• DocumentCode
    2400460
  • Title

    Feature extraction approach to blind source separation

  • Author

    Lin, Juan K. ; Grier, David G. ; Cowan, Jack D.

  • Author_Institution
    Dept. of Phys., Chicago Univ., IL, USA
  • fYear
    1997
  • fDate
    24-26 Sep 1997
  • Firstpage
    398
  • Lastpage
    405
  • Abstract
    Local independent component analysis is formulated as a task involving the extraction of local geometric structure in the joint distribution. Because the geometrical structure of statistical independence is not well captured by statistical descriptions such as moments and cumulants, we use feature detection tools from image analysis to locate the local independent component coordinate system. The resulting approach to source separation can be implemented in real time using conventional image analysis hardware. The generality of this approach is demonstrated by blind source separation of multi-modal sources, and the pseudo-separation of three sources from two mixtures
  • Keywords
    Hough transforms; feature extraction; geometry; probability; blind source separation; feature detection; feature extraction approach; local geometric structure; local independent component analysis; multi-modal sources; pseudo-separation; statistical independence; Blind source separation; Computer vision; Equations; Feature extraction; Histograms; Image analysis; Independent component analysis; Partitioning algorithms; Physics; Source separation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
  • Conference_Location
    Amelia Island, FL
  • ISSN
    1089-3555
  • Print_ISBN
    0-7803-4256-9
  • Type

    conf

  • DOI
    10.1109/NNSP.1997.622421
  • Filename
    622421