
NSF Org: |
ECCS Division of Electrical, Communications and Cyber Systems |
Recipient: |
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Initial Amendment Date: | August 17, 2009 |
Latest Amendment Date: | March 13, 2014 |
Award Number: | 0846750 |
Award Instrument: | Standard Grant |
Program Manager: |
Radhakisan Baheti
ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering |
Start Date: | September 1, 2009 |
End Date: | August 31, 2015 (Estimated) |
Total Intended Award Amount: | $399,951.00 |
Total Awarded Amount to Date: | $399,951.00 |
Funds Obligated to Date: |
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ARRA Amount: | $399,951.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
926 DALNEY ST NW ATLANTA GA US 30318-6395 (404)894-4819 |
Sponsor Congressional District: |
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Primary Place of Performance: |
225 NORTH AVE NW ATLANTA GA US 30332-0002 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | EPCN-Energy-Power-Ctrl-Netwrks |
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): | 47.041 |
ABSTRACT
Proposal Number: 0846750
This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
The project described in this proposal seeks to improve the stability and robustness of computer vision algorithms used in closed-loop systems. The research Endeavour examines the role control theory may play in enhancing closed-loop computer vision algorithms. The concept of understanding the interplay between control and computer vision is achieving prominence as vision is increasingly sought for automating processes.
Intellectual Merit:
As a sensor, the imaging system can be wrought with noise, either through the actual sensing process or through the geometry of the imaged scene (e.g., through clutter, occlusions, variable hypotheses, etc.). Thus, the vision task can be interpreted as a signal processing task in the presence of noise and uncertainty. By deriving observers for visual tracking systems that seek to estimate both target location and target boundary, the PI proposes to systematically derive a highly stable and robust visual tracking algorithm. The research Endeavour involves the definition of a probabilistic shape state and associated measurement models; the derivation of a full state observer for dynamic objects and environments; and the use of machine learning to impose soft and hard geometric constraints on the measurement model. The main difficulties lie in the richness of the visual field coupled with the noise inherent to all visual sensors, and the fragility of imposing constraints on the measurement model when the target itself is both flexible and corrupted by imaging noise.
Broader Impact:
Application of the proposed work will reduce the amount of human labor involved in many monotonous, repetitive, or time-intensive tasks. Efforts will be specifically driven by two application areas: workforce tracking for safety analysis of airport ground operations, construction site operations, surface mining operations, and microscopic cellular tracking and analysis. Both of these application areas are related to the health and well-being of the populace, have the potential to save lives, and will have a positive economic impact.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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