Digital anatomy in general, and brain anatomy in particular, is intended to represent and process anatomical patterns in 3D observed with CT, MR, and PET, as well as to facilitate automatic recognition and component measurement of said anatomies. The research primarily involves developing jump-diffusion algorithms for pattern inference based on knowledge about the vehicle dynamics and the physics of the sensors. In this approach templates are deformed by Lie groups of transformations.
Natural language lends itself nicely for representation by stochastic models.
Our focus is on mathematical representations of colloquial language. Of special interest to us are connector graphs with cycles. Non-numeric pattern theory for representing knowledge about history, literature, social systems. A selection of Matlab files related to computer vision are included in the gzipped tar file seeing.tar.gz or you can browse the files individually .