• DocumentCode
    288884
  • Title

    Contribution of Canny-Deriche filter and artificial neural networks to image segmentation

  • Author

    Koffi, R. ; Solaiman, B. ; Mouchot, M.C.

  • Author_Institution
    Ecole Nat. Superieure des Telecommun. de Bretagne, Brest, France
  • Volume
    6
  • fYear
    1994
  • fDate
    27 Jun- 2 Jul 1994
  • Firstpage
    4044
  • Abstract
    In this paper, an image segmentation method based on an edge detection view is presented. This method uses the contribution of two approaches: the optimal edge detector proposed by J. Canny, and then extended to the optimal recursive filter by R. Deriche; and the artificial neural network approach. Combining these two methods, the hysteresis stage needed in the technique developed by Deriche is avoided without damaging the segmentation result. Therefore, thresholds required in hysteresis phase and, which are usually quite difficult to find are no more needed. Experimental results show the validity of this method
  • Keywords
    edge detection; image segmentation; neural nets; recursive filters; Canny-Deriche filter; artificial neural networks; edge detection; image segmentation; optimal edge detector; optimal recursive filter; Artificial neural networks; Detectors; Filtering; Finite impulse response filter; Humans; Hysteresis; IIR filters; Image edge detection; Image segmentation; Signal to noise ratio;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    0-7803-1901-X
  • Type

    conf

  • DOI
    10.1109/ICNN.1994.374861
  • Filename
    374861