Representations of thermodynamic variability in the automated understanding of FLIR scenes

Grenander's pattern theory offers a unified approach to characterizing variability in complex systems. Automatic target recognition systems for forward-looking infrared sensors must be robust to three kinds of variability: (1) Geometric variability - Target appearances vary with their orientations and positions; (2) Image variability - Target appearances vary with their thermodynamic state, and natural backgrounds consist of widely varying textures; (3) Complexity/scene variability - The number of targets encountered will not be known in advance, and targets may enter or leave the scene at random times. Pattern theoretic algorithms based on jump-diffusion processes which accommodate variabilities (1) and (3) have been proposed. The diffusions account for (1) by estimating positions and orientations, and the jumps account for (3) by adding and removing hypothesized targets and changing target types. Here we extend the work to better accommodate (2) by summarizing the thermodynamic state of targets with a parsimonious set of variables which become nuisance parameters in the Grenander/Bayesian formulation.