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Proceedings Paper

Texture analysis using Gabor wavelets
Author(s): Golshah A. Naghdy; Jian Wang; Philip O. Ogunbona
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Paper Abstract

Receptive field profiles of simple cells in the visual cortex have been shown to resemble even- symmetric or odd-symmetric Gabor filters. Computational models employed in the analysis of textures have been motivated by two-dimensional Gabor functions arranged in a multi-channel architecture. More recently wavelets have emerged as a powerful tool for non-stationary signal analysis capable of encoding scale-space information efficiently. A multi-resolution implementation in the form of a dyadic decomposition of the signal of interest has been popularized by many researchers. In this paper, Gabor wavelet configured in a 'rosette' fashion is used as a multi-channel filter-bank feature extractor for texture classification. The 'rosette' spans 360 degrees of orientation and covers frequencies from dc. In the proposed algorithm, the texture images are decomposed by the Gabor wavelet configuration and the feature vectors corresponding to the mean of the outputs of the multi-channel filters extracted. A minimum distance classifier is used in the classification procedure. As a comparison the Gabor filter has been used to classify the same texture images from the Brodatz album and the results indicate the superior discriminatory characteristics of the Gabor wavelet. With the test images used it can be concluded that the Gabor wavelet model is a better approximation of the cortical cell receptive field profiles.

Paper Details

Date Published: 22 April 1996
PDF: 12 pages
Proc. SPIE 2657, Human Vision and Electronic Imaging, (22 April 1996); doi: 10.1117/12.238703
Show Author Affiliations
Golshah A. Naghdy, Univ. of Wollongong (Australia)
Jian Wang, Univ. of Wollongong (Australia)
Philip O. Ogunbona, Univ. of Wollongong (Australia)


Published in SPIE Proceedings Vol. 2657:
Human Vision and Electronic Imaging
Bernice E. Rogowitz; Jan P. Allebach, Editor(s)

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