This paper presents a new method for representing the spatial information present in digital grey-tone images. The method is based on using multi-resolution decompositions (MRDs) and Markov random fields (MRFs) concurrently. A given image is represented by a MRD of it, along with an optimally estimated set of Gaussian MRF (GMRF) parameters. Since the GMRF parameters are very small in number, this addition to the usual MRD results in only a small increase in the number of bits in the representation. It is shown, however, that such a minor addition helps when reconstructing the (given) original image from its MRD. Experimental results are presented to illustrate the usefulness of this new method.<>


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-resolution image representation using Markov random fields


    Contributors:
    Lakshmanan, S. (author) / Jain, A.K. (author) / Yu Zhong (author)


    Publication date :

    1994-01-01


    Size :

    440955 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multi-resolution Image Representation using Markov Random Fields

    Lakshmanan, S. / Jain, A. K. / Yu, Z. et al. | British Library Conference Proceedings | 1994



    Bayesian image classification using Markov random fields

    Berthod, M. / Kato, Z. / Yu, S. et al. | British Library Online Contents | 1996


    Markov random fields for vision and image processing

    Blake, Andrew ;Kohli, Pushmeet ;Rother, Carsten | TIBKAT | 2011

    Free access