@CONFERENCE\{IMM1997-0220,
    author       = "R. Larsen",
    title        = "A {3-D} Contextual Classifier",
    year         = "1997",
    month        = "jun",
    keywords     = "Classification, Segmentation, Contextual methods",
    pages        = "9--12",
    booktitle    = "Proceedings of the 7th Scandinavian Conference on Image Analysis (SCIA'97)",
    volume       = "",
    series       = "",
    editor       = "Michael Frydrych and Jussi Parkkinen and Ari Visa",
    publisher    = "",
    organization = "",
    address      = "Lappeenranta, Finland",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/220-full.html",
    abstract     = "In this paper we will consider an extension of the Bayesian {2-D} contextual class
ification
routine developed by Owen, Hjort \$\backslash\$\& Mohn to 3 spatial dimensions. It is evident
that compared
to classical pixelwise classification further information can be obtained by tak
ing into account
the spatial structure of image data, i.e.\$\backslash\$ neighbouring pixels tend to be of the
 same class.
The algorithm  developed by Owen, Hjort \$\backslash\$\& Mohn  consists of basing the classifi
cation of a pixel
on the simultaneous distribution of the values of a pixel and its four nearest n
eighbours.
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 m
ade
of a pixel and its four nearest neighbours.  We will
extend this algorithm to {3-D,} i.e. we will specify a simultaneous Gaussian distr
ibution
for a pixel and its 6 nearest {3-D} neighbours, and generalise the class
variable configuration distribution within the {3-D} cross.
The algorithm is tested on a synthetic {3-D} multivariate dataset."
}