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5 Jul 2006: factorial HMMs [19], loosely coupled models [20], system combination [16]– synchronous: likelihoods combined at the state level. ... 0. 5. 10. 15. 20. 25. 30. 35. 40. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000. %

1 Sep 2006: a) Glasses ON. 0 20 40 60 80 100 1200.85. 0.9. ... Shashua. Learning over sets using kernel principalangles.JMLR, 4(10), 2003. [20] L.

19 May 2006: Store additional branches ///. /// update bound and nearestNeighbour ///. }. return nearestNeighbour;. }. 20 Engineering Part IB: Paper 8 Image Search.

19 Jul 2006: 20 million words, 720 million frames of data);– rapid transcriptions/closed caption data. • ... 5. 10. 108. 64. 20. 24. 68. 10. 0. 0.05. 0.1.

29 Apr 2006: λ(1)Dj = λ(2)Dj. (19). Then, writing. |Ci| =D. j=1. λ(i)j , (20). ... 12] K. K. Sung and T. Poggio. Example-based learning for view-basedhuman face detection.PAMI, 20(1), 1998.

5 Jul 2006: 25. 20. 15. 10. 5. 0. 5 sh ow dh ax g r ih dd l iy z. ... Cambridge UniversityEngineering Department. Trajectory Models For Speech Processing Workshop 20. Augmented Statistical Models for Speech Recognition.

30 Jan 2006: 10. 15. 20. 25. 30. 35. 40. 45. Method. Err. or r. ... 5. 10. 15. 20. 25. Method. Err. or r. ate,. std. (%. ).

22 Nov 2006: Thefront-end uncertainty schemes used diagonal transformations. SNR(dB)System 20 15 10 5. ... Number of SNR(dB)System Transforms 20 15 10 5. Diagonal Transformations1 3.33 5.92 13.35 31.96.

21 Sep 2006: 20.2 fps).Figure 3 illustrates the recognition process on a typical test-ing clip. ... Pentland. Real-time ameri-can sign language recognition using desk and wearable com-puter based video.PAMI, 20(12):1371–1375, 1998.

22 Nov 2006: Frames from phrase:SHOW THE GRIDLEY’S. Legend. • True• HMM• SLDS. 20 40 60 80 10035. ... 0.5. 1. 2. 5. 10. 20. 40. False Alarm probability (in %)M.

21 Sep 2006: Learning Discriminative Canonical Correlationsfor Object Recognition with Image Sets. Tae-Kyun Kim1, Josef Kittler2, and Roberto Cipolla1. 1 Department of Engineering, University of CambridgeCambridge, CB2 1PZ, UK. {tkk22,cipolla}@eng.cam.ac.uk2

22 Nov 2006: S1HLDA 12.4 6.6 21.1GAUSS 11.9 6.2 20.3. Table 2. Unadapted performance of baseline systems using HLDAor GAUSS front-ends, V1 segmentation and lm1.0. ... System LMCER (%). dev04f eval03m eval04. S2 GAUSS. lm1.0 12.2 5.6 20.0lm2.0 12.3 5.2 20.2lm2.1 11.5 5

18 Sep 2006: 20].Several comparison studies of the various interest points and descriptors havebeen carried out by Mikolajczyk and Schmid, of which the most recent [14] isan excellent survey of the field. ... 20] T. Tuytelaars and Luc Van Gool. Matching widely

22 Nov 2006: babi. lity. Figure 3: Training set word posteriors, G0. 0 10 20 30 40 50 60 70 80 90 100104. ... oCptqu=s [ , corresponding to32% of words is also marked. Approximately 20% of words havezero posterior probability with respect to G0.

19 Sep 2006: These two steps are interleaved until convergence whichtakes about 20 steps for the sequences we experimentedwith.

22 Nov 2006: µm =xnm xdm Dµ̂mβnm βdm Dm. (20). Wmpem =Ynm Ydm Dm(Σ̂m µ̂mµ̂′m). ... Combining equations (20)and (21) gives the full covariance statistics in terms ofDm,.

19 Mar 2006: a) Glasses ON. 0 20 40 60 80 100 1200.85. 0.9. ... IEEE Trans. Pattern Analysis and Machine Intelli-gence, 20(12):1295–1307, 1998. [21] T.

22 Nov 2006: 0.1 0.2 0.5 1 2 5 10 20 40. 0.1. 0.2. ... 20, pp. 210–229, 2005. [5] D. Povey, M.J.F. Gales, D.Y. Kim, and P.C.

23 Feb 2006: S1HLDA 12.4 6.6 21.1GAUSS 11.9 6.2 20.3. Table 2. Unadapted performance of baseline systems using HLDAor GAUSS front-ends, V1 segmentation and lm1.0. ... System LMCER (%). dev04f eval03m eval04. S2 GAUSS. lm1.0 12.2 5.6 20.0lm2.0 12.3 5.2 20.2lm2.1 11.5 5

19 Sep 2006: 17]–[20]. In particular, the works of [18] and [19] use onlythe two outermost epipolar tangents,. ... to match them across different views and handle their visibility, as proposed in [16] and [20].