• DocumentCode
    2564906
  • Title

    Fast image segmentation method based on threshold

  • Author

    Xu-dong Tang ; Yong-jie Pang ; He Zhang ; Wei Zhu

  • Author_Institution
    Underwater Vehicle Technol. Key Lab., Harbin Eng. Univ., Harbin
  • fYear
    2008
  • fDate
    2-4 July 2008
  • Firstpage
    3281
  • Lastpage
    3285
  • Abstract
    Image segmentation is a key part in image processing field Though traditional threshold methods based on maximum entropy principle could sometimes divide the image into object and background, the size of search space increase very rapidly when the number of parameters needed to determine the membership function increase, so itpsilas time-consuming is often an obstacle. In this paper, based on the traditional threshold methods, the membership function is simplified and a new recursive scheme is introduced which decreases the computation complexity of the traditional method. The approach does not need the calculation of the membership function and increases the calculated speed. The experiments prove this novel approach is not only segment accuracy, but also decrease the computation time. Comparing with the traditional methods, the new method shows better real timing and noise restraining performance.
  • Keywords
    computational complexity; image segmentation; recursive estimation; computation complexity; fast image segmentation method; image processing field; maximum entropy principle; noise restraining performance; Automotive engineering; Entropy; Fuzzy control; Helium; Image processing; Image segmentation; Space technology; Timing; Underwater vehicles; Fuzzy Entropy; Image Segmentation; Membership Function; Recursive Algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference, 2008. CCDC 2008. Chinese
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-1733-9
  • Electronic_ISBN
    978-1-4244-1734-6
  • Type

    conf

  • DOI
    10.1109/CCDC.2008.4597936
  • Filename
    4597936