-surround differences, for example in color or luminance.
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.