References

1

Y. Raja, S. McKenna, and S. Gong, ``Segmentation and tracking using colour mixture models,'' in Asian Conference on Computer Vision, Hong Kong, January 1998.

2

S. McKenna, Y. Raja, and S. Gong, ``Object tracking using adaptive colour mixture models,'' in Advances in Color Machine Vision, ACCV Spec. Sess., Hong Kong, January 1998.

3

C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.

4

S. McKenna, S. Gong, and Y. Raja, ``Face recognition in dynamic scenes,'' in BMVC, 1997.

5

B. S. Everitt and D. J. Hand, Finite Mixture Distributions, Chapman and Hall, New York, 1981.

6

G. J. McLachlan and K. E. Basford, Mixture Models: Inference and Applications to Clustering, Marcel Dekker Inc., New York, 1988.

7

C. E. Priebe, ``Adaptive mixtures,'' J. Amer. Stat. Assoc., vol. 89, no. 427, pp. 796-806, 1994.

8

C. E. Priebe and D. J. Marchette, ``Adaptive mixtures: Recursive nonparametric pattern recognition,'' Pattern Recognition, vol. 24, no. 12, pp. 1197-1209, 1991.

9

C. E. Priebe and D. J. Marchette, ``Adaptive mixture density estimation,'' Pattern Recognition, vol. 26, no. 5, pp. 771-785, 1993.

10

D. M. Titterington, A. F. M. Smith, and U. E. Makov, Statistical Analysis of Finite Mixture Distributions, John Wiley, New York, 1985.

11

R. A. Redner and H. F. Walker, ``Mixture densities, maximum likelihood and the EM algorithm,'' SIAM Review, vol. 26, no. 2, pp. 195-239, 1984.

12

Y. Raja, S. McKenna, and S. Gong, ``Colour model selection and adaptation in dynamic scenes,'' in European Conference on Computer Vision, Germany, July 1998.