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Proceedings Paper

A general approach to defect detection in textured materials using a wavelet domain model and level sets
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Paper Abstract

This paper presents a novel approach for defect detection using a wavelet-domain Hidden Markov Tree (HMT)1 model and a level set segmentation technique. The background, which is assumed to contain homogeneous texture, is modeled off-line with HMT. Using this model, a region map of the defect image is produced on-line through likelihood calculations, accumulated in a coarse-to-fine manner in the wavelet domain. As expected, the region map is basically separated into two regions: 1) the defects, and 2) the background. A level-set segmentation technique is then applied to this region map to locate the defects. This approach is tested with images of defective fabric, as well as x-ray images of cotton with trash. The proposed method shows promising preliminary results, suggesting that it may be extended to a more general approach of defect detection.

Paper Details

Date Published: 7 November 2005
PDF: 6 pages
Proc. SPIE 6001, Wavelet Applications in Industrial Processing III, 60010D (7 November 2005); doi: 10.1117/12.633204
Show Author Affiliations
Hung-Yam Chan, Texas Tech. Univ. (United States)
Chaitanya Raju, Texas Tech. Univ. (United States)
Hamed Sari-Sarraf, Texas Tech. Univ. (United States)
Eric F. Hequet, Texas Tech. Univ. (United States)

Published in SPIE Proceedings Vol. 6001:
Wavelet Applications in Industrial Processing III
Frederic Truchetet; Olivier Laligant, Editor(s)

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