IMM technical report 16/97:
3-D contextual bayesian classifiers
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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
The new 3-D algorithms are tested on a synthetic 3-D multivariate dataset.
Keywords: Classification, Segmentation, 3-D, Contextual methods
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Last modified October 9, 1997
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