Using virtual reality to investigate the visual features that determine the effectiveness of disruptive camouflage
Disruptive camouflage (DC) is a strategy by which animals utilise high contrast markings to visually break up their otherwise recognisable shape and prevent detection and possibly recognition. Edge enhancement (EE) is a phenomenon sometimes found alongside disruptive camouflage, where the contrast between two intersecting patches is accentuated. EE may aid in concealment by more closely matching shadowed environments or by inducing false depth cues, and/or by distracting observers from the true edge of the animal. In this thesis I have two general objectives: to examine the visual features that make disruptive camouflage and edge enhancement effective, and the development, creation, and testing of camouflage principles with virtual reality technology. To achieve these objectives the thesis is divided into two sets of studies: 2-dimensional experimentation, where I make use of computer-based detection experiments and machine learning techniques; and, 3-dimensional experimentation, where I make use of virtual reality systems. In summary, I discuss how the spatial and spectral relationship between the background and target influence the effectiveness of (edge-enhanced) disruptive camouflage, highlighting the need to consider spatial features alongside spectral features. Additionally, I present two experimental environments for the investigation of animal camouflage in VR, demonstrating that it is viable to use this technology to examine camouflage theory. This investigation provides support for the idea that VR can be used to supply both experimental control and ecological validity, paving the way for future research. There are many ways that this technology could be used to advance our understanding of camouflage effectiveness. For example, VR experiments could incorporate ecological factors, such as lighting and animal behaviour, into the experimental design.
Camouflage, Virtual Reality, Vision, Machine learning, Edge enhancement, Disruptive camouflage
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