• DocumentCode
    541749
  • Title

    K-Means clustering based bi-level coarse image segmentation

  • Author

    Shanmugavadivu, P. ; Shanthasheela, A.

  • Author_Institution
    Dept. of Comput. Sci. & Applic., Gandhigram Rural Inst., Dindigul, India
  • fYear
    2010
  • fDate
    27-29 Dec. 2010
  • Firstpage
    254
  • Lastpage
    259
  • Abstract
    The proposed technique K-Means clustering based bi-level coarse image segmentation performs location-specific segmentation on any given input image and produces the binary image of the object of interest. This technique allows us to interactively define the boundaries of the region of interest (ROI) and produces the coarse image of that ROI. As the segmentation process is confined to the subregions of the given image, this technique promises accuracy and reduced computational time.
  • Keywords
    image segmentation; pattern clustering; statistical analysis; bi-level coarse image segmentation; k-means clustering; location-specific segmentation; region of interest; Active contours; Classification algorithms; Clustering algorithms; Image edge detection; Image segmentation; Kernel; coarse data; image segmentation; interactive segmentation; k-means clustering; unsupervised segmentation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communication and Computational Intelligence (INCOCCI), 2010 International Conference on
  • Conference_Location
    Erode
  • Type

    conf

  • Filename
    5738738
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=541749