Image analysis – Pattern recognition – Classification
Reexamination Certificate
2003-08-27
2010-02-02
Mehta, Bhavesh M (Department: 2624)
Image analysis
Pattern recognition
Classification
C704S245000
Reexamination Certificate
active
07657102
ABSTRACT:
A fast variational on-line learning technique for training a transformed hidden Markov model. A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, once the model has been initialized, an expectation-maximization (“EM”) algorithm is used to learn the one or more object class models, so that the video sequence has high marginal probability under the model. In the expectation step (the “E-Step”), the model parameters are assumed to be correct, and for an input image, probabilistic inference is used to fill in the values of the unobserved or hidden variables, e.g., the object class and appearance. In one embodiment of the invention, a Viterbi algorithm and a latent image is employed for this purpose. In the maximization step (the “M-Step”), the model parameters are adjusted using the values of the unobserved variables calculated in the previous E-step.
REFERENCES:
patent: 5487117 (1996-01-01), Burges et al.
patent: 5598507 (1997-01-01), Kimber et al.
patent: 5806030 (1998-09-01), Junqua
patent: 5892847 (1999-04-01), Johnson
patent: 5925065 (1999-07-01), Totakura et al.
patent: 6073096 (2000-06-01), Gao et al.
patent: 6404925 (2002-06-01), Foote et al.
patent: 6591146 (2003-07-01), Pavlovic
patent: 6668080 (2003-12-01), Torr et al.
patent: 7113185 (2006-09-01), Jojic et al.
patent: 7127127 (2006-10-01), Jojic et al.
Dellaert, The Expectation Maximization Algorithm, College of Computing, Georgia Institute of Technology, Technical Report No. GIT-GVU-02-20, Feb. 2002.
U.S. Appl. No. 10/294,211, filed Nov. 14, 2002, Jojic et al.
Bauer, E., D. Coller and Y. Singer, Update rules for parameter estimation in Bayesian networks,Proc. of the 13thUAI, Providence, Rhode Island, Aug. 1-3, 1997, pp. 3-13.
Black, M. J., and D. J. Fleet, Probabilistic detection and tracking of motion discontinuities,Int'l. J. on Comp. Vision, 2000.
Frey, B., and N. Jojic, Fast, large-scale transformation-invariant clustering,Advances in Neural Information Processing Systems, (NIPS 2001), 14, Cambridge, MA, MIT Press 2002.
Frey, B., and N. Jojic, Estimating mixture models of images and inferring spatial transformations using the EM algorithm,Comp. Vision and Pattern Recognition(CVPR), Fort Collins, Jun. 23-25, 1999, pp. 416-422.
Jepson, A., and M. J. Black, Mixture models for optical flow computation, Proc. of the IEEE Conf. on Comp. Vision and Pattern Recognition, Jun. 1993, pp. 760-761.
Jojic, N., and B. Frey, Learning flexible sprites in video layers,IEEE Conf. on Comp. Vision and Pattern Recognition(CVPR), 2001.
Jojic, N., N. Petrovic, B. Frey and T. Huang, Transformed hidden Markov models: Estimating mixture models and inferring spatial transformations in video sequences,IEEE Conf. on Comp. Vision and Pattern Recognition(CVPR), 2000.
Neal, R. M., and G. E. Hinton, A view of the EM algorithm that justifies incremental, sparse and other variants,Learning in Graphical Models, Kluwer Academic Publishers, Norwell MA, 1998, Ed. M. I. Jordan, pp. 355-368.
Tao, H., R. Kumar and H. S. Sawhney, Dynamic layer representation with applications to tracking,Proc. of the IEEE Conf. on Comp. Vision and Pattern Recognition, 2000.
Torr, P., R. Szeliski, and P. Anandan, An integrated Bayesian approach to layer extraction from image sequences,IEEE Trans. on Pattern Analysis and Mach. Intelligence, 2001, vol. 23, No. 3, pp. 297-303.
Wang, J. Y., and E. H. Adelson, Representing moving images with layers,IEEE Trans. on Image Processing, 1994, vol. 3, No. 5, pp. 625-638.
Wolf, J. K., A. M. Viterbi and G. S. Dixon, Finding the best set of K paths through a trellis with application to multitarget tracking,IEEE Trans. on Aerospace&Elect. Sys., Mar. 1989, vol. 25, No. 2, pp. 287-296.
Schödl, A., I. Essa, Controlled animation of video sprites, ACM, published Jul. 2002, pp. 121-127 and 196.
Kimbinh T. Nguyen, Office Action, U.S. Appl. No. 10/294,211, Jan. 12, 2005.
Kimbinh T. Nguyen, Notice of Allowance, U.S. Appl. No. 10/294,211, Feb. 17, 2006.
Jojic Nebojsa
Petrovic Nemanja
Lyon Katrina A.
Lyon & Harr LLP
Mehta Bhavesh M
Microsoft Corp.
Rashid David P
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