Abstract: We take a pattern theoretic approach to recognizing and tracking ground-based targets in sequences of forward-looking infrared images acquired from an airborne platform. A rich set of transformations on objects represented by 3D faceted models are formulated to accommodate the variability found in FLIR imagery. An hypothesized scene, simulated from the emissive characteristics of the hypothesized scene elements, is compared with the collected data by a likelihood function based on sensor statistics. This likelihood is combined with a prior distribution defined over the set of possible scenes to form a posterior distribution. A jump-diffusion process empirically generates the posterior distribution. The jumps accommodate the discrete aspects of the estimation problem, such as adding and removing hypothesized targets and changing target types. Between jumps, a diffusion process refines the hypothesis by following the gradient of the posterior. Since the likelihood function may include likelihoods from other sensors and may be defined over past and current times, interframe processing and sensor fusion are natural consequences of the pattern theoretic approach. !38
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