• DocumentCode
    2890510
  • Title

    Texture characterisation using a novel optimisation formulation for two-dimensional autoregressive modelling and K-means algorithm

  • Author

    Lee, Sarah ; Stathaki, Tania ; Harris, Fred

  • Author_Institution
    Commun. & Signal Processing, Imperial Coll., London, UK
  • Volume
    2
  • fYear
    2003
  • fDate
    9-12 Nov. 2003
  • Firstpage
    1605
  • Abstract
    In this paper, texture characterisation is attempted by two-dimensional autoregressive (AR) modelling, which is achieved by using a proposed constrained optimisation technique followed by the application of the k-means algorithm. The constrained optimisation formulation relates both second and third-order statistical parameters of the image with the AR model formulation. The k-means algorithm is applied to sets of AR model coefficients obtained by the proposed method in block-by-block process. A weighting scheme is then used to calculate the final estimated AR model coefficients.
  • Keywords
    autoregressive processes; image texture; optimisation; K-means algorithm; block-by-block process; constrained optimisation formulation; for two-dimensional autoregressive modelling; second-third-order statistical parameters; texture characterisation; weighting scheme; Artificial intelligence; Constraint optimization; Educational institutions; Equations; Gaussian noise; Image processing; Pixel; Signal processing algorithms; Signal to noise ratio; USA Councils;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on
  • Print_ISBN
    0-7803-8104-1
  • Type

    conf

  • DOI
    10.1109/ACSSC.2003.1292256
  • Filename
    1292256