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Stereo-Image Normalization of Voluminous Objects Improves Textile Defect Recognition

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

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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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Author information

Authors and Affiliations

  1. Fraunhofer Institute for Computer Graphics Research (IGD), Fraunhoferstrasse 5, 64283, Darmstadt, Germany

    Dirk Siegmund, Arjan Kuijper & Andreas Braun

Authors
  1. Dirk Siegmund
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  2. Arjan Kuijper
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  3. Andreas Braun
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Corresponding author

Correspondence to Dirk Siegmund .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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Siegmund, D., Kuijper, A., Braun, A. (2016). Stereo-Image Normalization of Voluminous Objects Improves Textile Defect Recognition. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_17

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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