1 September 1998 Automatic visual inspection of woven textiles using a two-stage defect detector
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Automatic inspection of woven textile fabric is discussed. A two-stage detection process is adopted, with the second stage involving set of novel contextual decision fusion techniques. Three significant problems are addressed: (1) texture feature extraction: Fourier transform features are found to be well matched to the spatially periodic nature of the woven pattern; (2) detection of localized flaw patterns: since prior probabilities are impossible to estimate, and we cannot hope to enumerate all defect classes, a Neyman-Pearson approach is adopted, i.e., flaw detection is via measured deviation from nominal; and (3) detection of extended flaw patterns: the most common flaws are characterized by linear or other cluster shaped patterns; although these are weakly detectable by local detectors, they may be ignored when local detector sensitivity is set to achieve tolerably low false-alarm rates; a localextended contextual decision fusion technique using morphological filtering enables us to achieve very low composite false-alarm rate. The performance of the system is evaluated on samples of denim fabric containing real defects. The predicted composite false-alarm rate is of the order 1 in 1013, or equivalent to 1 per 100 km of fabric roll. Experimental results demonstrate the compatibility of this favorable false-alarm rate with the reliable detection of flaws, which have been chosen for their subtlety and detection difficulty.
Jonathan George Campbell, Fionn D. Murtagh, "Automatic visual inspection of woven textiles using a two-stage defect detector," Optical Engineering 37(9), (1 September 1998). https://doi.org/10.1117/1.601692 . Submission:

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