
NSF Org: |
ECCS Division of Electrical, Communications and Cyber Systems |
Recipient: |
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Initial Amendment Date: | September 11, 2006 |
Latest Amendment Date: | July 14, 2009 |
Award Number: | 0622006 |
Award Instrument: | Standard Grant |
Program Manager: |
Radhakisan Baheti
ECCS Division of Electrical, Communications and Cyber Systems ENG Directorate for Engineering |
Start Date: | September 1, 2006 |
End Date: | August 31, 2010 (Estimated) |
Total Intended Award Amount: | $0.00 |
Total Awarded Amount to Date: | $249,994.00 |
Funds Obligated to Date: |
FY 2009 = $12,000.00 |
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: |
01000910DB NSF RESEARCH & RELATED ACTIVIT |
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
The project described in this proposal seeks to a develop a framework for generating robust
computer vision algorithms for use in closed-loop systems. A principal goal of the research endeavour
is to examine the role that control theory may play in enhancing closed-loop computer vision
algorithms.
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. Consequently, the computer
vision process can be interpreted as a signal processing task in the presence of noise and uncertainy.
This is further compounded for closed-loop vision systems, because the control effort can induce
additional disturbances.
Through an analysis of the classical Luenberger observer for finite- dimensional systems, the PI
proposes to systematically build up a similar framework for filtering of closed curves, which are the
by-products of th e computer vision algorithms known as active contours. Essentially, this research
endeavour involves the development of a Kalman filter algorithm for closed curves, and involves a
principled predict and update scheme. The main investigative challenge lies in the fact that closed
curves form an infinite-dimensional space which classical state-space observer theory is not capable
of handling.
Broader Impact. The successful consideration of computer vision algorithms as components of
a control and dynamical system has the ability to tremendously increase their level of robustness,
without severely complicating the nature of their processing. As a particular application demonstrating
the potential impact and challenges of this research avenue within a specific area, the PI
seeks to study the closed-loop control and estimation of bio-membranes at the mico-scale. The observer
concept described herein has the capacity to improve the signal generated from vision-based
algorithms that form the main information pathway of a closed-loop process.
The proposed education plan incorporates many of these ideas into existing computer vision
courses, as they are essential components in the use of computer vision for feedback-driven systems.
Secondly, summer research and senior-design projects are anticipated to motivate the importance
of control and dynamical systems theory for computer vision.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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