• DocumentCode
    557750
  • Title

    The research on segmentation of complex object

  • Author

    Wang, Xiaofei ; Pang, Quan

  • Author_Institution
    Inst. of Biol. Eng. & Instrum., Hang Zhou Dianzi Univ., Hangzhou, China
  • Volume
    3
  • fYear
    2011
  • fDate
    15-17 Oct. 2011
  • Firstpage
    1277
  • Lastpage
    1281
  • Abstract
    It is difficult to use the traditional image segmentation method to segment the complex object whose boundary is blurred from the background. The active contour model, based on level set, is widely used in recent years and selected to segment this type of object in this paper. To increase the speed of segmentation, the fast active contour model without need to solve partial differential equation is used in this paper. This paper proposes a new way of thinking about designing the data-dependent speed function in the fast active model and gives the corresponding general speed function. The probability estimate theory and the conventional CV model are introduced into the data-dependent speed function, and then this paper proposes two novel fast models which can be called histogram-based fast model and CV-based fast model. The two models are studied both theoretically and experimentally. Finally a conclusion can be drawn that the histogram-based fast model is applied to segment the complex object from the background.
  • Keywords
    edge detection; image segmentation; partial differential equations; probability; CV-based fast model; active contour; blurred background; complex object segmentation; data-dependent speed function; histogram-based fast model; level set; partial differential equation; probability estimate theory; Active contours; Biological system modeling; Computational modeling; Estimation; Image segmentation; Mathematical model; Switches; CV model; blurred boundary; complex object; fast active contour model; probability estimate;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image and Signal Processing (CISP), 2011 4th International Congress on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4244-9304-3
  • Type

    conf

  • DOI
    10.1109/CISP.2011.6100429
  • Filename
    6100429