A Bayesian non-Gaussian mixture analysis: Application to eye modeling

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2007, Pages

  Bouguila, Nizar ^a   Ziou, Djemel ^b   Hammoud, Riad I ^c  

^a Concordia University ^*  (Canada)

^b UNIVERSITÉ DE SHERBROOKE   (Canada)

^c DELPHI CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; GAUSSIAN DISTRIBUTION; KNOWLEDGE ACQUISITION; MATHEMATICAL MODELS;

DIRICHLET MIXTURE ESTIMATION; EYE MODELING; GAUSSIAN MIXTURE MODELS; GIBBS SAMPLING;

COMPUTER VISION;

EID: 35148862167     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2007.383439     Document Type: Conference Paper

References (37)

1. A. Haro, M. Flickner, and I. Essa. Detecting and Tracking Eyes by using their Physiological Properties. In IEEE CVPR, pages 163-168, 2000. 5
2. A. K. Jain and A. Vailaya. Image retrieval using color and shape. Pattern Recognition, 29(8): 1233-1244, 1996. 5
3. A. N. Srivastava. Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data. In Proc. SIAM International Conference on Data Mining (SDM), 2004. 7
4. A. N. Srivastava and J. Schumann, and B. Fischer. An Ensemble Approach to Building Mercer Kernels with Prior Information. In Proc. IEEE International Conference on Systems, Man and Cybernetics (SMC), pages 2352-2359, 2005. 7
5. B.D. Ripley. Pattern Recognition and Neural Networks. Cambridge University Press, 1996. 6
6. C.P. Robert. The Bayesian Choice. Springer-Verlag, 2001. 2, 3
7. C.P. Robert and G. Casella. Monte Carlo Statistical Methods. Springer-Verlag, 1999. 2, 3, 4
8. D. W. Hansen and R. I. Hammoud. Boosting particle filter-based eye tracker performance through adapted likelihood function to reflexions and light changes. In IEEE Conference on Advanced Video and Signal Based Surveillance, pages 111-116, 2005. 5
9. G. Celeux, M. Hurn and C. P. Robert. Computational and Inferential Difficulties With Mixture Posterior Distributions. Journal, of the Am. Stat. Association, 95:957-970, 2000. 4
10. G. D. Kleiter. Bayesian Diagnosis in Expert Systems. Artificial Intelligence, 54(1-2):1-32, 1992. 3
11. G.J. McLachlan and D. Peel. Finite Mixture Models. New York: Wiley, 2000. 1
12. G.J. McLachlan and T. Krishnan. The EM Algorithm and Extensions. New York: Wiley, 1997. 2
13. H. Bensmail, G. Celeux, A. Raftery, C. P. Robert. Inference in Model-Based Cluster Analysis. Statistics and Computing, 7:1-10, 1997. 3
14. J. Canny. A Computational Approach to Edge Detection. IEEE Transactions on PAMI, 8:679-698, 1986. 5
15. J.M. Marin, K. Mengersen and C.P. Robert. Bayesian modeling and inference on mixtures of distributions. In D. Dey and C. Rao, editors, Handbook of Statistics 25. Elsevier-Sciences, 2004. 2
16. K. Grauman, M. Betke, J. Gips, and G. R. Bradski. Communication Via Eye Blinks: Detection and Duration Analysis in Real Time. In IEEE CVPR, pages 1010-1017, 2001. 5
17. M. Stephens. Bayesian Analysis of mixture Models with an Unknown Number of Components: An Alternative to reversible Jump Methods. Annals of Statistics, 28:40-74, 2000. 4
18. M. Unser. Sum and Difference Histograms for Texture Classification. IEEE Transactions on RAMI, 8(1):118-125, 1986. 5
19. N. Bouguila and D. Ziou. MML-Based Approach for High-Dimensional Learning using the Generalized Dirichlet Mixture. In Proc. of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03, page 53, 2005. 1
20. N. Bouguila and D. Ziou. A Hybrid SEM Algorithm for High-Dimensional Unsupervised Learning Using a Finite Generalized Dirichlet Mixture. IEEE Transactions on Image Processing, 15(9):2657-2668, 2006. 1
21. N. Bouguila and D. Ziou. Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach. IEEE Transactions on Knowledge and Data Engineering, 18(8):993-1009, 2006. 1
22. N. Bouguila and D. Ziou. A Powerful Finite Mixture Model Based on the Generalized Dirichlet Distribution: Unsuper-vised Learning and Applications. In Proceedings of the 17th IEEE International Conference on Pattern Recognition, ICPR2004, pages 280-283, Cambridge, United Kingdom, Aout 2004. 1
23. N. Bouguila and D. Ziou. Dirichlet-Based Probability Model Applied to Human Skin Detection. In IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP2004, pages 521-524, Montreal, Canada, May 2004. 1
24. N. Bouguila, D. Ziou and E. Monga. Practical Bayesian Estimation of a Finite Beta Mixture Through Gibbs Sampling and its Applications. Statistics and Computing, 16(2):215-225, 2006. 2
25. N. Bouguila, D. Ziou and J. Vaillancourt. Novel Mixtures Based on the Dirichlet Distribution: Application to Data and Image Classification. In P. Perner and A. Rosenfeld, editors, Machine Learning and Data Mining in Pattern Recognition, (MLDM2003), pages 172-181, Leipzig, Germany, 2003. Springer, LNAI2734. 1
26. N. Bouguila, D. Ziou and J. Vaillancourt. Unsupervised Learning of a Finite Mixture Model Based on the Dirichlet Distribution and its Application. IEEE Transactions on Image Processing, 13(11):1533-1543, 2004. 1.
27. N. Edenborough, R. I. Hammoud, A. Harbach, and al. Drowsy Driver Monitor from Delphi. In Demo Session, IEEE CVPR, 2004. 5
28. O. Capp, C. P. Robert and T. Rydn. Reversible Jump MCMC Converging to Birth-and-Death. MCMC and More General Continuous Time Samplers. Journal of the Royal Statistical Society, B, 65:679-700, 2002. 4
29. R. E. Kass and A. E. Raftery. Bayes Factors. Journal of the Am. Stat. Association, 90:773-795, 1995. 4
30. R. I. Hammoud. A Robust Eye Position Tracker Based on Invariant Local Features, Eye Motion, and Infrared-Eye Responses. In SPIE Automatic Target Recognition XV, Volume 5807, pages 35-43, 2005. 5
31. R. I. Hammoud and D. W Hansen. Biophysics of the eye in computer vision: methods and advanced technologies. In F. Sadjadi and B. Javidi, editors, Physics of Automatic. Target Recognition, Series: Advanced Sciences and Technologies for Security Applications (Optics & Lazers), Vol 3, 2007. 5
32. R. I. Hammoud and R. Mohr. Mixture Densities for Video Objects Recognition. In IEEE International Conference on Pattern Recognition (ICPR), pages 71-75, Barcelona, Spain, 2000. 1
33. R. M. Haralick, K. Shanmugan and I. Dinstein. Texture Features for Image Classification. IEEE Transactions on Systems, Man and Cybernetics, 8:610-621, 1973. 5
34. S. M. Lewis and A. E. Raftery. Estimating Bayes Factors via Posterior Simulation With the Laplace-Metropolis Estimator. Journal of the Am. Stat. Association, 92:648-655, 1997. 2, 5
35. S. Richardson and P. J. Green. On Bayesian Analysis of Mixtures with an Unknown Number of Components (With Discussion). Journal of the Royal Statistical Society, B, 59:731-792, 1997. 4
36. T. Randen, and J. H. Husoy. Filtering for Texture Classication: A Comparative Study. IEEE Transactions on PAMI, 21(4):291-310, 1999. 5
37. Y. Tian, T. Kanade and J.F. Cohn. Dual-State Parametric Eye Tracking. In Proc. IEEE International Conference on Automatic Face and Gesture Recognition (FGR), pages 110-115, 2000. 7