In this paper, we describe a wavelet-based approach to multiresolution stochastic image modeling. The basic idea here is that a complex random field, e.g., one with long range and nonlinear spatial correlations, can be decomposed into several less complex random fields. This is done by defining a random field in each resolution of a wavelet expansion. Experiments, performed for the multiresolution AR (autoregressive) and RBF (radial basis function) models, have produced promising results. Specifically, the wavelet-AR model captures long range correlation better than the single resolution AR model, and for both the wavelet AR and RBF models, random fields in the wavelet domain do appear to be simpler to model than those on the finest resolution.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Wavelet-based multiresolution stochastic image models


    Beteiligte:
    Jun Zhang (Autor:in) / Que Tran (Autor:in)


    Erscheinungsdatum :

    01.01.1995


    Format / Umfang :

    795942 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Wavelet-based Multiresolution Stochastic Image Models

    Zhang, J. / Tran, Q. / IEEE; Computer Society; Technical Committee for Pattern Analysis and Machine Intelligence | British Library Conference Proceedings | 1995


    Efficient Image Coding Using Multiresolution Wavelet Transform and Vector Quantization

    Pemmaraju, S. / Mitra, S. / IEEE; Signal Processing Society | British Library Conference Proceedings | 1996



    A wavelet-based multiresolution edge detection and tracking

    Shih, M. Y. / Tseng, D. C. | British Library Online Contents | 2005


    Review of industrial applications of wavelet and multiresolution-based signal and image processing

    Truchetet, F. / Laligant, O. | British Library Online Contents | 2008