Stereo-Image Normalization of Voluminous Objects Improves Textile Defect Recognition

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10072)

Abstract

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.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Fraunhofer Institute for Computer Graphics Research (IGD)DarmstadtGermany

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