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28 Nov 2006: Session 3: Grouping and Matching. Thursday 24 July, 09.30-12.30 (Chair: Dr A. ... Session 4: Geometry and Statistics. Thursday 24 July, 14.00-17.40 (Chair: Dr R.

21 Mar 2006: Due to the smoothness of faces, each track corre-sponds to an appearance manifold [2, 22, 24], as illustratedin Fig. ... 24] B. Moghaddam and A. Pentland. Principal manifolds and probabilis-tic subspaces for visual recognition.PAMI, 24(6), 2002.2, 3.

22 Nov 2006: Km(Oi, Oj ; λ(1)) (24)where Kl(Oi, Oj ; λ) is the dot-product of the log-likelihoodratios and Kc(Oi, Oj ; λ(1)), Km(Oi, Oj ; λ(1)), etc., ... than in the ML case. This is because the MMI base-line performs much better than the ML baseline

14 Mar 2006: 18] N. Vlassis and A Likas. A kurtosis-based dynamic approach to Gaussian mixture modeling.Systems, Max, and Cybernetics – Part A: Systems and Humans, 24(9):393–399, 1999.

25 May 2006: Thus with no loss of accuracy Equation 9 isrewritten in the form of Equation 24.

14 Sep 2006: 24] andfor exemplar templates by Toyama and Blake [43].However, it is acknowledged that “one problem withexemplar sets is that they can grow exponentiallywith object complexity. ... Wetake inspiration from Jojic et al. [24] who modelleda video sequence

21 Sep 2006: 24] and for exem-plar templates by Toyama and Blake [43]. However, it is acknowledged that“one problem withexemplar sets is that they can grow exponentially with object complexity. ... We take inspiration from Jojicet al.[24] whomodeled a video

22 Nov 2006: This sensitivity to outliers is a well known feature of the MMI criterion [24]. ... j))}. (24). Each Gaussian component is assumed to be independent of all others.

17 Feb 2006: 24]). Briefly, we estimate multivariate Gaussian components using the ExpectationMaximization (EM) algorithm [14], initialized by k-means clustering. ... appearance. IJCV, 14:5–24, 1995.34. S. Palanivel, B. S. Venkatesh, and B Yegnanarayana.

19 Jul 2006: 5. 10. 108. 64. 20. 24. 68. 10. 0. 0.05. 0.1.