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Stereo-image normalization of voluminous objects improves textile defect recognition

: Siegmund, Dirk; Kuijper, Arjan; Braun, Andreas


Bebis, G.:
Advances in visual computing. 12th international symposium, ISVC 2016. Pt.1 : Las Vegas, NV, USA, December 12-14, 2016; Proceedings
Cham: Springer International Publishing, 2016 (Lecture Notes in Computer Science 10072)
ISBN: 978-3-319-50834-4 (Print)
ISBN: 978-3-319-50835-1 (Online)
International Symposium on Visual Computing (ISVC) <12, 2016, Las Vegas/Nev.>
Fraunhofer IGD ()
Fraunhofer IGD-R ()
3D Image processing; Multi-view stereo; Machine learning; Pattern recognition; Digitized Work; computer vison (CV)

The visual detection of defects in textiles is an important application in the textile industry. Existing systems require textiles to be spread flat so they appear as 2D surfaces, in order to detect defects. In contrast, we show classification of textiles and textile feature extraction methods, which can be used when textiles are in inhomogeneous, voluminous shape. We present a novel approach on image normalization to be used in stain-defect recognition. The acquired database consist of images of piles of textiles, taken using stereo vision. The results show that a simple classifier using normalized images outperforms other approaches using machine learning in classification accuracy.