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.
Type
Publication
IEEE Trans. Control. Syst. Technol.
Marc Niethammer
Professor of Computer Science
My research interests include image registration, image segmentation, shape analysis, machine learning, and biomedical applications.