Markov chain Monte Carlo methods for clustering of image features

M.N.M. Lieshout, van, A.J. Baddeley

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    9 Citations (Scopus)

    Abstract

    The identification of centres of clustering is of interest in many areas of applications, for instance edge detector output has to be grouped into meaningful curves. The authors argue that stochastic geometry models are helpful both in providing models for clustering and as a prior distribution to combat overestimation of the number of clusters and to improve robustness. The general idea in connection with object recognition was proposed by Baddeley and van Lieshout [1993] and van Lieshout [1993]. Independently, in an epidemiological context, a different Gibbs sampler technique for detection of cluster centres in a Cox process was developed by Lawson [1993]
    Original languageEnglish
    Title of host publicationProceedings 5th International Conference on Image Processing and its Applications, Edinburgh, UK, July 4-6, 1995
    Place of PublicationLondon
    PublisherInstitute of Electrical Engineers
    Pages241-245
    ISBN (Print)0-85296-642-3
    DOIs
    Publication statusPublished - 1995

    Publication series

    NameIEE Conference Publication
    Volume410
    ISSN (Print)0537-9989

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