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DOI	10.1098/rsif.2019.0183
	Optimizing colour for camouflage and visibility using deep learning: the effects of the environment and the observer's visual system
	Fennell, J. G.1; Talas, L.1; Baddeley, R. J.1; Cuthill, I. C.2; Scott-Samuel, N. E.1
通讯作者	Fennell, J. G.
来源期刊	JOURNAL OF THE ROYAL SOCIETY INTERFACE
ISSN	1742-5689
EISSN	1742-5662
出版年	2019
卷号	16 期号:154
英文摘要	Avoiding detection can provide significant survival advantages for prey, predators, or the military; conversely, maximizing 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 minimize (or maximize) 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 minimizes or maximizes 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 generalized 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.
英文关键词	camouflage conspicuity dichromacy trichromacy deep learning visual perception
类型	Article
语种	英语
国家	England
开放获取类型	hybrid, Green Published, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000470117900021
WOS关键词	VISION ; EVOLUTION
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/217383
作者单位	1.Univ Bristol, Sch Psychol Sci, 12a Priory Rd, Bristol BS8 1TU, Avon, England; 2.Univ Bristol, Sch Biol Sci, Bristol Life Sci Bldg,24 Tyndall Ave, Bristol BS8 1TQ, Avon, England
推荐引用方式 GB/T 7714	Fennell, J. G.,Talas, L.,Baddeley, R. J.,et al. Optimizing colour for camouflage and visibility using deep learning: the effects of the environment and the observer's visual system[J],2019,16(154).
APA	Fennell, J. G.,Talas, L.,Baddeley, R. J.,Cuthill, I. C.,&Scott-Samuel, N. E..(2019).Optimizing colour for camouflage and visibility using deep learning: the effects of the environment and the observer's visual system.JOURNAL OF THE ROYAL SOCIETY INTERFACE,16(154).
MLA	Fennell, J. G.,et al."Optimizing colour for camouflage and visibility using deep learning: the effects of the environment and the observer's visual system".JOURNAL OF THE ROYAL SOCIETY INTERFACE 16.154(2019).