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21 - 40 of 48 search results for KaKaoTalk:ZA31 24 24 |u:mi.eng.cam.ac.uk where 0 match all words and 48 match some words.
  1. Results that match 2 of 3 words

  2. Semi-supervised Learning of Joint DensityModels for Human Pose…

    mi.eng.cam.ac.uk/reports/svr-ftp/navaratnam_semi_supervised.pdf
    14 Sep 2006: ln = 40.80RMS= 24.85. ln = 78.35RMS= 26.56. ln = 98.72RMS = 12.67. ... ln = 30.47RMS= 13.12. ln = 54.29RMS= 24.98. Figure 6:Pose Detection:This illustrates results from applying the GMM learnt from 8k marginaland 2k joint data points with 50
  3. 21 Sep 2006: Pattern Recognition Letters, 24(2003):2743–2749, 2003. 8. O. Yamaguchi, K. Fukui, and K.
  4. 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.
  5. article.dvi

    mi.eng.cam.ac.uk/~mjfg/rosti_CSL04.pdf
    22 Nov 2006: Factor analysed hidden Markov models for. speech recognition. A-V.I. Rosti , M.J.F. Gales. Cambridge University Engineering Department, Trumpington Street, Cambridge,. CB2 1PZ, UK. Abstract. Recently various techniques to improve the correlation
  6. Incremental Learning of Temporally-CoherentGaussian Mixture Models…

    mi.eng.cam.ac.uk/reports/svr-ftp/arandjelovic_SME06.pdf
    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.
  7. 21 Nov 2006: b. . . . . (24). One candidate for estimating the decision bound-ary is the Support Vector Machine (SVM).
  8. 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.
  9. 22 Nov 2006: Now for the priors to satisfy 22. wmKm 0 (24). with the additional constraint that at least one of the meta-component valuesis greater than zero.
  10. SUB-SAMPLE INTERPOLATIONSTRATEGIES FOR SENSORLESSFREEHAND 3D…

    mi.eng.cam.ac.uk/reports/svr-ftp/housden_tr545.pdf
    13 Jan 2006: 0.11 9.47 0.09fourier 5.26 0.44 12.24 0.17 9.89 0.08 8.84 0.31.
  11. 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
  12. 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.
  13. EUROGRAPHICS 2006 / E. Gröller and L. Szirmay-Kalos(Guest Editors) ...

    mi.eng.cam.ac.uk/reports/svr-ftp/johnson_semantic06.pdf
    1 Jun 2006: IEEE Trans. Pattern Analysis and MachineIntelligence 24, 6 (2002), 748–763. [MBSL99] MALIK J., BELONGIE S., SHI J., LEUNG T.: Tex-tons, contours and regions: Cue integration in image segmenta-tion.
  14. 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
  15. 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.
  16. 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.
  17. 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.
  18. 19 Dec 2006: Uncertainty 1 4 16 256. Clean — 33.2. SPLICENo. 24.6 20.7 17.0 12.3FE-CMLLR 16.3 15.3 12.8 13.5.
  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. 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.
  21. 22 Nov 2006: Devel-opment data, dev04, was made available for this task comprising2 hours of data, 24 conversations.

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