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.
|Journal||Proceedings of SPIE - The International Society for Optical Engineering|
|State||Published - 2005|
|Event||Wavelet Applications in Industrial Processing III - Boston, MA, United States|
Duration: Oct 24 2005 → Oct 24 2005
- Defect detection
- Level set