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A Fast Detection Method for Bottle Caps Surface Defect Based on Sparse Representation

  • Conference paper
Intelligent Computing for Sustainable Energy and Environment (ICSEE 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 355))

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Abstract

A machine-vision-based system is developed for detecting defects occurring on the surface of bottle caps. This system adopts a novel algorithm which uses circular region projection histogram (CRPH) as the matching feature. A fast algorithm is proposed based on sparse representation for speed-up searching. The non-zero elements of the sparse vector indicate the defect size and position. Experimental results show that the proposed method is superior to the orientation code method (OCM) and has promising results for detecting defects on the caps’ surface.

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Author information

Authors and Affiliations

  1. School of Mechatronical Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Wenju Zhou, Minrui Fei & Zhe Li

  2. School of Information and Electronic Engineering, Ludong University, Yantai, China

    Wenju Zhou

  3. The Institute of Electronics, Communications and Information Technology, Queen’s University Belfast, United Kingdom

    Huiyu Zhou

Authors
  1. Wenju Zhou
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  2. Minrui Fei
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  3. Huiyu Zhou
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  4. Zhe Li
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Editor information

Editors and Affiliations

  1. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, Ashby Building, Stranmillis Road, BT9 5AH, Belfast, UK

    Kang Li

  2. Department of Automation, Shanghai Jiao Tong University, 800 Dongchuan Road, 200240, Shanghai, China

    Shaoyuan Li  & Dewei Li  & 

  3. School of Mechatronics Engineering and Automation, Shanghai University, 149 Yanchang Road, 200072, Shanghai, China

    Qun Niu

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© 2013 Springer-Verlag Berlin Heidelberg

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Zhou, W., Fei, M., Zhou, H., Li, Z. (2013). A Fast Detection Method for Bottle Caps Surface Defect Based on Sparse Representation. In: Li, K., Li, S., Li, D., Niu, Q. (eds) Intelligent Computing for Sustainable Energy and Environment. ICSEE 2012. Communications in Computer and Information Science, vol 355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37105-9_9

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  • DOI: https://doi.org/10.1007/978-3-642-37105-9_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37104-2

  • Online ISBN: 978-3-642-37105-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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