Isbn: 9783659910579 - hand gesture recognition using computer vision techniques (6 risultati)

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    • Lingua: Inglese

      Editore: LAP LAMBERT Academic Publishing, 2016

      3659910570 / 9783659910579

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      Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books

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      EUR 76,50

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      Paperback. Condizione: Brand New. 64 pages. 8.66x5.91x0.15 inches. In Stock.

    • Lingua: Inglese

      Editore: LAP LAMBERT Academic Publishing, 2016

      3659910570 / 9783659910579

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      Da: preigu, Osnabrück, Germaniapreigu

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      EUR 33,30

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      Taschenbuch. Condizione: Neu. Hand Gesture Recognition Using Computer Vision Techniques | Hassam Muazzam | Taschenbuch | 64 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783659910579 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

    • Lingua: Inglese

      Editore: LAP LAMBERT Academic Publishing Jun 2016, 2016

      3659910570 / 9783659910579

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      • Print on Demand

      Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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      EUR 35,90

      EUR 23,00 spedizione 
      Spedito da Germania a U.S.A.

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      Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -An endeavor has been made with the help of computer vision techniques and the release of Microsoft Kinect camera. The task of segmentation has been implemented using RGB with depth information; to enhance it, YCbCr color scheme is used. Two level feature extraction algorithms are examined, Scale Invariant Feature Transform (SIFT) for RGB images and the Gradient kernel descriptor method for depth images. Finally, Support Vector Machine (SVM) and K- nearest neighbor (K-NN) are tested. 64 pp. Englisch.

    • Lingua: Inglese

      Editore: LAP LAMBERT Academic Publishing, 2016

      3659910570 / 9783659910579

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      • Print on Demand

      Da: moluna, Greven, Germaniamoluna

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      EUR 31,27

      EUR 48,99 spedizione 
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      Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Muazzam HassamEngr. Hassam Muazzam received his Bachelors in Electrical Engineering from University of the Punjab and Masters in Electrical Engineering from Government College University.He is currently working as a Lecturer in Depar.

    • Lingua: Inglese

      Editore: LAP LAMBERT Academic Publishing Jun 2016, 2016

      3659910570 / 9783659910579

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      • Print on Demand

      Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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      Condizione: Nuovo

      EUR 35,90

      EUR 60,00 spedizione 
      Spedito da Germania a U.S.A.

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      Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -An endeavor has been made with the help of computer vision techniques and the release of Microsoft Kinect camera. The task of segmentation has been implemented using RGB with depth information; to enhance it, YCbCr color scheme is used. Two level feature extraction algorithms are examined, Scale Invariant Feature Transform (SIFT) for RGB images and the Gradient kernel descriptor method for depth images. Finally, Support Vector Machine (SVM) and K- nearest neighbor (K-NN) are tested.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.

    • Lingua: Inglese

      Editore: LAP LAMBERT Academic Publishing, 2016

      3659910570 / 9783659910579

      • Brossura
      • Print on Demand

      Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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      Condizione: Nuovo

      EUR 35,90

      EUR 60,57 spedizione 
      Spedito da Germania a U.S.A.

      Quantità: 1 disponibili

      Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - An endeavor has been made with the help of computer vision techniques and the release of Microsoft Kinect camera. The task of segmentation has been implemented using RGB with depth information; to enhance it, YCbCr color scheme is used. Two level feature extraction algorithms are examined, Scale Invariant Feature Transform (SIFT) for RGB images and the Gradient kernel descriptor method for depth images. Finally, Support Vector Machine (SVM) and K- nearest neighbor (K-NN) are tested.