• DocumentCode
    2762899
  • Title

    Unsupervised texture segmentation using stochastic version of the EM algorithm and data fusion

  • Author

    Cruz, Carlos Avils

  • Author_Institution
    Dept. de Electr., Univ. Autonoma Metropolitana, San Pablo, Mexico
  • Volume
    2
  • fYear
    1998
  • fDate
    16-20 Aug 1998
  • Firstpage
    1005
  • Abstract
    In this paper I present a new methodology for texture segmentation. This methodology is based through the high order statistics features, the data fusion techniques and finally though the maximum likelihood method in order to find the clusters. The methodology is applied in order to segment natural micro-textures
  • Keywords
    higher order statistics; image segmentation; image texture; maximum likelihood estimation; sensor fusion; clusters; data fusion; expectation maximisation; high-order statistics features; maximum likelihood method; natural micro-texture segmentation; stochastic EM algorithm; unsupervised texture segmentation; Character recognition; Delay estimation; Feature extraction; Frequency domain analysis; Parallel architectures; Radar; Robots; Robustness; Statistics; Stochastic processes;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
  • Conference_Location
    Brisbane, Qld.
  • ISSN
    1051-4651
  • Print_ISBN
    0-8186-8512-3
  • Type

    conf

  • DOI
    10.1109/ICPR.1998.711859
  • Filename
    711859
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2762899