• DocumentCode
    642673
  • Title

    Applying Cellular Neural Networks dynamics for image representation

  • Author

    Tang Tang ; Tetzlaff, Ronald

  • Author_Institution
    Fac. of Electr. Eng & Inf. Technol., Tech. Univ. Dresden, Dresden, Germany
  • fYear
    2013
  • fDate
    8-12 Sept. 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    In this paper we discuss in detail the feasibility of implementation and realization of uncoupled Cellular Neural Networks (CNN) systems for image representation. Applying CNN systems for representation of binary image patterns with sparse distribution of points as an example for a possible application is studied here. The test results show a high quality of representation with this method and proved it to be a possible way to implement the proposed CNN structures in practical application.
  • Keywords
    cellular neural nets; image representation; CNN structures; CNN systems; binary image patterns; cellular neural networks dynamics; image representation; sparse distribution; Accuracy; Hamming distance; Image coding; Image representation; Polynomials; Standards; Wavelet coefficients; CNN; Nonlinear dynamical systems; image representation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Circuit Theory and Design (ECCTD), 2013 European Conference on
  • Conference_Location
    Dresden
  • Type

    conf

  • DOI
    10.1109/ECCTD.2013.6662224
  • Filename
    6662224