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14 Sep 2006: 24, 18]. Both systems group an image’sspatial features, performing a global annealing optimizationthat propagates the certainty at distinct person-boundaries touncertain areas where those people’s outlines are ambigu-ous. ... 24] P. Tu and J.

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.

22 Nov 2006: Cambridge UniversityEngineering Department. University of East Anglia Seminar 24. Modelling Dependencies in Sequence Classification: Augmented Statistical Models. ... Comp.EER (%). GMM A-GMM. 128 12.17 8.62256 11.24 7.88512 11.13 7.481024 10.43† 7.31.

22 Nov 2006: HMM ML – 29.4 27.3C-Aug ML CML 24.2 –. HMM MMI – 25.3 24.8C-Aug MMI CML 23.4 –. Table 2. Classification error on the TIMIT core test ... A point of particular interest is that despite poorer statesegmentation—the sufficient statistics fix the

29 Apr 2006: average 92.0 64.1 58.3 17.0std 7.8 9.2 24.3 8.8. video sequences of the person in arbitrary motion (signif-icant translation, yaw and pitch, ... cluded and expression variant faces from a single sample per class.PAMI, 24(6), 2002.

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

21 Sep 2006: 21] for upper bodypose estimation. In [24] it is suggested to partition the parameter spaceof a 3D hand model using a multi-resolution grid. ... range. At detection rates of 0.99 the false positiverate for the centre template is 0.24, wheras it is

5 Jul 2006: Cambridge UniversityEngineering Department. Trajectory Models For Speech Processing Workshop 24. Augmented Statistical Models for Speech Recognition.

15 Feb 2006: For cases like these, the algorithm of [24] couldbe used to refine the class labeling. ... In: AAAI.(2005) 1508–1513. 24. Kumar, S., Hebert, M.: A hierarchical field framework for unified context-basedclassification.

5 Jul 2006: Complementary System Selection (“Random”). • Variability to systems can be obtained by varying for example:– segmentation and clustering [3]– acoustic model decision tree [24]– acoustic model context (tri/quin-phone) [4]– ... Cambridge

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: In addition to training GI models for BN systems theuse of Gender Dependent (GD) models has been found tobe advantageous [24]. ... 0.16 0.18 0.2 0.22 0.24 0.2612. 14. 16. 18. 20. 22.

19 Mar 2006: 24] P. S. Penev. Dimensionality reduction by sparsification in a local-features representation of human faces. ... Ross and A. Jain. Information fusion in biometrics.PatternRecognition Letters, 24(13):2115–2125, 2003.

22 Nov 2006: ãtj = max{atj , amin} (24). whereãtj is the floored scale factor andamin is the scale floor.In this paper,amin of 0.1 was used.

19 Sep 2006: Cambridge University Press, 1999. [FB81] FISCHLER M., BOLLES R.: Random sample consensus:A paradigm for model-fitting with applications to image analysisand automated cartography.CACM 24, 6 (1981), 381–395.

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.

30 Mar 2006: Examples includequasistatic compression imaging [26, 29], axial shear wave imaging [32] and acoustic radiationforce imaging in both quasistatic/impulsive [24] and dynamic [2] forms. ... We substitute this into Equation 24, and rearrange to produce a

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

5 Jun 2006: 233.2.4 State-Based Speech Enhancement. 24. 3.3 Model-Based Techniques. 243.3.1 Linear Regression Adaptation. ... Ljm(τ ) = p(qjm(τ )|YT , M) (2.24)=. 1L(YT |M) Uj (τ )cjmbjm(y(τ ))βj (τ ). where. Uj (τ ) =. . a1j , if τ = 1N1i=2.

22 Nov 2006: The set of parameters,Θ(sm),. 1Using this form of auxiliary function yields the same update formulae asusing the extended Baum-Welch (EBW) algorithm [24], [25]. ... Wmpem =B2D. 2m B1Dm B0β. (c)m Dm. (23). where. B2 = Σ̂m (24).