Thalamocortical oscillations in sleep and wakefulness
metric representation and tedious parameter estimation, the BPNN probabilistic modeling gives a more relaxed and more reliable solution, as evidenced by successful application to textured image modeling and segmentation of both artificial and real-world textures. It can be argued that the proposed BPNN modeling approach requires more parameters (interconnection weights of the BPNN) than the number of clique parameters used in a conventional M R F modeling. Even though this challenge is valid, there is no way to increase the number of clique parameters for a better performance without increasing the order of the neighborhood system under the very restricted MRF formulation. More importantly, when the order of the neighborhood system increases in an M R F model, the number of clique parameters increases exponentially, and the parameter estimation performance degrades rapidly. Conversely, the BPNN probabilistic modeling increases its number of interconnection weights linearly (i.e., the input dimension increases), with slight performance degradation observed from our experience. In spite of its seemingly superior performance based on a limited set of simulations, the proposed BPNN texture modeling can possibly suffer performance degradation in the presence of texture rotation, texture scaling, and also larger number of gray levels. All these difficulties are yet to be overcome before the BPNN texture modeling can be of practical use.