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

Hung-Yam Chan; Chaitanya Raju; Hamed Sari-Sarraf; Eric F. Hequet

doi:10.1117/12.633204

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7 November 2005 A general approach to defect detection in textured materials using a wavelet domain model and level sets

Hung-Yam Chan, Chaitanya Raju, Hamed Sari-Sarraf, Eric F. Hequet

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Proceedings Volume 6001, Wavelet Applications in Industrial Processing III; 60010D (2005) https://doi.org/10.1117/12.633204
Event: Optics East 2005, 2005, Boston, MA, United States

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.

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Hung-Yam Chan, Chaitanya Raju, Hamed Sari-Sarraf, and Eric F. Hequet "A general approach to defect detection in textured materials using a wavelet domain model and level sets", Proc. SPIE 6001, Wavelet Applications in Industrial Processing III, 60010D (7 November 2005); https://doi.org/10.1117/12.633204

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