Elements of information theory
Elements of information theory
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
Multibody Grouping from Motion Images
International Journal of Computer Vision
A view of the EM algorithm that justifies incremental, sparse, and other variants
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
A Multibody Factorization Method for Independently Moving Objects
International Journal of Computer Vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Unsupervised Learning of Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Skeletal Parameter Estimation from Optical Motion Capture Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Automatic Kinematic Chain Building from Feature Trajectories of Articulated Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Learning Parts-Based Representations of Data
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Skeletons from Motion
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Learning probabilistic models for visual motion
Learning probabilistic models for visual motion
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
POSECUT: simultaneous segmentation and 3D pose estimation of humans using dynamic graph-cuts
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Spatio-temporal extraction of articulated models in a graph pyramid
GbRPR'11 Proceedings of the 8th international conference on Graph-based representations in pattern recognition
Special Issue on Probabilistic Models for Image Understanding, Part II
International Journal of Computer Vision
J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Application of heterogenous motion models towards structure recovery from motion
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Hierarchical object discovery and dense modelling from motion cues in RGB-D video
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Humans demonstrate a remarkable ability to parse complicated motion sequences into their constituent structures and motions. We investigate this problem, attempting to learn the structure of one or more articulated objects, given a time series of two-dimensional feature positions. We model the observed sequence in terms of "stick figure" objects, under the assumption that the relative joint angles between sticks can change over time, but their lengths and connectivities are fixed. The problem is formulated as a single probabilistic model that includes multiple sub-components: associating the features with particular sticks, determining the proper number of sticks, and finding which sticks are physically joined. We test the algorithm on challenging datasets of 2D projections of optical human motion capture and feature trajectories from real videos.