A class of discrete multiresolution random fields and its application to image segmentation

Roland Wilson, Chang Tsun Li

    Research output: Contribution to journalArticlepeer-review

    71 Citations (Scopus)

    Abstract

    In this paper, a class of Random Field model, defined on a multiresolution array is used in the segmentation of gray level and textured images. The novel feature of one form of the model is that it is able to segment images containing unknown numbers of regions, where there may be significant variation of properties within each region. The estimation algorithms used are stochastic, but because of the multiresolution representation, are fast computationally, requiring only a few iterations per pixel to converge to accurate results, with error rates of 1-2 percent across a range of image structures and textures. The addition of a simple boundary process gives accurate results even at low resolutions, and consequently at very low computational cost.

    Original languageEnglish
    Pages (from-to)42-56
    Number of pages15
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Volume25
    Issue number1
    DOIs
    Publication statusPublished - Jan 2003

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