• DocumentCode
    1677109
  • Title

    Displacement vector estimation with cellular neural networks

  • Author

    Feiden, Dirk ; Tetzlaff, Ronald

  • Author_Institution
    Inst. of Appl. Phys., Frankfurt Univ., Germany
  • Volume
    3
  • fYear
    2002
  • fDate
    6/24/1905 12:00:00 AM
  • Firstpage
    2049
  • Lastpage
    2052
  • Abstract
    Displacement vector estimation is one of the open key problems in computer vision and video coding. For example, in computer vision, displacement vector estimation is usually the basis of some kind of motion estimation. Unfortunately, displacement vector estimation using statistical methods is always computationally complex, which might be a restriction in real-time processing. In this paper, we show that displacement vector estimation can be efficiently performed by using cellular neural networks (CNNs). In order to find CNN templates, therefore, we have used the new optimization method of iterative annealing
  • Keywords
    cellular neural nets; computational complexity; computer vision; estimation theory; iterative methods; motion estimation; real-time systems; simulated annealing; vectors; video coding; cellular neural networks; computational complexity; computer vision; displacement vector estimation; iterative annealing; motion estimation; optimization method; real-time processing; statistical methods; templates; video coding; Cellular neural networks; Computer vision; Couplings; Motion estimation; Neural networks; Object detection; Optimization methods; Physics; Polynomials; Video coding;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
  • Conference_Location
    Honolulu, HI
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7278-6
  • Type

    conf

  • DOI
    10.1109/IJCNN.2002.1007455
  • Filename
    1007455