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2016 | OriginalPaper | Chapter

Stereo-Image Normalization of Voluminous Objects Improves Textile Defect Recognition

Authors : Dirk Siegmund, Arjan Kuijper, Andreas Braun

Published in: Advances in Visual Computing

Publisher: Springer International Publishing

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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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previous chapter A Multiple Object Tracking Evaluation Analysis Framework
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Metadata
Title
Stereo-Image Normalization of Voluminous Objects Improves Textile Defect Recognition
Authors
Dirk Siegmund
Arjan Kuijper
Andreas Braun
Copyright Year
2016
Book
Advances in Visual Computing

Print ISBN: 978-3-319-50834-4

Electronic ISBN: 978-3-319-50835-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-50835-1_17

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