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Clinically Practical Freehand Three-Dimensional Ultrasound
mi.eng.cam.ac.uk/research/projects/cp3dus/22 Jul 2010: We have developed novel reconstruction algorithms to relax this constraint [1, 2, 15, 20, 23, 31, 33]. ... 20] R.J. Housden, A.H. Gee, R.W. Prager and G.M. Treece. Sensorless freehand 3D ultrasound for non-monotonic and intersecting frames. -
2010-FREng
mi.eng.cam.ac.uk/~cipolla/archive/Public-Understanding/2010-FREng.pdf19 Oct 2010: 20 September 2010. Congratulations to Roberto Cipolla Professor ofInformation Engineering and Dr Ivor Day SeniorRolls-Royce Research Fellow, at the WhittleLaboratory, who have been elected as Fellows of theRoyal Academy of -
Acoustic Modelling for Speech Recognition:Hidden Markov Models and…
mi.eng.cam.ac.uk/~mjfg/ASRU_talk09.pdf5 Jan 2010: Cambridge UniversityEngineering Department. 20. Acoustic Modelling for Speech Recognition: Hidden Markov Models and Beyond? ... IEEE Int.Conf. Acoust., Speech, Signal Process., Orlando, FL, May 2002. [20] D. -
A NORMALIZATION METHODFOR AXIAL-SHEAR STRAIN ELASTOGRAPHY L. Chen, R. …
mi.eng.cam.ac.uk/reports/svr-ftp/chen_tr645.pdf13 May 2010: effect very similar to that observed with non-axially aligned,elliptical inclusions [20, 21]. ... This is very likely the “fillin” effect [20, 21]. 6. (a) 10 mm 0. -
paper.dvi
mi.eng.cam.ac.uk/~mjfg/richter_EURO99.pdf19 Nov 2010: 19). and. Σ̂(m) =. τγ(m)(τ). (. q(m)(τ))α(m)/21. Ŵ(m)(τ). τγ(m)(τ). , (20). whereq(m)(τ) is defined in equation (10),̂W(m)(τ) ... It is not knownthat the overall likelihood is guaranteed to increase with the updategiven by (19)–(20), -
A DATA WEIGHTING SCHEMEFOR QUASISTATIC ULTRASOUND ELASTICITY IMAGING…
mi.eng.cam.ac.uk/reports/svr-ftp/chen_tr651.pdf13 May 2010: 1. 2.5. (f) (g) 0. 10. 20. Pixel. 0. 63. Str. -
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 4, ...
mi.eng.cam.ac.uk/~cipolla/publications/article/2010-IP-Face-Recognition.pdf25 Oct 2010: Prior video-basedattempts [18]–[20] have shown that including a strong temporalconstraint deteriorates recognition performance when personsmove arbitrarily in a testing video sequence. ... CVPR, Madison, WI, 2003, pp.340–345. [20] K. Lee, M. Yang, -
1 Structured Log Linear Models for Noise RobustSpeech Recognition ...
mi.eng.cam.ac.uk/~mjfg/zhang10.pdf8 Sep 2010: 3.86 3.63 3.3305 11.20 10.02 9.16 8.6600 29.55 28.00 25.09 23.90. -
A NEW METHOD FOR THEACQUISITION OF ULTRASONIC STRAIN IMAGE ...
mi.eng.cam.ac.uk/reports/svr-ftp/housden_tr656.pdf10 Aug 2010: This compression can be achieved ina laboratory setting using an external fixture to compress the scanning subject [1, 8, 10, 15, 20, 22]. ... brain 1 14.75 15.72 20.90brain 2 8.35 8.68 14.60brain 3 11.37 11.49 17.53. -
eps.dis.dur.testa.eps
mi.eng.cam.ac.uk/~mjfg/gales_ASRU09.pdf14 Sep 2010: SYN HMM 30 9.20 8.51 9.34 9.02HTS 5 8.41 8.03 8.70 8.38. ... Beyond HMM Workshop, December 2004. [20] H. AlDamarki, “Filter trees for noise robust small vocabulary speechrecognition,” M.Phil. -
RAPID HYBRIDULTRASOUND VOLUME REGISTRATION U. Z. Ijaz, R. W. ...
mi.eng.cam.ac.uk/reports/svr-ftp/Ijaz_tr644.pdf26 May 2010: Time (s)0 20 40 60 80. Relia. bili. ty. 60. 80. -
DISCRIMINATIVE CLASSIFIERS WITH ADAPTIVE KERNELS FOR NOISE ROBUST…
mi.eng.cam.ac.uk/~mjfg/gales_flego_CSL10.pdf14 Sep 2010: T)(20). µϕ =1. n. ni=1. ϕ(Yi; λ) (21). where there are n training data sequences Y1,. , ... dB) N1 N2 N3 N4. 20 1.38 †1.45 †1.25 †1.30 1.35. -
dualSpd_D2c_TechR.dvi
mi.eng.cam.ac.uk/reports/svr-ftp/Shin_TR637.pdf26 May 2010: based on an approach applied to homogeneous media by Jensen and Svendsen [20]. ... 15.3 mm, and the other with 20.5 mm. The thickness of each top layer was evaluated later based on. -
Model-Based Approaches to HandlingUncertainty M.J.F. Gales Abstract A …
mi.eng.cam.ac.uk/~mjfg/noise_review10.v1.pdf3 Sep 2010: This makes them impractical for very rapid adaptation, thoughmodifications to improve robustness are possible [7, 20]. ... 0.02. 0.04. 0.06. 0.08. 0.1. 0.12. 0.14. 0.16. 0.18. 0.2. (a) Clean Speech (speech mean=0)25 20 15 10 5 -
High resolution cortical thickness measurement from clinical CT data…
mi.eng.cam.ac.uk/reports/svr-ftp/treece_tr634.pdf12 Jan 2010: The whole processtakes approximately 15 minutes for typical low resolution data, producing a surface with up to 20,000 ver-tices. ... results. 20 3 RESULTS. Table 4: Cortical thickness estimation error, for errors below 4 mm and thicknesses in the range 0 -
IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 19, NO. 4, ...
mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2010-IP-Face-Recognition.pdf25 Oct 2010: Prior video-basedattempts [18]–[20] have shown that including a strong temporalconstraint deteriorates recognition performance when personsmove arbitrarily in a testing video sequence. ... CVPR, Madison, WI, 2003, pp.340–345. [20] K. Lee, M. Yang, -
Int J Comput VisDOI 10.1007/s11263-010-0381-3 Incremental Linear…
mi.eng.cam.ac.uk/~cipolla/publications/article/2010-IJCV-Kim.pdf25 Oct 2010: where. A =. ks. n1kn2kn1k n2k (m2k m1k)(m2k m1k). T. (20). ... 20 20 pixelgray-value images were used. LDA was computed with thelabeled train data and class label estimation of the unlabeledsamples, which was obtained by the maximum -
Kernel Methods forText-Independent Speaker Verification Chris…
mi.eng.cam.ac.uk/~mjfg/thesis_cl336.pdf25 Feb 2010: 151. 8.20 Combination of derivative kernel with various static kernels. Viterbi statisticswere used and component posteriors were obtained from GMMs trained usingthe original observations. -
Int J Comput VisDOI 10.1007/s11263-010-0381-3 Incremental Linear…
mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2010-IJCV-Kim.pdf25 Oct 2010: where. A =. ks. n1kn2kn1k n2k (m2k m1k)(m2k m1k). T. (20). ... 20 20 pixelgray-value images were used. LDA was computed with thelabeled train data and class label estimation of the unlabeledsamples, which was obtained by the maximum -
Lattice Rescoring Methods forStatistical Machine Translation Graeme…
mi.eng.cam.ac.uk/~wjb31/ppubs/gwbthesis2010.pdf6 Oct 2010: 20. 4 Statistical Machine Translation 214.1 Introduction to Statistical Machine Translation. ... 11. 3.1 Finite-state acceptor representation of a trigram language model. 20.
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