• DocumentCode
    1741522
  • Title

    Two dimensional Volterra parameter estimation using a zero tolerance optimisation formulation

  • Author

    Stathakis, Georgios ; Stathaki, Tania

  • Author_Institution
    Dept. of Electr. & Electron. Eng., Imperial Coll. of Sci., Technol. & Med., London, UK
  • Volume
    1
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    288
  • Abstract
    This paper forms a part of a series of previous studies we have undertaken, where the problem of nonlinear textured image modeling is examined. We assume that the observed “output” image is derived from a Volterra filter that is driven by a Gaussian input. Both the filter parameters and the input signal are unknown and therefore the problem can be classified as blind or unsupervised in nature. In the statistical approach to the solution of the above problem we seek for equations that relate the unknown parameters of the Volterra model with the statistical parameters of the “output” image to be modeled. These equations are highly nonlinear and their solution is achieved through a novel constrained optimisation formulation
  • Keywords
    Gaussian processes; Volterra series; filtering theory; image texture; optimisation; parameter estimation; statistical analysis; 2D Volterra parameter estimation; Gaussian input; Volterra filter; Volterra model; Volterra series; blind problem; constrained optimisation; filter parameters; input signal; nonlinear equations; nonlinear textured image modeling; output image; statistical approach; statistical parameters; unsupervised problem; zero tolerance optimisation; Constraint optimization; Educational institutions; Gaussian processes; Kernel; Mathematical model; Nonlinear equations; Nonlinear filters; Parameter estimation; Signal processing; Statistical analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing, 2000. Proceedings. 2000 International Conference on
  • Conference_Location
    Vancouver, BC
  • ISSN
    1522-4880
  • Print_ISBN
    0-7803-6297-7
  • Type

    conf

  • DOI
    10.1109/ICIP.2000.900951
  • Filename
    900951