A Bayesian model of camouflage detection by humans
Abhranil Das, Wilson S. Geisler · Journal of Vision · 2025
Camouflage is an impressive feat of biological evolution, but so is its detection by predators and prey. Well-camouflaged animals copy the luminance, contrast, colour and texture of their natural backgrounds, leaving only the animals’ boundary available for detection. This is one of the hardest cases of visual detection and reveals many detection strategies and their limits. We conducted experiments where humans detect synthetic camouflaged targets across varying conditions, including different textures. To explain this data, we developed a principled detection model that follows human optics and biologically plausible computations and is informed by the statistics of the relevant features in natural images. The model filters an image with human optics, then computes edge gradients and groups them into edge contours. It then computes several contour features: the fraction of area they cover, their lengths, position and orientation alignments with the true target boundary, curvatures, and edge powers at 5 scales. Additionally, it computes histograms of edge gradient magnitude, orientation, and their product (a proxy for their correlation) across all pixels. In parallel, we model the statistical distribution of each of these features over natural images, by computing them on our database of optics-filtered natural image patches. Using these known feature distribution families, we then construct optimal Bayesian decision variables that measure whether the features in the camouflage image are the same over the target boundary region, as compared to outside it. We combine these feature decision variables using a multivariate Gaussian model, which outputs a final detection response, as well as the relative contribution of each feature to detection. We fit these to our experimental data so that the single principled model can predict human camouflage detection performance across our entire array of diverse stimuli, and account for many of our parametric experimental observations.