A Computational Approach to the Quantification of Animal Camouflage
Derya Akkaynak, Ph.D., 2014
Ruth Rosentholtz, Advisor
Evolutionary pressures have led to some astonishing camouflage strategies in nature. Cephalopods can rapidly adapt the way their skin looks in color, texture and pattern, blending in with their backgrounds. This thesis uses a computational and data-driven approach to quantify camouflage patterns of cuttlefish in terms of color and pattern. First, we assess the color match of cuttlefish to the features in its natural background in the eyes of its predators. Then, we study overall body patterns to discover relationships and limitations between chromatic components. We also explore ways for unbiased data acquisition using consumer cameras and conventional spectrometers. This thesis makes the following contributions: (1) Proposes a methodology for scene-specific color calibration for the use of RGB cameras for accurate and consistent data acquisition. (2) Quantifies the degree of spectral contamination by relating the numerical aperture and diameter of the optical fiber of a spectrometer to measurement distance and angle. (3) Presents the first study assessing the color match of cuttlefish (S. officinalis) to its background using in situ spectrometry.(4)Develops a computational approach to pattern quantification using techniques from computer vision, statistics and pattern recognition; and introduces Cuttlefish72x5, the first database of calibrated linear images of cuttlefish.