Shift- and rotation-invariant interpattern heteroassociation model

Francis T. Yu, Chii Maw Uang, Shizhuo Yin

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    A shift and rotation invariant neural network using interpattern hetero association (IHA) model is illustrated. To preserve the shift and rotation invariant properties, a set of binarized-encoded circular harmonic expansion (CHE) function at the Fourier domain is used as the training set. The interconnection weight matrix is constructed using an IHA model. By using the shift and symmetric properties of the modulus Fourier spectral, the problem of centering the CHE functions can be avoided. Computer simulations and experimental demonstrations are provided in which we have shown that the shift and rotation invariant properties of the proposed IHA neural net are indeed preserved.

    Original languageEnglish (US)
    Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
    EditorsDavid P. Casasent
    PublisherPubl by Society of Photo-Optical Instrumentation Engineers
    Number of pages10
    ISBN (Print)0819411957
    StatePublished - Dec 1 1993
    EventOptical Pattern Recognition IV - Orlando, FL, USA
    Duration: Apr 13 1993Apr 14 1993

    Publication series

    NameProceedings of SPIE - The International Society for Optical Engineering
    ISSN (Print)0277-786X


    OtherOptical Pattern Recognition IV
    CityOrlando, FL, USA

    All Science Journal Classification (ASJC) codes

    • Electronic, Optical and Magnetic Materials
    • Condensed Matter Physics
    • Computer Science Applications
    • Applied Mathematics
    • Electrical and Electronic Engineering


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