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  1. Results that match 1 of 2 words

  2. PII: S0167-6393(99)00044-8

    mi.eng.cam.ac.uk/~sjy/papers/wiyo00.pdf
    20 Feb 2018: For exam-ple, the threshold for a phone p can be dened interms of the mean lp and variance rp of all theGOP scores for phone p in the training data,. ... For exam-ple, Fig. 2 shows the resulting network for theword but''.
  3. The Joint Manifold Model for Semi-supervised Multi-valued Regression…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2007-ICCV-manifold-learning.pdf
    13 Mar 2018: this “vision as regression” paradigm: given training exam-ples comprising corresponding pairs of image features (z)and joint angles (θ), learn a function θ = f (z). ... This significantly reduces the number of training exam-ples needed to learn
  4. Uncertain RanSaC Ben Tordoff and Roberto CipollaDepartment of…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2005-MVA-Tordoff.pdf
    13 Mar 2018: Althoughits utility is widespread (two appropriately random exam-ples of substantially different uses are [7], [9]), RanSaChas proved particularly effective for estimating inter-imagetransforms such as the homography and fundamental
  5. IMAGE MOSAICING VIA QUADRIC SURFACE ESTIMATION WITH PRIORS FOR ...

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2009-ICIP-tunnel-mosaic.pdf
    13 Mar 2018:  . . Fig. 4: SVM
  6. 15 Jun 2018: kate.knill,mjfg,kk492,ar527,yw396}@eng.cam.ac.uk. Abstract. Automatic systems for practice and exams are essential to sup-port the growing worldwide demand for learning English as anadditional
  7. Uncertain RanSaC Ben Tordoff and Roberto CipollaDepartment of…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2005-MVA-Tordoff.pdf
    13 Mar 2018: Althoughits utility is widespread (two appropriately random exam-ples of substantially different uses are [7], [9]), RanSaChas proved particularly effective for estimating inter-imagetransforms such as the homography and fundamental
  8. 20 Feb 2018: 5 Discriminative Training. In contrast to the traditional ML criteria (Equation 1)whose goal is to maximise the log-likelihood of cor-rect examples, DT aims at separating correct exam-ples
  9. The Joint Manifold Model for Semi-supervised Multi-valued Regression…

    mi.eng.cam.ac.uk/~cipolla/archive/Publications/inproceedings/2007-ICCV-manifold-learning.pdf
    13 Mar 2018: this “vision as regression” paradigm: given training exam-ples comprising corresponding pairs of image features (z)and joint angles (θ), learn a function θ = f (z). ... This significantly reduces the number of training exam-ples needed to learn
  10. Using Frontier Points to Recover Shape, Reflectance and Illumination…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2005-ICCV-Vogiatzis-frontier.pdf
    13 Mar 2018: For exam-ple, we present a novel solution tophotometric stereo, i.e.the problem of recovering 3d shape given multiple imagescaptured from the same viewing point, but under differentunknown illumination conditions. ... Figure6demonstrates that similar
  11. Tracking Using Online Feature Selectionand a Local Generative Model…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2007-BMVC-Woodley.pdf
    13 Mar 2018: The classifier uses a set of discriminative local features which is updated at eachtime step using on-line boosting [4]: using the previous object location as positive exam-ple and surrounding

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