Georgia Tech Research Corporation
Grant: $399,951 - National Science Foundation - Aug. 17, 2009
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Award Description: 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.
Project Description: See award description
Jobs Summary: None (Total jobs reported: 0)
Project Status: Less Than 50% Completed
This award's data was last updated on Aug. 17, 2009. Help expand these official descriptions using the wiki below.
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