Knowledge-Based Segmentation for Tracking Through Deep Turbulence

Patricio A. Vela, Marc Niethammer, Gallagher Pryor, Allen R. Tannenbaum, Robert Butts, Donald Washburn

Abstract: A combined knowledge-based segmentation/active contour algorithm is used for target tracking through turbulence. The algorithm utilizes Bayesian modeling for segmentation of noisy imagery obtained through longrange, laser imaging of a distance target, and active contours for tip tracking. The algorithm demonstrates improved target tracking performance when compared to weighted centroiding. Open-loop and closed-loop comparisons of the algorithms using simulated imagery validate the hypothesis.