Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Factorization-Based Approach to Articulated Motion Recovery
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Articulated Structure from Motion by Factorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Motion segmentation with missing data using power factorization and GPCA
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Generalized principal component analysis (GPCA)
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
RANBAR: RANSAC-based resilient aggregation in sensor networks
Proceedings of the fourth ACM workshop on Security of ad hoc and sensor networks
Implicit Non-Rigid Structure-from-Motion with Priors
Journal of Mathematical Imaging and Vision
Security and usability challenges of moving-object CAPTCHAs: decoding codewords in motion
Security'12 Proceedings of the 21st USENIX conference on Security symposium
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Articulated motions are partially dependent. Most of the existing segmentation methods, e.g. Costeira and Kanade[2], can not be applied to articulated motions. We propose a novel algorithm for articulated motion segmentation called RANSAC with priors. It does not require prior knowledge of the number of articulated parts. It is both robust and efficient. Its robustness comes from its RANSAC nature. Its efficiency is due to the priors, which are derived from the spectral affinities between every pair of trajectories. We test our algorithm with synthetic and real data. In some highly challenging case, where other motion segmentation algorithms may fail, our algorithm still achieves robust results. Though our algorithm is inspired by articulated motions, it also applies to independent motions which can be regarded as a special case and treated uniformly.