One of the most important problems in computer vision and image processing is signal representation and shape description. This usually involves mapping the signal from an original domain to a set of coefficients describing feature contents of the signal. These methods can be classified in two, global and local, categories. Global methods are sensitive to inaccurate segmentation and masking effects. Local methods, on the other hand, are better in partial pattern recognition but are generally application-dependent. Generalized pattern spectrum (GPS) is a local method and it has proved to be a robust shape descriptor which can also be used in partial pattern recognition. We have investigated the classification properties of this mapping on both noisy and partially missed objects. The results show that it is superior to the pecstrum method in the case of partially missed patterns.<>


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Generalized morphological pattern spectrum for classification


    Beteiligte:


    Erscheinungsdatum :

    01.01.1994


    Format / Umfang :

    468277 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Generalized Morphological Pattern Spectrum for Classification

    Khorsandi, S. / Venetsanopoulos, A. N. / IEEE; Signal Processing Society | British Library Conference Proceedings | 1994


    Generalized Morphological Pattern Spectrum, Its Classification and Coding Properties

    Khorsandi, S. / Venetsanopoulos, A. N. / IEEE et al. | British Library Conference Proceedings | 1994




    Pattern Classification Based upon Multivalue Morphological Segmentation Tools

    Sbihi, A. / Postaire, J. G. / Touzani, A. et al. | British Library Conference Proceedings | 1994