Learning Gestalt of surfaces in natural scenes

Abstract

We develop a computational model for scenes with surfaces that have rough and non-smooth small-scale structure but with a perceived global (larger-scale) geometric form. Examples include grass and meadow, surfaces textured with sand-paper, natural scenes having rough texture such as the skin of crocodile, pine cones, a field of sea urchins, forests, ripples and waves on water surfaces, etc. Another domain of examples arise in scientific exploration of microscopic images, such as the atomic force microscopy (AFM) images from alloys in materials science, molecular beam epitaxy (MBE), rough surfaces due to ballistic deposition (ED surfaces) and random deposition surfaces (RD). As a last example, one may translate some outstanding image processing problems of infra-red astronomy to understanding the random texture of clouds combined with noise, e.g. to describe algorithms that detect stars within noisy data provided by infra-red imaging devices.