Karim

Results: 529



#Item
21

Joint Source-Channel Turbo Decoding of Entropy Coded Sources Karim Ali Fabrice Labeau Center for Advanced Systems and Technologies in Communications (SYTACom)

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Source URL: www.karimali.org

- Date: 2007-12-13 08:39:29
    22

    Training for Task Specific Keypoint Detection Christoph Strecha, Albrecht Lindner, Karim Ali and Pascal Fua CVLab EPFL Lausanne Switzerland Abstract. In this paper, we show that a better performance can be achieved by

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    Source URL: www.karimali.org

    - Date: 2013-02-08 23:21:01
      23

      Confidence-Rated Multiple Instance Boosting for Object Detection Karim Ali1,2 and Kate Saenko2 1 2 University of California Berkeley, Berkeley, CA, USA

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      Source URL: www.karimali.org

      - Date: 2014-05-15 23:42:36
        24

        Data-Driven Visual Tracking in Retinal Microsurgery Raphael Sznitman1 , Karim Ali1 , Rogerio Richa2 , Russell H. Taylor2 , Gregory D. Hager2 , and Pascal Fua1 ´ Ecole

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        Source URL: www.karimali.org

        - Date: 2013-02-08 23:22:03
          25

          Learning Context Cues for Synapse Segmentation in EM Volumes Carlos Becker, Karim Ali, Graham Knott, and Pascal Fua ´ Ecole Polytechnique F´ed´erale de Lausanne, Switzerland

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          Source URL: www.karimali.org

          - Date: 2012-05-31 18:50:20
            26

            Karim AliDr. Penfield, Montréal, Québec, H3G1B9 + – http://cvlabwww.epfl.ch/~ali

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            - Date: 2013-02-09 18:05:09
              27

              REDUCED COMPLEXITY JOINT SOURCE-CHANNEL TURBO DECODING OF ENTROPY CODED SOURCES Karim Ali Fabrice Labeau

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              Source URL: www.karimali.org

              - Date: 2007-12-13 08:40:03
                28

                Learning Deep Object Detectors from 3D Models Xingchao Peng, Baochen Sun, Karim Ali, Kate Saenko University of Massachusetts Lowell arXiv:1412.7122v4 [cs.CV] 12 Oct 2015

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                Source URL: cs-people.bu.edu

                - Date: 2017-12-15 00:58:34
                  29

                  Deconstructing the Deformable Parts Model: Do More with Less Brigit Schroeder1 , Baochen Sun1 , Kate Saenko1 , Karim Ali1,2 1 University of Massachusetts, Lowell

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                  Source URL: www.cs.uml.edu

                  - Date: 2014-10-01 23:46:42
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