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Optimising colour for camouflage and visibility using deep learning: the effects of the environment and the observer’s visual system

Research output: Contribution to journalArticle

Original languageEnglish
Article number20190183
Pages (from-to)20190183
Number of pages8
JournalJournal of the Royal Society Interface
Volume16
Issue number154
Early online date29 May 2019
DOIs
DateSubmitted - 26 Sep 2018
DateAccepted/In press - 1 May 2019
DateE-pub ahead of print - 29 May 2019
DatePublished (current) - May 2019

Abstract

Avoiding detection can provide significant survival advantages for prey, predators, or the military; conversely, maximising visibility would be useful for signalling. One simple determinant of detectability is an animal’s colour relative to its environment. But identifying the optimal colour to minimise (or maximise) detectability in a given natural environment is complex, partly because of the nature of the perceptual space. Here for the first time, using image processing techniques to embed targets into realistic environments together with psychophysics to estimate detectability and deep neural networks to interpolate between sampled colours, we propose a method to identify the optimal colour that either minimises or maximises visibility. We apply our approach in two natural environments (temperate forest and semi-arid desert) and show how a comparatively small number of samples can be used to predict robustly the most and least effective colours for camouflage. To illustrate how our approach can be generalised to other non-human visual systems, we also identify the optimum colours for concealment and visibility when viewed by simulated red-green colour-blind dichromats, typical for non-human mammals. Contrasting the results from these visual systems sheds light on why some predators seem, at least to humans, to have colouring that would appear detrimental to ambush hunting. We found that for simulated dichromatic observers, colour strongly affected detection time for both environments. In contrast, trichromatic observers were more effective at breaking camouflage.

    Research areas

  • deep learning, trichromacy, dichromacy, conspicuity, camouflage, visual perception

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via the Royal Society at https://royalsocietypublishing.org/doi/10.1098/rsif.2019.0183 . Please refer to any applicable terms of use of the publisher.

    Final published version, 1 MB, PDF-document

    Licence: CC BY

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