Markov/Gibbs texture modeling: aura matrices and temperature effects

We use a new \aura" framework to rewrite the non-linear energy function of a homogeneous anisotropic Markov/Gibbs random eld (MRF) as a linear sum of aura measures. The new formulation relates MRF's to co-occurrence matrices. It also provides a physical interpretation of MRF textures in terms of the mixing and separation of graylevel sets, and in terms of boundary maximization and minimization. Within this framework, we introduce the use of temperature for texture modeling and show how the parameters of the MRF can be interpreted as temperature annealing rates. In particular, we show evidence for a transition temperature, above which all patterns generated will be visually similar, and below which a pattern evolves down to its ground state. Finally, we describe brieey some new results which characterize the ground state patterns.