We present a practical pattern recognition system that is invariant with respect to translation, scale and rotation of objects. The system is also insensitive to large variations of the threshold used. As feature vectors, Zernike moments are used and we compare them with Hu's seven moment invariants. For a practical machine vision system, three key issues are discussed: pattern normalization, fast computation of Zernike moments, and classification using k-NN rule. As testing results, the system recognizes a set of 62 alphanumeric machine-printed characters with different sizes, at arbitrary orientations, and with different thresholds where the size of the characters varies from 10/spl times/10 to 512/spl times/512 pixels.<>


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A practical pattern recognition system for translation, scale and rotation invariance


    Beteiligte:
    Whoi-Yul Kim (Autor:in) / Po Yuan (Autor:in)


    Erscheinungsdatum :

    01.01.1994


    Format / Umfang :

    503023 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A Practical Pattern Recognition System for Translation, Scale, and Rotation Invariance

    Kim, W.-Y. / Yuan, P. / Institute of Electrical and Electronics Engineers; Computer Society | British Library Conference Proceedings | 1994



    Pattern recognition method is rotation-invariant

    British Library Online Contents | 2001


    Overcomplete steerable pyramid filters and rotation invariance

    Greenspan / Belongie / Goodman et al. | IEEE | 1994


    A Neural Architecture for Pattern Recognition Insensitive to Translation, Scale, and Line Thickness

    Fukumi, M. / Omatu, S. / Nishikawa, Y. | British Library Online Contents | 1994