• DocumentCode
    1991272
  • Title

    An Unsupervised Image Segmentation Using B-Splines Functions

  • Author

    Hadrich, Atizez ; Zribi, Mourad ; Masmoudi, Afif

  • Author_Institution
    Lab. of Probability & Stat., Fac. of Sci. of Sfax, Sfax, Tunisia
  • fYear
    2012
  • fDate
    27-30 May 2012
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    We propose in this paper an unsupervised Bayesian image segmentation based on non-parametric expectation-maximization (EM) algorithm. The non-parametric aspect comes from the use of the B-spline probability density function (pdf) estimation, which is reduced to the estimation of the parameters of B-splines of the pdf.
  • Keywords
    Bayes methods; expectation-maximisation algorithm; image segmentation; probability; B-spline functions; B-spline parameter estimation; B-spline probability density function estimation; nonparametric EM algorithm; nonparametric expectation-maximization algorithm; unsupervised Bayesian image segmentation; Bayesian methods; Estimation; Histograms; Image segmentation; Kernel; Probability density function; Splines (mathematics);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering and Technology (S-CET), 2012 Spring Congress on
  • Conference_Location
    Xian
  • Print_ISBN
    978-1-4577-1965-3
  • Type

    conf

  • DOI
    10.1109/SCET.2012.6342064
  • Filename
    6342064
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1991272