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

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations

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.

Original languageEnglish
Article number60010D
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume6001
DOIs
StatePublished - 2005
EventWavelet Applications in Industrial Processing III - Boston, MA, United States
Duration: Oct 24 2005Oct 24 2005

Keywords

  • Defect detection
  • Level set
  • Segmentation
  • Wavelet

Fingerprint

Dive into the research topics of 'A general approach to defect detection in textured materials using a wavelet domain model and level sets'. Together they form a unique fingerprint.

Cite this