• DocumentCode
    2251631
  • Title

    Estimating the CNN Steady State using Forward-Backward Recursions

  • Author

    Shi, Bertram E.

  • Author_Institution
    Dept. of Electron. & Comput. Eng., Hong Kong Univ. of Sci. & Technol., Kowloon
  • fYear
    2006
  • fDate
    28-30 Aug. 2006
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    We describe a technique by which the steady state solution of some CNN template operations can be approximated in a few passes over the input image. For digital implementations, this is much more efficient than numerical integration of the CNN differential equations. In particular, we show that the steady state solution to linear 1D CNN filtering operations can be found exactly in two passes over the input image using the forward-backward filtering technique commonly used in digital signal processing to obtain zero phase linear filters. We then show that this technique can be modified to find approximate solutions to some nonlinear CNN operations, using the example of a local winner-take-all network
  • Keywords
    cellular neural nets; differential equations; filtering theory; image processing; CNN differential equations; CNN template operations; cellular neural networks steady state; digital signal processing; forward backward recursions; forward-backward filtering; image processing; linear 1D CNN filtering; Analog circuits; Cellular neural networks; Digital circuits; Digital filters; Equations; Nonlinear filters; Recursive estimation; State estimation; State feedback; Steady-state; analog and digital systems; cellular neural networks; image processing; recursive digital filters;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cellular Neural Networks and Their Applications, 2006. CNNA '06. 10th International Workshop on
  • Conference_Location
    Istanbul
  • Print_ISBN
    1-4244-0639-0
  • Electronic_ISBN
    1-4244-0640-4
  • Type

    conf

  • DOI
    10.1109/CNNA.2006.341623
  • Filename
    4145863