@TECHREPORT\{IMM1997-01139,
    author       = "R. Larsen",
    title        = "{3-D} contextual Bayesian classifiers",
    year         = "1997",
    keywords     = "Classification, Segmentation, {3-D,} Contextual methods",
    number       = "",
    series       = "",
    institution  = "Dept. of Mathematical Modelling, Technical University of Denmark, {DTU}",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    type         = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/1139-full.html",
    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.",
    isbn_issn    = "IMM-REP-97-16"
}