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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/senior_thesis.pdf9 Aug 2005: 8X;OP>L,72]N [=r687[FTP>L H Q LG;3=@DGF 7N [L DGQ:]. ... 1=@DED.9[=2 =@DML,_:= 9A Y V=OP-L F [P>L,H FPOC.l_:= = ;>=LKZ 7KCEPOCML,7U? -
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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/kadirkamanathan_ioa92.pdf9 Aug 2005: pGj#G.G[_e 03TCOrpVc#<6. ) (y #@(0T8:Op r3TCKG[:f_( gO+fOnj:0O j O&(0/3O CTC0yJK(Oe9Zn. RO. ... OLF 8&F(iGJK}gO8)Y/0 o 9_90 :d 0 Op(O0. 3y(0COY #DViL @1{0y =C 03TCOC(OlO. -
Hole Filling Through Photomontage Marta Wilczkowiak∗, Gabriel J.…
mi.eng.cam.ac.uk/reports/svr-ftp/brostow_HoleFillingBMVC05.pdf14 Sep 2006: Now the problem of finding an op-timal replacement of pixels in patch p′ from those in patch m′ is equivalent to finding apath in the graph such that the error -
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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/yow_accv95.pdf9 Aug 2005: "!#$%&"'(%&). ,-/.1023435.67398 :9.<;>=3@?<ACBDE3 0-/FG3@HIHJ:KLACFM:NBDEOAC.PD3NQR.6@-I.ACASB-I.6T.-IU4ACBWVX-/DXYZ3NQ[0:9O?B-];P65A. 0:NO?B-J;65AZ0_[ aSbdce<R.6@H]:9.<;. fghji9k9lm5in&o9prqPs5tus5vwxCvXqzy -
A Comparative Study of Methods forPhonetic Decision-Tree State…
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/nock_euro97.pdf9 Aug 2005: Thus, although the baseline systems were op-timised over a limited range of thresholds, the figures obtainedare still susceptible to some degree of noise. -
Filtering Using a Tree-Based Estimator B. Stenger∗ A. Thayananthan∗…
mi.eng.cam.ac.uk/reports/svr-ftp/thayananthan_iccv03.pdf8 Aug 2005: Filtering Using a Tree-Based Estimator. B. Stenger A. Thayananthan P. H. S. Torr† R. Cipolla. University of Cambridge † Microsoft Research Ltd.Department of Engineering 7 JJ Thompson AvenueCambridge, CB2 1PZ, UK Cambridge, CB3 OFB, UK. -
A NEURAL NETWORK BASED, SPEAKER INDEPENDENT, LARGE…
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/wernicke_eurospeech93.pdf9 Aug 2005: 1.3. ComputingThe HMM-ANN approach is very computationally expensive.The networks currently require about 1013 floating point op-erations to train and future estimates of the required computepower is one -
A Simple Technique for Self-Calibration
mi.eng.cam.ac.uk/reports/svr-ftp/mendonca_self-calibration.pdf10 Aug 1999: This goal is achieved by solving an op-timization problem by numerical techniques, searching di-rectly for the intrinsic parameters of the cameras, instead ofthe indirect search performed by the algorithms -
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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/robinson_icassp92.pdf9 Aug 2005: l>SZ'C< 3)OP@j- /);@)f;@!;@j7r) V h[H&M5@)(Af)(>-@ 5 I "HI@EHH)&O})(47)7O;<<?@)(9jAf @@Af3)VX C 3 I"j#f@B < f@C# ... Hl@H-)( (B )(.>#B 4P47(#;47(,4> '6:>o6; H(# q>9Km>o. @#>19I. #AY[>oP>o6;-<V<>_$@E3SRTA6CSÇ4(-Vdf)(T3)&H-mKMC (-"!IC5O})(/H-)( &)(&J! -
Multi-Sensory Face Biometric Fusion (for Personal Identification)…
mi.eng.cam.ac.uk/reports/svr-ftp/arandjelovic_OTCBVS06.pdf19 Mar 2006: The optimal. values were found to be2.3 and6.2 for visual data; the op-timal filter for thermal data was found to be alow-passfilterwith W2 = 2.8 (i.e.
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