• DocumentCode
    2705383
  • Title

    Image segmentation algorithm based on swarm intelligence technology

  • Author

    Hui-jie Sun

  • Author_Institution
    Coll. of Comput. Sci. & Inf. Eng., Harbin Normal Univ., Harbin, China
  • fYear
    2015
  • fDate
    17-18 Jan. 2015
  • Firstpage
    68
  • Lastpage
    71
  • Abstract
    Image segmentation is one of the key technologies in image processing, image segmentation quality relates to subsequent processing directly such as image measurement and image recognition, etc. This paper presents a new intelligent optimization algorithm: (artificial fish swarm algorithm, artificial bacterial swarm algorithm and artificial bee colony swarm algorithm), a new method of image segmentation, namely the wavelet transform for segmented images, combined with gray-scale morphology and rough sets theory to solve the problem of image noise, uses a new intelligent optimization algorithm to improve the effect of segmentation, the segmentation performance better, faster, and has important theoretical significance and practical value.
  • Keywords
    ant colony optimisation; image segmentation; rough set theory; swarm intelligence; wavelet transforms; artificial bacterial swarm algorithm; artificial bee colony swarm algorithm; artificial fish swarm algorithm; gray-scale morphology; image measurement; image noise; image processing; image recognition; image segmentation algorithm; image segmentation quality; intelligent optimization algorithm; rough sets theory; swarm intelligence technology; wavelet transform; Classification algorithms; Image recognition; Image segmentation; Manganese; Optimization; Sun; artificial fish swarm algorithm; image segmentation; rough sets; swarm intelligence;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computing and Internet of Things (ICIT), 2014 International Conference on
  • Conference_Location
    Harbin
  • Print_ISBN
    978-1-4799-7533-4
  • Type

    conf

  • DOI
    10.1109/ICAIOT.2015.7111540
  • Filename
    7111540