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Parameterisation of slant-Haar transforms

Parameterisation of slant-Haar transforms

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  • Author(s): S. Agaian 1 ; K. Tourshan 2 ; J.P. Noonan 3
    • View affiliations
    • Affiliations: 1: Electrical Engineering Department, University of Texas at San Antonio, USA
      2: Electrical Engineering Department, Lockheed Martin NE & SS Surface Systems, Moorestown, USA
      3: Electrical Engineering and Computer Science Department, Tufts University, Medford, USA
  • Source: Volume 150, Issue 5, October 2003, p. 306 – 311
    DOI:  10.1049/ip-vis:20030522 , Print ISSN 1350-245X, Online ISSN 1359-7108
IEE
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A parameterisation of the slant-Haar transform is presented, which includes an existing version of the slant-Haar transform. An efficient algorithm for the slant-Haar transform is developed and its computational complexity is estimated. The parametric slant-Haar transforms are compared to the Karhunen–Loeve transform. The parametric slant-Haar is shown to perform better than the commonly used slant-Haar and slant-Hadamard transforms for the first-order Markov model and also performs better than the discrete cosine transform for images approximated by the generalised image correlation model.

Inspec keywords: parameter estimation; Markov processes; image processing; computational complexity; correlation methods; Haar transforms

Other keywords: first-order Markov model; parameterisation; generalised image correlation model; computational complexity; slant-Haar transform

Subjects: Optical, image and video signal processing; Signal processing theory; Integral transforms in numerical analysis; Markov processes; Integral transforms in numerical analysis; Computer vision and image processing techniques; Markov processes

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