3-D contextual Bayesian classifiers

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    Abstract

    In this paper we will consider extensions of a series of Bayesian 2-D contextual classification pocedures proposed by Owen (1984) Hjort & Mohn (1984) and Welch & Salter (1971) and Haslett (1985) to 3 spatial dimensions. It is evident that compared to classical pixelwise classification further information can be obtained by taking into account the spatial structure of image data. The 2-D algorithms mentioned above consist of basing the classification of a pixel on the simultaneous distribution of the values of a pixel and its four nearest neighbours. This includes the specification of a Gaussian distribution for the pixel values as well as a prior distribution for the configuration of class variables within the cross that is made of a pixel and its four nearest neighbours. We will extend these algorithms to 3-D, i.e. we will specify a simultaneous Gaussian distribution for a pixel and its 6 nearest 3-D neighbours, and generalise the class variable configuration distributions within the 3-D cross given in 2-D algorithms. The new 3-D algorithms are tested on a synthetic 3-D multivariate dataset.
    Original languageEnglish
    PublisherDepartment of Mathematical Modelling, Technical University of Denmark
    Publication statusPublished - 1997

    Keywords

    • Classification
    • Segmentation
    • Contextual methods
    • 3-D

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