Learning articulated structure and motion

International Journal of Computer Vision

Volumn 88, Issue 2, 2010, Pages 214-237

  Ross, David A ^a   Tarlow, Daniel ^a   Zemel, Richard S ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

Graphical models; Non rigid motion; Structure from motion

Indexed keywords

2D PROJECTIONS; ARTICULATED OBJECT; ARTICULATED STRUCTURES; DATA SETS; GRAPHICAL MODEL; HUMAN MOTION CAPTURE; JOINT ANGLE; MOTION SEQUENCES; NON-RIGID MOTION; PROBABILISTIC MODELS; SUB-COMPONENTS; TWO-DIMENSIONAL FEATURES;

ALGORITHMS; GRAPHIC METHODS; TIME SERIES;

RIGID STRUCTURES;

EID: 77951294397     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-010-0325-y     Document Type: Article

References (42)

1. Abdel-Malek, K., Arora, J., Beck, S., Bhatti, M., Carroll, J., Cook, T., Dasgupta, S., Grosland, N., Han, R., Kim, H., Lu, J., Swan, C., Williams, A., & Yang, J. Digital human modeling and virtual reality for FCS (Technical Report VSR-04.02). The Virtual Soldier Research (VSR) Program, Center for Computer-Aided Design, College of Engineering, The University of Iowa, October 2004.
2. Bray, M., Kohli, P., & Torr, P. (2006). Posecut: Simultaneous segmentation and 3d pose estimation of humans using dynamic graph-cuts. In ECCV (2), pp. 642-655.
3. Costeira, J., & Kanade, T. (1996). A multi-body factorization method for motion analysis. In Image understanding workshop (pp. 1013-1026).
4. J. P. Costeira T. Kanade 1998 A multibody factorization method for independently moving-objects International Journal of Computer Vision 29 3 159 179 10.1023/A:1008000628999
5. Cover, T.M., & Thomas, J.A. (1991). Elements of information theory. New York: Wiley-Interscience.
6. Culverhouse, P. F., & Wang, H. (2003). Robust motion segmentation by spectral clustering. In British machine vision conference (pp. 639-648).
7. A. P. Dempster N. M. Laird D. B. Rubin 1977 Maximum likelihood from incomplete data via the em algorithm Journal of the Royal Statistical Society 39 1 38 0364.62022 501537
8. B. J. Frey D. Dueck 2007 Clustering by passing messages between data points Science 315 972 976 10.1126/science.1136800 2292174 (Pubitemid 46281181)
9. C. W. Gear 1998 Multibody grouping from motion images International Journal of Computer Vision 29 2 133 150 10.1023/A:1008026310903 10.1023/A:1008026310903 ISSN 0920-5691
10. Ghahramani, Z., & Hinton, G. E. (1996a). The EM algorithm for mixtures of factor analyzers (Technical Report CRG-TR-96-1). University of Toronto.
11. Ghahramani, Z., & Hinton, G. E. (1996b). Parameter estimation for linear dynamical systems (Technical Report CRG-TR-96-2). University of Toronto.
12. Golub, G. H., & Van Loan, C. F. (1996). Matrix computations. Baltimore: Johns Hopkins Press.
13. Gruber, A., & Weiss, Y. (2003). Factorization with uncertainty and missing data: Exploiting temporal coherence. In Thrun, S., Saul, L. K., & Schölkopf, B. (Eds.) Advances in Neural Information Processing Systems. Cambridge: MIT Press. ISBN0-262-20152-6.
14. Gruber, A., & Weiss, Y. (2004). Multibody factorization with uncertainty and missing data using the EM algorithm. In Proceedings of IEEE conference on computer vision and pattern recognition (pp. 707-714).
15. Hartley, R., & Zisserman, A. (2003). Multiple view geometry. Cambridge: Cambridge University Press.
16. L. Herda P. Fua R. Plankers R. Boulic D. Thalmann 2001 Using skeleton-based tracking to increase the reliability of optical motion capture Human Movement Science Journal 20 3 313 341 10.1016/S0167-9457(01)00050-1 (Pubitemid 33619171)
17. G. Johansson 1973 Visual perception of biological motion and a model for its analysis Perception and Psychophysics 14 201 211
18. Kirk, A. G., O'Brien, J. F., & Forsyth, D. A. (2005). Skeletal parameter estimation from optical motion capture data. In Proceedings of IEEE conference on computer vision and pattern recognition. Los Alamitos: IEEE Comput. Soc. ISBN 0-7695-2372-2.
19. Neal, R., & Hinton, G. (1998). A view of the em algorithm that justifies incremental, sparse, and other variants. In Jordan, M. I. (Ed.) Learning in graphical models. Norwell: Kluwer Academic.
20. Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On spectral clustering: analysis and an algorithm. In Advances in neural information processing systems (NIPS).
21. Ross, D. A. (2008a). Learning probabilistic models for visual motion (PhD thesis). University of Toronto, Ontario, Canada.
22. Ross, D. A. (2008b). Learning probabilistic models for visual motion (PhD thesis). University of Toronto, Toronto, Ontario, Canada.
23. D. A. Ross R. S. Zemel 2006 Learning parts-based representations of data Journal of Machine Learning Research 7 2369 2397 2274443
24. Ross, D. A., Tarlow, D., & Zemel, R. S. (2007). Learning articulated skeletons from motion. In Workshop on dynamical vision at ICCV.
25. Ross, D. A., Tarlow, D., & Zemel, R. S. (2008). Unsupervised learning of skeletons from motion. In Forsyth, D., Torr, P., & Zisserman, A. (Eds.) Proceedings of the 10th European conference on computer vision (ECCV 2008). Berlin: Springer.
26. J. Shi J. Malik 2000 Normalized cuts and image segmentation IEEE Transactions on Pattern Analysis and Machine Intelligence 22 8 888 905 10.1109/34.868688
27. Shi, J., & Tomasi, C. (1994). Good features to track. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), (pp. 593-600).
28. Silaghi, M. C., Plankers, R., Boulic, R., Fua, P., & Thalmann, D. (1998). Local and global skeleton fitting techniques for optical motion capture, modeling and motion capture techniques for virtual environments. In Lecture notes in artificial intelligence (pp. 26-40). Berlin: Springer.
29. C. Sminchisescu B. Triggs 2003 Estimating articulated human motion with covariance scaled sampling International Journal of Robotics Research 22 6 371 393 10.1177/0278364903022006003
30. Y. Song L. Goncalves P. Perona 2003 Unsupervised learning of human motion IEEE Transactions on Pattern Analysis and Machine Intelligence 25 7 814 827 10.1109/TPAMI.2003.1206511
31. Song, Y., Goncalves, L., & Perona, P. (2001). Learning probabilistic structure for human motion detection. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR) (pp. 771-777). Los Alamitos: IEEE Comput. Soc. ISBN 0-7695-1272-0.
32. Taycher, L., Fisher III, J. W., & Darrell, T. (2002). Recovering articulated model topology from observed rigid motion. In Becker, S., Thrun, S., & Obermayer, K. (Eds.) Advances in neural information processing systems (NIPS) (pp. 1311-1318). Cambridge: MIT Press.
33. C. Tomasi T. Kanade 1992 Shape and motion from image streams under orthography: a factorization method International Journal of Computer Vision 9 137 154 10.1007/BF00129684
34. (Pubitemid 43897482)
35. P. Tresadern I. Reid 2005 Articulated structure from motion by factorization CVPR '05: proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) 2 IEEE Comput. Soc. Washington 1110 1115 10.1109/CVPR.2005.75 10.1109/CVPR.2005.75 0-7695-2372-2 Tresadern, P., & Reid, I. (2005). Articulated structure from motion by factorization. In CVPR '05: proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR'05) (Vol. 2, pp. 1110-1115). Washington: IEEE Comput. Soc. doi: 10.1109/CVPR.2005.75. ISBN 0-7695-2372-2. (Pubitemid 43897482)
36. Viklands, T. (2006). Algorithms for the weighted orthogonal Procrustes problem and other least squares problems (PhD thesis). Umeå University, Umeå, Sweden.
37. Weiss, Y. (1999). Segmentation using eigenvectors: a unifying view. In Proceedings of the international conference on computer vision (ICCV).
38. Yan, J., & Pollefeys, M. (2005a). Factorization-based approach to articulated motion recovery. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR).
39. Yan, J., & Pollefeys, M. (2005b). Articulated motion segmentation using ransac with priors. In Workshop on dynamical vision (ICCV).
40. Yan, J., & Pollefeys, M. (2006a). A general framework for motion segmentation: Independent, articulated, rigid, non-rigid, degenerate and non-degenerate. In Proceedings computer vision-ECCV 2006, 9th European conference on computer vision, Part III, Graz, Austria, May 7-13.
41. Yan, J., & Pollefeys, M. (2006b). Automatic kinematic chain building from feature trajectories of articulated objects. In Proceedings of IEEE conference on computer vision and pattern recognition (CVPR).
42. J. Yan M. Pollefeys 2008 A factorization-based approach for articulated nonrigid shape, motion and kinematic chain recovery from video IEEE Transactions on Pattern Analysis and Machine Intelligence 30 5 865 877 10.1109/TPAMI.2007. 70739 ISSN 0162-8828. http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007. 70739 (Pubitemid 351486754)