Project Grant 2232300

Award Date 4/1/23
Completion Date 3/31/26
Dollars Obligated $150K
Federal Grant Program
47.070
Assistance Type
Project Grant
Place of Performance
Fairfax, VA, USA
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This three-year project grant from the National Science Foundation's Computer and Information Science and Engineering program (CFDA 47.070) provides $149,572 to George Mason University to develop a generalizable motion field estimator using neural networks. The goal is to enhance computer vision tasks in long-range imaging, where input images lack stereo information and turbulence introduces motion artifacts that deteriorate performance.

The university will pursue three research thrusts. The first will develop algorithms to estimate and recover motion fields accounting for both object motion and turbulence. The second will develop quantitative turbulence motion models applicable to air and water environments using deep learning. The third will integrate turbulence motion fields into visual computing pipelines to benefit long-range tasks such as navigation, tracking, and monitoring. A large motion field dataset with true turbulent parameters will also be collected to facilitate machine learning algorithm development.

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