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  2. pami04.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/stenger_pami06.pdf
    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
  3. Sparse and Semi-supervised Visual Mapping with the S3GP Oliver ...

    mi.eng.cam.ac.uk/reports/svr-ftp/williams_cvpr06.pdf
    3 Apr 2006: model. In the case of gaze tracking,the standard calibration process givesn = 80 (nl = 16);with m = 24, the S3GP takes 8s to train (24s including cal-ibration) and requires
  4. Reconstruction in the round using photometric normals. George…

    mi.eng.cam.ac.uk/reports/svr-ftp/hernandez_cvpr06.pdf
    19 Sep 2006: CACM, 24(6):381395,1981. 3. [5] D. Goldman, B. Curless, A. Hertzmann, and S.
  5. The Layout Consistent Random Field for Recognizing and Segmenting ...

    mi.eng.cam.ac.uk/reports/svr-ftp/shotton_cvpr06.pdf
    3 Apr 2006: Benavente. The AR face database. TechnicalReport 24, CVC, June 1998. [14] A.
  6. A New Look at Filtering Techniques for Illumination Invariance ...

    mi.eng.cam.ac.uk/reports/svr-ftp/arandjelovic_AFG06.pdf
    30 Jan 2006: FaceDB100 64.1/9.2 73.6/22.5 58.3/24.3 17.0/ 8.8FaceDB60 81.8/9.6 79.3/18.6 46.6/28.3
  7. 1 Model-Based Hand Tracking Using a HierarchicalBayesian Filter…

    mi.eng.cam.ac.uk/reports/svr-ftp/thayananthan_pami06.pdf
    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
  8. 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.
  9. IEEE TRANS. ON SAP, VOL. ?, NO. ??, ????? ...

    mi.eng.cam.ac.uk/research/projects/AGILE/publications/mjfg_ASL.pdf
    23 Feb 2006: Gales et al.: THE CUED BROADCAST NEWS TRANSCRIPTION SYSTEM 7. developing the Cambridge 10RT broadcast news system in1998 [24]11. ... 0.16 0.18 0.2 0.22 0.24 0.2612. 14. 16. 18. 20. 22.
  10. 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).
  11. techreport_20060422MJ.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/brostow_Eurographics06.pdf
    14 Sep 2006: Pattern Analysis andMachine Intelligence, 24(6):748–763, 2002. [22] S. Obdržálek and J. ... ACM Siggraph, 2004. [24] Carsten Rother, Sanjiv Kumar, Vladimir Kolmogorov,and Andrew Blake.
  12. PHASE-BASED ULTRASONICDEFORMATION ESTIMATION J. E. Lindop, G. M.…

    mi.eng.cam.ac.uk/reports/svr-ftp/lindop_tr555.pdf
    25 May 2006: Thus with no loss of accuracy Equation 9 isrewritten in the form of Equation 24.
  13. 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.
  14. thesis.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/nock_thesis.pdf
    14 Jun 2006: 145]; an empirical comparisonof techniques is provided by [24]. The N-gram model captures only local constraints and ignores higher-level structure.Many more sophisticated models have been investigated.
  15. pami04.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/hernandez_pami06.pdf
    19 Sep 2006: 3b. A simple way of measuring the silhouette coherence using theconcept of visual hull [24] is. ... pp. 245–262, 2001. [24] A. Laurentini, “The visual hull concept for silhouettebased image understanding,”IEEE Trans.
  16. IEEE TRANS. ON SAP, VOL. ?, NO. ??, ????? ...

    mi.eng.cam.ac.uk/~mjfg/liu_ASL07.pdf
    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. 21 Sep 2006: Pattern Recognition Letters, 24(2003):2743–2749, 2003. 8. O. Yamaguchi, K. Fukui, and K.
  18. 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
  19. Face Recognition from Video using the GenericShape-Illumination…

    mi.eng.cam.ac.uk/reports/svr-ftp/arandjelovic_ECCV06.pdf
    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.
  20. C:/SFWDoc/Academic/Publications/2005/BMVC_2005/FinalPaper/bmvc_05_sfwo…

    mi.eng.cam.ac.uk/reports/svr-ftp/sfwong_bmvc05.pdf
    21 Sep 2006: The average frame rate is 24.1 frames per second(fps)). That is to say, the system can run inreal-time.
  21. 21 Nov 2006: b. . . . . (24). One candidate for estimating the decision bound-ary is the Support Vector Machine (SVM).

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