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Proceedings Paper

One-dimensional Fourier transform coefficients for rotation-invariant texture classification
Author(s): Hamzah Arof; Farzin Deravi
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Paper Abstract

This paper introduces a texture descriptor that is invariant to rotation. The new texture descriptor utilizes the property of the magnitudes of Fourier transform coefficients that do not change with spatial shift of input elements. Since rotating an image by an arbitrary angle does not change pixel intensities in an image but shift them in circular motion, the notion of producing textural features invariant to rotation using 1D Fourier transform coefficients can be realized if the relationship between circular motion and spatial shift can be established. By analyzing pixels in a circular neighborhood in an image, a number of FOurier transform coefficients can be generated to describe local properties of the neighborhood. From the magnitudes of these coefficients, several rotation invariant features are obtained to represent each texture class. Based on these features, an unknown image is assigned to one of the known classes using a nearest neighbor classifier. All of the feature samples for the classifier are extracted from unrotated texture images only. The new texture descriptor outperformed the circular simultaneous autoregressive model in classifying rotated texture images taken from 30 texture classes.

Paper Details

Date Published: 31 October 1996
PDF: 8 pages
Proc. SPIE 2908, Machine Vision Applications, Architectures, and Systems Integration V, (31 October 1996); doi: 10.1117/12.257258
Show Author Affiliations
Hamzah Arof, Univ. of Wales Swansea (United Kingdom)
Farzin Deravi, Univ. of Wales Swansea (United Kingdom)


Published in SPIE Proceedings Vol. 2908:
Machine Vision Applications, Architectures, and Systems Integration V
Susan Snell Solomon; Bruce G. Batchelor; Frederick M. Waltz, Editor(s)

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