Knowledge-Based Segmentation for Tracking Through Deep Turbulence
Author(s)
Vela, Patricio A.
Niethammer, Marc
Pryor, Gallagher D.
Tannenbaum, Allen R.
Butts, Robert
Washburn, Donald
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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. Index Terms—Active contours, Bayesian statistics, geometric flows, tracking, turbulence.
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Date
2008-05
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Text
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Article