Locally time-invariant models of human activities using trajectories on the grassmannian
暂无分享,去创建一个
[1] Junji Yamato,et al. Recognizing human action in time-sequential images using hidden Markov model , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[2] Alex Pentland,et al. Real-time American Sign Language recognition from video using hidden Markov models , 1995 .
[3] Lawton Hubert Lee,et al. Identification and Robust Control of Linear Parameter-Varying Systems , 1997 .
[4] Bart De Moor,et al. Subspace algorithms for the stochastic identification problem, , 1993, Autom..
[5] Pietro Perona,et al. Human action recognition by sequence of movelet codewords , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.
[6] Y. Chikuse. Statistics on special manifolds , 2003 .
[7] Georgios B. Giannakis,et al. Subspace methods for blind estimation of time-varying FIR channels , 1997, IEEE Trans. Signal Process..
[8] Yang Wang,et al. Unsupervised Discovery of Action Classes , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[9] Jianbo Shi,et al. Detecting unusual activity in video , 2004, CVPR 2004.
[10] Trevor Darrell,et al. Latent-Dynamic Discriminative Models for Continuous Gesture Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[11] James M. Rehg,et al. Learning and Inferring Motion Patterns using Parametric Segmental Switching Linear Dynamic Systems , 2008, International Journal of Computer Vision.
[12] P. Absil,et al. Riemannian Geometry of Grassmann Manifolds with a View on Algorithmic Computation , 2004 .
[13] Alan Edelman,et al. The Geometry of Algorithms with Orthogonality Constraints , 1998, SIAM J. Matrix Anal. Appl..
[14] Payam Saisan,et al. Dynamic texture recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[15] Cristian Sminchisescu,et al. Conditional Random Fields for Contextual Human Motion Recognition , 2005, ICCV.
[16] Rama Chellappa,et al. Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Vladimir Pavlovic,et al. Impact of dynamic model learning on classification of human motion , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).
[18] E. Klassen. Bayesian, Geometric Subspace Tracking , 2002 .
[19] Lennart Ljung,et al. System Identification: Theory for the User , 1987 .
[20] Michel Verhaegen,et al. Subspace identification of multivariable linear parameter-varying systems , 2002, Autom..
[21] Ashok Veeraraghavan,et al. The Function Space of an Activity , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[22] A. Willsky,et al. Time-varying parametric modeling of speech☆ , 1983 .
[23] T. Claasen,et al. On stationary linear time-varying systems , 1982 .
[24] T. Rao. The Fitting of Non-stationary Time-series Models with Time-dependent Parameters , 1970 .
[25] Michael Isard,et al. Learning and Classification of Complex Dynamics , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[26] Dimitris N. Metaxas,et al. ASL recognition based on a coupling between HMMs and 3D motion analysis , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).
[27] Mario Sznaier,et al. A model (in)validation approach to gait classification , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.