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Proceedings Paper

Optimal filtering scheme for unsupervised texture feature extraction
Author(s): Trygve Randen; Vidar Alvestad; John Hakon Husoy
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Paper Abstract

In this paper a technique for unsupervised optimal feature extraction and segmentation for textured images is presented. The image is first divided into cells of equal size, and similarity measures on the autocorrelation functions for the cells are estimated. The similarity measures are used for clustering the image into clusters of cells with similar textures. Autocorrelation estimates for each cluster are then estimated, and two-dimensional texture feature extractors using filters, optimal with respect to the Fisher criterion, are constructed. Further, a model for the feature response at and near the texture borders is developed. This model is used to estimate whether the positions of the detected edges in the image are biased, and a scheme for correcting such bias using morphological dilation is devised. The article is concluded with experimental results for the proposed unsupervised texture segmentation scheme.

Paper Details

Date Published: 27 February 1996
PDF: 12 pages
Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); doi: 10.1117/12.233260
Show Author Affiliations
Trygve Randen, Hogskolen i Stavanger (Norway)
Vidar Alvestad, Hogskolen i Stavanger (Norway)
John Hakon Husoy, Hogskolen i Stavanger (Norway)


Published in SPIE Proceedings Vol. 2727:
Visual Communications and Image Processing '96
Rashid Ansari; Mark J. T. Smith, Editor(s)

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