• DocumentCode
    2869759
  • Title

    Image segmentation through using the evidence theory based data fusion technique

  • Author

    Dromigny-Badin, A. ; Zhu, Y.M. ; Gimenez, G. ; Goutte, R.

  • Author_Institution
    INSA, CNRS, Villeurbanne, France
  • Volume
    2
  • fYear
    1998
  • fDate
    1998
  • Firstpage
    994
  • Abstract
    An image segmentation method is presented that is based on a pixel-level data fusion technique employing the evidence theory of Dempster-Shafer (DS). A probability mass being defined for each gray level, each couple of pixels is combined through Dempster´s combination rule and a look-up fusion table. The segmentation procedure is iterative, and the determination of probability mass is automatic. The proposed method is illustrated with the aid of both simulations and examples on physical images. The obtained results show the interest of exploiting multiple information for image segmentation
  • Keywords
    case-based reasoning; image segmentation; iterative methods; probability; sensor fusion; table lookup; Dempster-Shafer theory; combination rule; evidence theory; gray level; image segmentation; iterative procedure; look-up table; pixel-level data fusion; probability mass; simulations; Biomedical imaging; Histograms; Image fusion; Image segmentation; Industrial control; Magnetic resonance imaging; Optical imaging; Pixel; Ultrasonic imaging; X-rays;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing Proceedings, 1998. ICSP '98. 1998 Fourth International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    0-7803-4325-5
  • Type

    conf

  • DOI
    10.1109/ICOSP.1998.770781
  • Filename
    770781