Department of Pattern Recognition

(eng): Rough textures describe a general visual appearance of real-world materials with regard to view and illumination directions. As the massive size and dimensionality of such representations is a main limitation of their broader use, efficient parameterization and compression methods are needed. Our method is based on estimating the joint probability density of the included nine spatial, directional, and spectral variables in the form of a Gaussian mixture of product components. Our reflectance prediction formula can be expressed analytically as a simple continuous function of input variables and allows fast analytic evaluation for arbitrary spatial and directional values without need for a lengthy interpolation from a finite grid of angular measurements. This method achieves high compression ratio increasing linearly with texture spatial resolution.