3-D structure recovery from 2-D observations

Authors:
Huiyu Zhou;Gerald Schaefer;Tangwei Liu;Faquan Lin
Affiliations:
Queen's University Belfast, Belfast, UK;Loughborough University, Loughborough, UK;Guangxi Medical University, Nanning, P.R. China;Guangxi Medical University, Nanning, P.R. China
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 5
Cited 0

Shape and motion from image streams under orthography: a factorization method

International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Self-Calibration and Metric Reconstruction Inspite of Varying and Unknown Intrinsic Camera Parameters

International Journal of Computer Vision - 1998 Marr Prize
A Real-Time Human Stress Monitoring System Using Dynamic Bayesian Network

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Non-Rigid Metric Shape and Motion Recovery from Uncalibrated Images Using Priors

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we present a novel method for simultaneously determining three dimensional motion and structure of a non-rigid object from its uncalibrated two dimensional data with Gaussian or non-Gaussian distributions. A non-rigid motion can be treated as a combination of a rigid component and a non-rigid deformation. To reduce the high dimensionality of the deformable structure or shape, we estimate the probability distribution function of the structure through random sampling, integrating an established probabilistic model. The fitting between the observations and the estimated 3-D structure is evaluated using the pooled variance estimator. Applications of the proposed method to both synthetic and real image sequences show promising results.