• DocumentCode
    2071496
  • Title

    Digital image processing with dynamical neural networks for resource management: theories aspects

  • Author

    Morales-Mendoza, L.J. ; Ibarra-Manzano, O.G. ; Ibarra-Manzano, M.A. ; Shmaliy, Y.

  • Author_Institution
    Fac. de Ingenieria Mecanica Electrica Electronica, Salamanca
  • fYear
    2006
  • fDate
    7-10 Nov. 2006
  • Firstpage
    199
  • Lastpage
    203
  • Abstract
    In this paper, we present the theories aspects of the problem from the reconstruction and enhancing of radar imaging in the natural resource management with detection of specials characteristics. The problem is oriented to the data massive processing involving with recurrent neural networks. The Maximum Entropy Variational Analysis method is implemented into the recurrent neural networks modified Hopfield-type to the reconstruction and, detection and stopping edges (enhancing) in the radar imaging. Furthermore, here we present two forms of the computational implementation of the recurrent neural network.
  • Keywords
    image enhancement; image reconstruction; maximum entropy methods; natural resources; radar imaging; recurrent neural nets; data massive processing; digital image processing; dynamical neural network; image enhancement; image reconstruction; maximum entropy variational analysis method; natural resource management; radar imaging; recurrent neural network; Digital images; Entropy; Image analysis; Image edge detection; Image reconstruction; Neural networks; Radar detection; Radar imaging; Recurrent neural networks; Resource management;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electronics and Photonics, 2006. MEP 2006. Multiconference on
  • Conference_Location
    Guanajuato
  • Print_ISBN
    1-4244-0627-7
  • Electronic_ISBN
    1-4244-0628-5
  • Type

    conf

  • DOI
    10.1109/MEP.2006.335663
  • Filename
    4135747