• DocumentCode
    1710429
  • Title

    Real-time hand postures recognition using low computational complexity Artificial Neural Networks and Support Vector Machines

  • Author

    Bragatto, Ticiano A C ; Ruas, Gabriel S I ; Lamar, Marcus V.

  • Author_Institution
    Dept. of Electr. Eng., Brasilia Univ., Brasilia
  • fYear
    2008
  • Firstpage
    1530
  • Lastpage
    1535
  • Abstract
    This paper proposes two main techniques for reduce computational complexity on artificial neural networks, using piecewise linear activation function, and support vector machines built on a probability based binary tree. These methods are compared with well-known classifiers based on the computational complexity, correct rate and time taken to process the required information. The results show that probability based binary tree SVM has an equivalent recognition rate and is faster than ANNs.
  • Keywords
    computational complexity; neural nets; pose estimation; probability; support vector machines; trees (mathematics); artificial neural network; binary tree; low computational complexity; piecewise linear activation function; probability; real-time hand posture recognition; support vector machine; Artificial neural networks; Binary trees; Computational complexity; Feature extraction; Fingers; Handicapped aids; Humans; Real time systems; Support vector machine classification; Support vector machines; Artificial Neural Networks; Hand Posture Recognition; Support Vector Machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications, Control and Signal Processing, 2008. ISCCSP 2008. 3rd International Symposium on
  • Conference_Location
    St Julians
  • Print_ISBN
    978-1-4244-1687-5
  • Electronic_ISBN
    978-1-4244-1688-2
  • Type

    conf

  • DOI
    10.1109/ISCCSP.2008.4537470
  • Filename
    4537470