Classification of 3D macro texture using perceptual observables

A new method for analyzing macro texture using perceptual observables is presented. The typical geometric Gestalt grouping criteria such as proximity and parallelism are extended with descriptive measures of topology and photometry enabled by region neighborhood analysis. It is proposed that these perceptual measures provide a common description of image content encompassing both macro texture and perceptual grouping. This theory enables a new algorithm for macro texture classification that is invariant to rotation, and robust against very large changes in illumination, viewpoint and scale. The classification process also provides a method to determine which perceptual attributes are the most relevant for discriminating between various textures and objects.