• DocumentCode
    1742205
  • Title

    Unsupervised segmentation of Poisson data

  • Author

    Nowak, Robert D. ; Figueiredo, Mario A.T.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA
  • Volume
    3
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    155
  • Abstract
    Describes an approach to the analysis of Poisson point processes, in time (1D) or space (2D), which is based on the minimum description length (MDL) framework. Specifically, we describe a fully unsupervised recursive segmentation algorithm for 1D and 2D observations. Experiments illustrate the good performance of the proposed methods
  • Keywords
    encoding; maximum likelihood estimation; stochastic processes; 1D observations; 2D observations; Poisson data; Poisson point processes; minimum description length; unsupervised segmentation; Bayesian methods; Biomedical imaging; Electron microscopy; Electronic mail; Image segmentation; Maximum likelihood decoding; Maximum likelihood estimation; Physics; Statistics; Telecommunications;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition, 2000. Proceedings. 15th International Conference on
  • Conference_Location
    Barcelona
  • ISSN
    1051-4651
  • Print_ISBN
    0-7695-0750-6
  • Type

    conf

  • DOI
    10.1109/ICPR.2000.903508
  • Filename
    903508
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1742205