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    Discrete vs. Continuous Orthogonal Moments for Image Analysis

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    12584531_cisst01.pdf (166.2Kb)
    Author
    Mukundan, R.
    Ong, S.H.
    Lee, P.A.
    Date
    2001
    Permanent Link
    http://hdl.handle.net/10092/470

    Image feature representation techniques using orthogonal moment functions have been used in many applications such as invariant pattern recognition, object identification and image reconstruction. Legendre and Zernike moments are very popular in this class, owing to their feature representation capability with a minimal information redundancy measure. This paper presents a comparative analysis between these moments and a new set of discrete orthogonal moments based on Tchebichef polynomials. The implementation aspects of orthogonal moments are discussed, and experimental results using both binary and gray-level images are included to show the advantages of discrete orthogonal moments over continuous moments.

    Subjects
    orthogonal moment functions
     
    Legendre moments
     
    Zernike moments
     
    Tchebichef polynomials
     
    Fields of Research::280000 Information, Computing and Communication Sciences::280200 Artificial Intelligence and Signal and Image Processing::280203 Image processing
     
    Fields of Research::280000 Information, Computing and Communication Sciences::280200 Artificial Intelligence and Signal and Image Processing::280207 Pattern recognition
    Collections
    • Engineering: Conference Contributions [2012]
    Rights
    https://hdl.handle.net/10092/17651

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