Fabric Defect Detection Using a Hybrid and Complementary Fractal Feature Vector and FCM-based Novelty Detector
Journal Title: Fibres and Textiles in Eastern Europe - Year 2017, Vol 25, Issue 6
Abstract
Automated detect detection in woven fabrics for quality control is still a challenging novelty detection problem. This work presents five novel fractal features based on the box-counting dimension to address the novelty detection of fabric defect. Making use of the formation of woven fabric, the fractal features are extracted in a one-dimension series obtained by projecting a fabric image along the warp and weft directions, where their complementarity in discriminating defects is taken into account. Furthermore a new novelty detector based on fuzzy c-means (FCM) is devised to deal with one-class classification of the features extracted. Finally, by jointly applying the features proposed and the FCM based novelty detector, we evaluate the method proposed for eight datasets with different defects and textures, where satisfying results are achieved with a low overall missing detection rate.
Authors and Affiliations
Jian Zhou, Jun Wang, Honggang Bu
Keywords
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- EP ID EP228984
- DOI 10.5604/01.3001.0010.5370
- Views 75
- Downloads 0
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