| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | March 15, 2023 |
| Latest Amendment Date: | March 15, 2023 |
| Award Number: | 2232300 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Jie Yang
jyang@nsf.gov (703)292-4768 IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | April 1, 2023 |
| End Date: | March 31, 2026 (Estimated) |
| Total Intended Award Amount: | $149,572.00 |
| Total Awarded Amount to Date: | $149,572.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
4400 UNIVERSITY DR FAIRFAX VA US 22030-4422 (703)993-2295 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
4400 UNIVERSITY DR FAIRFAX VA US 22030-4422 |
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Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Robust Intelligence |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Data-driven computer vision approaches suffer from deteriorated performance when the input images are captured from long distance. For example, images from unmanned aerial vehicles (UAVs), satellites, and reconnaissance cameras lack stereo information causing 3D reconstruction and depth estimation to fail. Turbulence caused by air and water also causes light rays to deflect from their original path and introduces noticeable motion artifacts like blurriness and distortion. This project develops a generalizable motion field estimator using neural networks coupled with specific hardware settings to enhance computer vision tasks in long-range imaging. Successful development of such a motion field estimator can enable applications of computer vision systems at long distances and/or under turbulent environments including UAV navigation, object tracking and detection, and long-range monitoring. The project has broader impact in industrial applications which leverage such technologies. In addition, research results will be integrated into new course materials for physics-informed computer vision and computational photography classes. The project will provide training to underrepresented students and outreach to K-12 students throughout its duration.
This project will develop computational solutions to decouple the entangled motion fields and use turbulence motion to enhance visual computing applications in long-range imaging. This research is motivated by the observation that turbulence-induced motion fields can provide depth and sub-pixel color information, which is crucial in restoring scenes with high-frequency details. To achieve this goal, the project will pursue three research thrusts: 1) neural field decoupling of object and turbulence motion; 2) reconstructing turbulence strength and flows from passive visual imagery; and 3) motion field guided intelligent foveation for long-range imaging. The first thrust will develop algorithms for estimating and recovering motion fields with both object and turbulence motion by investigating physics-based velocity fields. The second thrust will develop tractable quantitative turbulence motion models that can be applied to both air and water environments using deep neural networks. The third thrust will integrate the turbulence motion field into different visual computing pipelines to benefit long-range computer vision tasks. This project will collect a large motion field dataset with true turbulent parameters of different media types and turbulence strengths, which can facilitate the development of data-driven machine learning algorithms for long-range computer vision.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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