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1 - 10 of 17 search results for KaKaoTalk:vb20 200 |u:mi.eng.cam.ac.uk where 0 match all words and 17 match some words.

  1. Results that match 1 of 2 words

  2. TextonBoost: Joint Appearance, Shape andContext Modeling for…

    mi.eng.cam.ac.uk/reports/svr-ftp/shotton_eccv06.pdf
    15 Feb 2006: 9. (a)0 100 200 300 400 500. 0. 2. 4. 6.

  3. stenger_imavis06.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/stenger_imavis06.pdf
    21 Sep 2006: At. 400 200 0 200 4000. 0.01. 0.02. 0.03. 0.04. 0.05. ... a). 100 0 100 200 3000. 0.02. 0.04. 0.06. 0.08. x.

  4. SENSORLESS RECONSTRUCTIONOF UNCONSTRAINED FREEHAND 3D ULTRASOUND DATA …

    mi.eng.cam.ac.uk/reports/svr-ftp/housden_tr553.pdf
    22 May 2006: SENSORLESS RECONSTRUCTIONOF UNCONSTRAINED FREEHAND. 3D ULTRASOUND DATA. R. J. Housden, A. H. Gee,G. M. Treece and R. W. Prager. CUED/F-INFENG/TR 553. May 2006. University of CambridgeDepartment of Engineering. Trumpington StreetCambridge CB2 1PZ.

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

    mi.eng.cam.ac.uk/reports/svr-ftp/navaratnam_semi_supervised.pdf
    14 Sep 2006: The question then is to what extent adding marginal samples. 0 50 100 150 200 250 3002.

  6. paper563_final.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/thayananthan_eccv06.pdf
    14 Sep 2006: Fig. 2. RVM regression on a toy dataset. The data set consists of 200 samplesfrom three polynomial functions with added Gaussian noise. ... For testing, 200 poses aregenerated by randomly sampling the same region in parameter space and in-troducing

  7. paper.dvi

    mi.eng.cam.ac.uk/~mjfg/liao_INTER06.pdf
    22 Nov 2006: 180 190 200 210 220 2300. 50. 100. Frame. aii. Figure 3: Plot of log energy for snippet from AURORA digit string8-6-Zero-1-1-6-2, showing joint ... Asanticipated, the extremes previously observed have disappeared. 180 190 200 210 220 230.

  8. pami04.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/stenger_pami06.pdf
    21 Sep 2006: 10. 20. 30. 40. Frame. RM. S e. rror. (b). 0 50 100 150 200 250 300 350 400 450 5000.

  9. Machine Learning for Speech & LanguageProcessing Mark Gales 28 ...

    mi.eng.cam.ac.uk/~mjfg/FCSW_talk.pdf
    19 Jul 2006: Gaussianise the data for each speaker:. 20 15 10 5 0 5 10 15 200. ... 0.01. 0.02. 0.03. 0.04. 0.05. 0.06. 0.07. 0.08. 0.09. 0.1. 20 15 10 5 0 5 10 15 200.

  10. Learning Discriminative Canonical Correlationsfor Object Recognition…

    mi.eng.cam.ac.uk/reports/svr-ftp/kim_eccv06.pdf
    21 Sep 2006: 9. 100 150 200 250 300 350 4000.5. 0.6. 0.7. 0.8.

  11. Incremental Learning of Locally OrthogonalSubspaces for Set-based…

    mi.eng.cam.ac.uk/reports/svr-ftp/kim_bmvc06.pdf
    21 Sep 2006: 0 50 100 150 200 250 300 350 4000.25. 0.2. 0.15. ... Fea. ture. Val. ueBatch OSMINC OSM. 50 100 150 200 250 300 350 4000.

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