perceptually optimizing textures for layered surfaces
In this paper, we take a new look at the problem of texturing surfaces so that they can be displayed layered over each other but remain clearly visible. Finding optimal textures that solve this problem is complex because of the perceptual interactions between the visual effects of parameters controlling texture generation. Instead of using controlled experiments to investigate this problem, we use a genetic algorithm based human-in-the-loop parameter space search to build a large database of human-rated textures. This database is then analyzed with a varity of datamining techniques, including clustering, principle component analysis, neural networks, and histogram analysis. We detail this analysis, concluding with a set of guidelines for building strong layered surface textures, and a display of a number of example textures.
layered surfaces genetic algorithm neural networks perception principal component analysis visualization evaluation data mining