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CVPR
2001
IEEE

Flexible flow for 3D nonrigid tracking and shape recovery

14 years 11 months ago
Flexible flow for 3D nonrigid tracking and shape recovery
We introduce linear methods for model-based tracking of nonrigid 3D objects and for acquiring such models from video. 3D motions and flexions are calculated directly from image intensities without information-lossy intermediate results. Measurement uncertainty is quantified and fully propagated through the inverse model to yield posterior mean (PM) and/or mode (MAP) pose estimates. A Bayesian framework manages uncertainty, accommodates priors, and gives confidence measures. We obtain highly accurate and robust closed-form estimators by minimizing information loss from non-reversible (inner-product and least-squares) operations, and, when unavoidable, performing such operations with the appropriate error norm. For model acquisition, we show how to refine a crude or generic model to fit the video subject. We demonstrate with tracking, model refinement, and super-resolution texture lifting from low-quality lowresolution video.
Matthew Brand, Rahul Bhotika
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2001
Where CVPR
Authors Matthew Brand, Rahul Bhotika
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