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Semi-supervised Learning of Joint DensityModels for Human Pose…
mi.eng.cam.ac.uk/reports/svr-ftp/navaratnam_semi_supervised.pdf14 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 -
Learning Discriminative Canonical Correlationsfor Object Recognition…
mi.eng.cam.ac.uk/reports/svr-ftp/kim_eccv06.pdf21 Sep 2006: Pattern Recognition Letters, 24(2003):2743–2749, 2003. 8. O. Yamaguchi, K. Fukui, and K. -
C:/SFWDoc/Academic/Publications/2005/BMVC_2005/FinalPaper/bmvc_05_sfwo…
mi.eng.cam.ac.uk/reports/svr-ftp/sfwong_bmvc05.pdf21 Sep 2006: The average frame rate is 24.1 frames per second(fps)). That is to say, the system can run inreal-time. -
article.dvi
mi.eng.cam.ac.uk/~mjfg/rosti_CSL04.pdf22 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 -
Incremental Learning of Temporally-CoherentGaussian Mixture Models…
mi.eng.cam.ac.uk/reports/svr-ftp/arandjelovic_SME06.pdf14 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. -
IEICE TRANS. ??, VOL.Exx–??, NO.xx XXXX 200x1 INVITED PAPER ...
mi.eng.cam.ac.uk/~mjfg/gales_IEICE06.pdf21 Nov 2006: b. . . . . (24). One candidate for estimating the decision bound-ary is the Support Vector Machine (SVM). -
IEEE TRANS. ON SAP, VOL. ?, NO. ??, ????? ...
mi.eng.cam.ac.uk/research/projects/AGILE/publications/mjfg_ASL.pdf23 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. -
Product of Gaussians for Speech Recognition M.J.F. Gales and ...
mi.eng.cam.ac.uk/~mjfg/airey_CSL06.pdf22 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. -
SUB-SAMPLE INTERPOLATIONSTRATEGIES FOR SENSORLESSFREEHAND 3D…
mi.eng.cam.ac.uk/reports/svr-ftp/housden_tr545.pdf13 Jan 2006: 0.11 9.47 0.09fourier 5.26 0.44 12.24 0.17 9.89 0.08 8.84 0.31. -
1 Model-Based Hand Tracking Using a HierarchicalBayesian Filter…
mi.eng.cam.ac.uk/reports/svr-ftp/thayananthan_pami06.pdf14 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 -
techreport_20060422MJ.dvi
mi.eng.cam.ac.uk/reports/svr-ftp/brostow_Eurographics06.pdf14 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. -
EUROGRAPHICS 2006 / E. Gröller and L. Szirmay-Kalos(Guest Editors) ...
mi.eng.cam.ac.uk/reports/svr-ftp/johnson_semantic06.pdf1 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. -
Sparse and Semi-supervised Visual Mapping with the S3GP Oliver ...
mi.eng.cam.ac.uk/reports/svr-ftp/williams_cvpr06.pdf3 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 -
Reconstruction in the round using photometric normals. George…
mi.eng.cam.ac.uk/reports/svr-ftp/hernandez_cvpr06.pdf19 Sep 2006: CACM, 24(6):381395,1981. 3. [5] D. Goldman, B. Curless, A. Hertzmann, and S. -
The Layout Consistent Random Field for Recognizing and Segmenting ...
mi.eng.cam.ac.uk/reports/svr-ftp/shotton_cvpr06.pdf3 Apr 2006: Benavente. The AR face database. TechnicalReport 24, CVC, June 1998. [14] A. -
thesis.dvi
mi.eng.cam.ac.uk/reports/svr-ftp/nock_thesis.pdf14 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. -
Joint Uncertainty Decoding for Noise Robust Speech Recognition H. ...
mi.eng.cam.ac.uk/~mjfg/liao_INTER05.pdf19 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. -
Face Recognition from Video using the GenericShape-Illumination…
mi.eng.cam.ac.uk/reports/svr-ftp/arandjelovic_ECCV06.pdf17 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. -
PHASE-BASED ULTRASONICDEFORMATION ESTIMATION J. E. Lindop, G. M.…
mi.eng.cam.ac.uk/reports/svr-ftp/lindop_tr555.pdf25 May 2006: Thus with no loss of accuracy Equation 9 isrewritten in the form of Equation 24. -
DEVELOPMENT OF THE CUHTK 2004 MANDARIN CONVERSATIONAL TELEPHONESPEECH …
mi.eng.cam.ac.uk/~mjfg/gales_ICASSP05.pdf22 Nov 2006: Devel-opment data, dev04, was made available for this task comprising2 hours of data, 24 conversations.
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