Twin gaussian processes for structured prediction

International Journal of Computer Vision

Volumn 87, Issue 1-2, 2010, Pages 28-52

  Bo, Liefeng ^a   Sminchisescu, Cristian ^b  

^a TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

^b UNIVERSITY OF BONN   (Germany)

Author keywords

3d human pose reconstruction; Feature extraction; Gaussian processes; Structured prediction; Video processing

Indexed keywords

3D HUMAN POSE RECONSTRUCTION; GAUSSIAN PROCESSES; HUMAN POSE; STRUCTURED PREDICTION; VIDEO PROCESSING;

CONCRETE DAMS; FEATURE EXTRACTION; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); NORMAL DISTRIBUTION; REPAIR; VIDEO RECORDING; VIDEO SIGNAL PROCESSING;

THREE DIMENSIONAL;

EID: 75149188375     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-008-0204-y     Document Type: Article

References (73)

1. Agarwal, A., & Triggs, B. (2006). Recovering 3d human pose from monocular images. IEEE transactions on pattern analysis and machine intelligence.
2. Bar-Shalom, Y., & Fortman, T. (1988). Tracking and data association. San Diego: Academic Press.
3. Battu, B., Krappers, A., & Koenderink, J. (2007). Ambiguity in pictorial depth. Perception 36.
4. Bishop, C., & Svensen, M. (2003). Bayesian mixtures of experts. In Uncertainty in artificial intelligence, 2003.
5. A. Blake B. North M. Isard 1999 Learning multi-class dynamics Advances in Neural Information Processing Systems 11 389 395
6. Bo, L., & Sminchisescu, C. (2008). Twin Gaussian processes for structured prediction. Snowbird Learning, April.
7. Bo, L., Sminchisescu, C., Kanaujia, A., & Metaxas, D. (2008). Fast algorithms for large scale conditional 3D prediction. In IEEE conference on computer vision and pattern recognition, 2008.
8. Brubaker, M., & Fleet, D. (2008). The kneed walker for human pose tracking. In IEEE international conference on computer vision and pattern recognition, 2008.
9. Choo, K., & Fleet, D. (2001). People tracking using hybrid Monte Carlo filtering. In IEEE international conference on computer vision, 2001.
10. CMU (2003). Human Motion DataBase. Online at http://mocap.cs.cmu.edu/ search.html.
11. Cortes, C., Mohri, M., & Weston, J. (2005). A general regression technique for learning transductions. In International conference on machine learning (pp. 153-160) 2005.
12. Cristianini, N., Shawe-Taylor, J., Elisseeff, A., & Kandola, J. S. (2001a). On kernel-target alignment. In Advances in neural information processing systems (pp. 367-373) 2001.
13. Cristianini, N., Shawe-Taylor, J., & Kandola, J. S. (2001b). Spectral kernel methods for clustering. In Advances in neural information processing systems (pp. 649-655) 2001.
14. Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE international conference on computer vision and pattern recognition, 2005.
15. Deutscher, J., Blake, A., & Reid, I. (2000). Articulated body motion capture by annealed particle filtering. In IEEE international conference on computer vision and pattern recognition, 2000.
16. Deutscher, J., Davidson, A., & Reid, I. (2001). Articulated partitioning of high dimensional search spaces associated with articulated body motion capture. In IEEE international conference on computer vision and pattern recognition, 2001.
17. S. Duane A. D. Kennedy B. J. Pendleton D. Roweth 1987 Hybrid Monte Carlo Physics Letters B 195 2 216 222 10.1016/0370-2693(87)91197-X
18. Elgammal, A., & Lee, C. (2004). Inferring 3d body pose from silhouettes using activity manifold learning. In IEEE international conference on computer vision and pattern recognition, 2004.
19. Geurts, P., Wehenkel, L., & d'Alché Buc, F. (2006). Kernelizing the output of tree-based methods. In International conference on machine learning (pp. 345-352) 2006.
20. Geurts, P., Wehenkel, L., & d'Alché Buc, F. (2007). Gradient boosting for kernelized output spaces. In International conference on machine learning, New York, NY, USA (pp. 289-296) 2007.
21. Gretton, A., Bousquet, O., Smola, A. J., & Schölkopf, B. (2005a). Measuring statistical dependence with Hilbert-Schmidt norms. In S. Jain & W.-S. Lee (Eds.), Proceedings algorithmic learning theory, 2005.
22. A. Gretton R. Herbrich A. J. Smola O. Bousquet B. Schölkopf 2005 Kernel methods for measuring independence Journal of Machine Learning Research 6 2075 2129
23. Guzman, A. N., & Holden, S. (2007). Twinned Gaussian processes. In Advances in neural information processing systems, December 2007.
24. Hinton, G. E., & Roweis, S. T. (2002). Stochastic neighbor embedding. In Advances in neural information processing systems (pp. 833-840) 2002.
25. Isard, M., & Blake, A. (1998). CONDENSATION-conditional density propagation for visual tracking. International Journal of Computer Vision.
26. Kanaujia, A., Sminchisescu, C., & Metaxas, D. (2006). Semi-supervised hierarchical models for 3D human pose reconstruction. In IEEE international conference on computer vision and pattern recognition, 2006.
27. Kanaujia, A., Sminchisescu, C., & Metaxas, D. (2007). Spectral latent variable models for perceptual inference. In IEEE International Conference on Computer Vision, Vol. 1, 2007.
28. Kehl, R., Bray, M., & Gool, L. V. (2005). Full body tracking from multiple views using stochastic sampling. In IEEE international conference on computer vision and pattern recognition, 2005.
29. Koenderink, J. (1998). Pictorial relief. Philosophical Transactions Royal Society London A 356.
30. Koenderink, J., & van Doorn, A. (1979). The internal representation of solid shape with respect to vision. Biological Cybernetics 32(3).
31. Krauthgamer, R., & Lee, J. R. (2004). Navigating nets: simple algorithms for proximity search. In SODA '04: Proceedings of the fifteenth annual ACM-SIAM symposium on discrete algorithms (pp. 798-807) 2004.
32. Lafferty, J., McCallum, A., & Pereira, F. (2001). Conditional random fields: probabilistic models for segmenting and labeling sequence data. In International conference on machine learning, 2001.
33. N. Lawrence 2005 Probabilistic non-linear component analysis with Gaussian process latent variable models Journal of Machine Learning Research 6 1783 1816 2249872
34. H. J. Lee Z. Chen 1985 Determination of 3D human body postures from a single view Computer Vision, Graphics and Image Processing 30 148 168 10.1016/0734-189X(85)90094-5 813920
35. Li, R., Yang, M., Sclaroff, S., & Tian, T. (2006). Monocular tracking of 3D human motion with a coordinated mixture of factor analyzers. In European conference on computer vision, 2006.
36. Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2).
37. Memisevic, R. (2006). Kernel information embeddings. In International conference on machine learning, 2006.
38. Morris, D., & Rehg, J. (1998). Singularity analysis for articulated object tracking. In IEEE international conference on computer vision and pattern recognition (pp. 289-296) 1998.
39. Neal, R. (1998). Annealed importance sampling (Technical Report 9805). Department of Statistics, University of Toronto.
40. Poppe, R. (2007). Evaluating example-based human pose estimation: Experiments on HumanEva sets. In HumanEva Workshop CVPR, 2007.
41. Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. Adaptive computation and machine learning. Cambridge: MIT Press.
42. Rosales, R., & Sclaroff, S. (2002). Learning body pose via specialized maps. In Advances in neural information processing systems, 2002.
43. Roth, S., Sigal, L., & Black, M. (2004). Gibbs likelihoods for Bayesian tracking. In IEEE international conference on computer vision and pattern recognition, 2004.
44. S. Schaal C. Atkeson A. Moore 1997 Locally weighted learning Artificial Intelligence Review 11 11 73 10.1023/A:1006559212014
45. T. Serre L. Wolf S. Bileschi M. Riesenhuber 2007 Robust object recognition with cortex-like mechanisms IEEE Transactions on Pattern Analysis and Machine Intelligence 29 3 411 426 10.1109/TPAMI.2007.56 (Pubitemid 46336417)
46. Shakhnarovich, G., Viola, P., & Darrell, T. (2003). Fast pose estimation with parameter sensitive hashing. In IEEE international conference on computer vision, 2003.
47. Sidenbladh, H., & Black, M. (2001). Learning image statistics for Bayesian tracking. In IEEE international conference on computer vision, 2001.
48. Sidenbladh, H., Black, M., & Fleet, D. (2000). Stochastic tracking of 3D human figures using 2D image motion. In European conference on computer vision, 2000.
49. Sidenbladh, H., Black, M., & Sigal, L. (2002). Implicit probabilistic models of human motion for synthesis and tracking. In European conference on computer vision, 2002.
50. Sigal, L., & Black, M. (2006). HumanEva: synchronized video and motion capture dataset for evaluation of articulated human motion (Technical Report CS-06-08). Brown University.
51. Sigal, L., Balan, A., & Black, M. J. (2007). Combined discriminative and generative articulated pose and non-rigid shape estimation. In Advances in neural information processing systems, 2007.
52. Sminchisescu, C. (2002). Consistency and coupling in human model likelihoods. In IEEE international conference on automatic face and gesture recognition (pp. 27-32). Washington, DC, 2002.
53. Sminchisescu, C., & Jepson, A. (2004a). Generative modeling for continuous non-linearly embedded visual inference. In International conference on machine learning (pp. 759-766). Banff, 2004.
54. Sminchisescu, C., & Jepson, A. (2004b). Variational mixture smoothing for non-linear dynamical systems. In IEEE international conference on computer vision and pattern recognition (Vol. 2, pp. 608-615). Washington, DC, 2004.
55. Sminchisescu, C., & Telea, A. (2002). Human pose estimation from silhouettes. A consistent approach using distance level sets. In WSCG international conference for computer graphics, visualization and computer vision, Czech Republic, 2002.
56. Sminchisescu, C., & Triggs, B. (2001). Covariance-scaled sampling for monocular 3D body tracking. In IEEE international conference on computer vision and pattern recognition (Vol. 1, pp. 447-454). Hawaii, 2001.
57. Sminchisescu, C., & Triggs, B. (2002a). Building roadmaps of local minima of visual models. In European conference on computer vision (Vol. 1, pp. 566-582). Copenhagen, 2002.
58. Sminchisescu, C., & Triggs, B. (2002b). Hyperdynamics importance sampling. In European conference on computer vision (Vol. 1, pp. 769-783). Copenhagen, 2002.
59. Sminchisescu, C., & Triggs, B. (2003). Kinematic jump processes for monocular 3D human tracking. In IEEE international conference on computer vision and pattern recognition (Vol. 1, pp. 69-76). Madison, 2003.
60. Sminchisescu, C., & Triggs, B. (2005). Mapping minima and transitions in visual models. International Journal of Computer Vision 61(1).
61. Sminchisescu, C., & Welling, M. (2007). Generalized darting Monte-Carlo. In Artificial Intelligence and Statistics, Vol. 1, 2007.
62. Sminchisescu, C., Kanaujia, A., Li, Z., & Metaxas, D. (2005). Conditional visual tracking in kernel space. In Advances in neural information processing systems, 2005.
63. Sminchisescu, C., Kanaujia, A., & Metaxas, D. (2006). Learning joint top-down and bottom-up processes for 3D visual inference. In IEEE international conference on computer vision and pattern recognition, 2006.
64. 3 E: discriminative density propagation for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence.
65. Smola, A. J., & Schölkopf, B. (2000). Sparse greedy matrix approximation for machine learning. In International conference on machine learning (pp. 911-918) 2000.
66. Taskar, B., Guestrin, C., & Koller, D. (2004). Max-margin Markov networks. In Advances in neural information processing systems, 2004.
67. Tresp, V. (2000). Mixtures of Gaussian processes. In Advances in neural information processing systems, 2000.
68. Tsochantaridis, I., Hofmann, T., Joachims, T., & Altun, Y. (2004). Support vector machine learning for interdependent and structured output spaces. In International conference on machine learning, 2004.
69. Urtasun, R., Fleet, D., Hertzmann, A., & Fua, P. (2005). Priors for people tracking in small training sets. In IEEE international conference on computer vision, 2005.
70. P. Vincent Y. Bengio 2002 Kernel matching pursuit Machine Learning 48 165 187 0998.68120 10.1023/A:1013955821559 (Pubitemid 34247577)
71. Vondrak, M., Sigal, L., & Jenkins, O. C. (2008). Physical simulation for probabilistic motion tracking. In IEEE international conference on computer vision and pattern recognition, 2008.
72. Wang, J., Fleet, D. J., & Hertzmann, A. (2008). Gaussian process dynamical models. In IEEE transactions on pattern analysis and machine intelligence, 2008.
73. Weston, J., Chapelle, O., Elisseeff, A., Scholkopf, B., & Vapnik, V. (2002). Kernel dependency estimation. In Advances in neural information processing systems, 2002.