Project Details

Description

The power of modern statistical techniques is exploited to develop novel approaches to fundamental problems in image understanding. The common statistical foundation for a broad class of vision tasks is identified first, and then the underlying key problems are solved in a rigorous framework. For example, since heteroscedasticity inherently appears in most 3D vision tasks the development of a complete estimation procedure (which includes imposing further geometric constraints on the parameter estimates) is of great importance for computer vision. Such a procedure can provide a faster and more reliable alternative to the widely used (and too general) nonlinear Levenberg-Marquardt method. The task of deriving the 3D description of a scene (static or dynamic) from an image sequence captured with an uncalibrated camera was chosen as a testbed. Estimation problems related to self-calibration, object recognition supported by uncertainty information, will not only allow us to enhance our existing toolbox but also to gain expertise in integrating these tools in a closed-loop autonomous vision system.

StatusFinished
Effective start/end date6/1/005/31/04

Funding

  • National Science Foundation: $379,514.00

Fingerprint

Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.