A robust video foreground segmentation by using generalized gaussian mixture modeling

Proceedings - Fourth Canadian Conference on Computer and Robot Vision, CRV 2007

Volumn , Issue , 2007, Pages 503-509

  Allili, Mohand Said ^a   Bouguila, Nizar ^b   Ziou, Djemel ^a  

^a UNIVERSITÉ DE SHERBROOKE   (Canada)

^b Dept of Electrical and Computer Engineering   (Canada)

Author keywords

Mixture of general gaussians (MoGG); MML; Video foreground segmentation

Indexed keywords

BAYESIAN NETWORKS; IMAGE SEGMENTATION; MATHEMATICAL MODELS;

GAUSSIAN MIXTURES; MIXTURE OF GENERAL GAUSSIANS (MOGG); VIDEO FOREGROUND SEGMENTATION;

VIDEO SIGNAL PROCESSING;

EID: 34548763871     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CRV.2007.7     Document Type: Conference Paper

References (22)

1. M.S. Allili, N. Bouguila and D. Ziou. Finite Generalized Gaussian Mixture Modeling and Applications to Segmentation and Tracking. Technical Report, September 2006.
2. M.S. Allili and D. Ziou. A Robust Video Object Tracking by Using Active Contours. In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition Workshop, p. 135, 2006.
3. M. Baccar, L.A. Gee and M.A. Abidi. Reliable Location and Regression Estimates with Application to Range Image Segmentation. J. of Mathematical Imaging and Vision, 11:195-205, 1999.
4. R.A. Baxter and J.J. Olivier. Finding Overlapping Components with MML. Statistics and Computing, 10(1):516, 2000.
5. N. Bouguila and D. Ziou. Unsupervised Selection of a Finite Dirichlet Mixture Model: An MML-Based Approach. IEEE Trans. on Knowledge and Data Engineering, 18(8):993-1009, 2006.
6. N. Bouguila and D. Ziou. Online Clustering via Finite Mixtures of Dirichlet and Minimum Message Length. Engineering Applications of Artificial Intelligence, 19(4):371-379, 2006.
7. G.E.P. Box and P.W. Tidwell. Transformation of Independent Variables. Technometrics, 4(4):531-550, 1962.
8. R. Collins, A. Lipton and T. Kanade. Introduction to the Special Section on Video Surveillance. IEEE Trans. on Pattern Analysis Machine Intelligence, 22(8):745-746, 2000.
9. J. Cheng, J. yang, Y. Zhou and Y. Cui. Flexible Background Mixture Models for Foreground Segmentation. Image and Vision Computing, 24(5):473-482, 2006.
10. M.N. Do and M. Vetterli. Wavelet-Based Texture Retrieval Using Generalized Gaussian Density and Kullback-Leibler Distance. IEEE Trans. on Image Processing, 11(2):146-158, 2002.
11. S.Indupalli, M.A.Ali, B.Boufama. A Novel Clustering-Based Method for Adaptive Background Segmentation. In Proc. of IEEE Canadian Conf. on Computer and Robot Vision, p. 37, 2006.
12. R.L. Joshi and T.R. Fischer. Comparison of Generalized Gaussian and Laplacian Modelling in DCT Image Coding. IEEE Signal Processing Letters, 2(5):81-82, 1995.
13. M. H. C. Law, M.A.T. Frigueiredo and A.K. Jain. Simultanuous Feature Selection and Clustering Using Mixture Models. IEEE Trans. on Pattern Analysis Machine Intelligence, 26(9):1154-1166, 2004.
14. G. McLachlan and D. Peel. Finite Mixture Models. Wiley Series in Probability and Statistics, 2000.
15. P. McLauchlan, D. Beymer, B. Coifman and J. Malik. A Real-time Computer Vision System for Measuring Traffic Parameters. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, p. 495, 1997.
16. A.E. Raftery and J.D. Banfield. Model-Based Gaussian and Non-Gaussian Clustering. Biometrics, 49:803-821, 1993.
17. J. Rittscher, J. Kato, S. Joga and A. Blake. A Probabilistic Background Model for Tracking. In Proc. of European Conf. on Computer Vision, LNCS 1843, 336-350, 2000.
18. K. Sharifi and A. Leon-Garcia. Estimation of Shape Parameter for Generalized Gaussian Distibution in Subband Decomposition of Video. IEEE Trans. on Circuits Systems and Video Technology, 5(1):52-56, 1995.
19. C. Stauffer and W.E.L. Grimson. Learning Patterns of Activity Using Real-Time Tracking. IEEE Trans. on Pattern Analysis Machine Intelligence, 22(8):747-757, 2000.
20. J. Sullivan, A. Blake and J. Rittscher. Statistical Foreground Modelling for Object Localisation. In Proc. of European Conf. on Computer Vision, LNCS 1843, 307-323, 2000.
21. D.M. Titterington. Recursive Parameter Estimation Using Incomplete Data. J. of Royal Statistical Society, 46(2):257-267, 1984.
22. J-F. Yao. On Recursive Estimation in Incomplete Data Models. Statistics, 34:27-51, 2000.