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    CVPR
    1999
    IEEE
    165views Computer Vision» more  CVPR 1999»
    Implicit Representation and Scene Reconstruction from Probability Density Functions
    15 years 11 months ago
    Implicit Representation and Scene Reconstruction from Probability Density Functions
    Download  www.cs.cmu.edu
    A technique is presented for representing linear features as probability density functions in two or three dimensions. Three chief advantages of this approach are (1) a unified re...
    Steven M. Seitz, P. Anandan
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    WSCG
    2004
    245views more  WSCG 2004»
    Pel-Recursive Motion Estimation Using the Expectation-Maximization Technique and Spatial Adaptation
    14 years 11 months ago
    Pel-Recursive Motion Estimation Using the Expectation-Maximization Technique and Spatial Adaptation
    Download  wscg.zcu.cz
    Pel-recursive motion estimation is a well-established approach. However, in the presence of noise, it becomes an ill-posed problem that requires regularization. In this paper, mot...
    Vania V. Estrela, Luís A. Rivera, Marcos H....
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    AVSS
    2005
    IEEE
    173views Signal Processing» more  AVSS 2005»
    Multi-modal face image super-resolutions in tensor space
    15 years 3 months ago
    Multi-modal face image super-resolutions in tensor space
    Download  www.bmva.ac.uk
    Face images of non-frontal views under poor illumination with low resolution reduce dramatically face recognition accuracy. To overcome these problems, super-resolution techniques...
    Kui Jia, Shaogang Gong
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    CVPR
    2008
    IEEE
    197views Computer Vision» more  CVPR 2008»
    On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation
    15 years 11 months ago
    On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation
    Download  hci.iwr.uni-heidelberg.de
    Linear inverse problems in computer vision, including motion estimation, shape fitting and image reconstruction, give rise to parameter estimation problems with highly correlated ...
    Björn Andres, Claudia Kondermann, Daniel Kond...
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    83
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    ICML
    2008
    IEEE
    157views Machine Learning» more  ICML 2008»
    Efficiently learning linear-linear exponential family predictive representations of state
    15 years 10 months ago
    Efficiently learning linear-linear exponential family predictive representations of state
    Download  web.mit.edu
    Exponential Family PSR (EFPSR) models capture stochastic dynamical systems by representing state as the parameters of an exponential family distribution over a shortterm window of...
    David Wingate, Satinder P. Singh
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