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Proceedings Paper

Supervised texture segmentation using DT-CWT and a modified k-NN classifier
Author(s): Brian W. Ng; Abdesselam Bouzerdoum
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Paper Abstract

Texture segmentation has been an important problem in image processing. Filtering approaches have been popular, and recent studies have indicated a need for efficient, low- complexity algorithms. In this paper, we present a texture segmentation scheme based on the Dual-Tree Complex Wavelet Transform (DT-CWT). The advantage of the DT-CWT over other approaches is that it offers a partially redundant representation with strong directionality. The texture segmentation scheme presented here consists of three steps: feature extraction, conditioning, and classification. A number of feature smoothing windows have been tested. Classification is performed using a modified K-NN clustering algorithm. The proposed scheme consistently achieves error rates of less than 10%.

Paper Details

Date Published: 30 May 2000
PDF: 9 pages
Proc. SPIE 4067, Visual Communications and Image Processing 2000, (30 May 2000); doi: 10.1117/12.386707
Show Author Affiliations
Brian W. Ng, Univ. of Adelaide (Australia)
Abdesselam Bouzerdoum, Edith Cowan Univ. (Australia)

Published in SPIE Proceedings Vol. 4067:
Visual Communications and Image Processing 2000
King N. Ngan; Thomas Sikora; Ming-Ting Sun, Editor(s)

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