A 3-D Contextual Classifier |
Rasmus Larsen
|
| Abstract | In this paper we will consider an extension of the Bayesian 2-D contextual class
ification
routine developed by Owen, Hjort \& 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.\ neighbouring pixels tend to be of the
same class.
The algorithm developed by Owen, Hjort \& 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. |
| Keywords | Classification, Segmentation, Contextual methods |
| Type | Conference paper [With referee] |
| Conference | Proceedings of the 7th Scandinavian Conference on Image Analysis (SCIA'97) |
| Editors | Michael Frydrych and Jussi Parkkinen and Ari Visa |
| Year | 1997 Month June pp. 9--12 |
| Address | Lappeenranta, Finland |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Image Analysis & Computer Graphics |