The Regional Detection of 2D Barcode in Complicated Backgrounds of Metal Parts

  • Wei Wang
  • Wei-ping He
  • Lei Lei
  • Wen-tao Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7202)

Abstract

The traditional detection algorithms fall into non-machine learning method, which utilize geometrical characteristic of 2D code, introduce digital image analysis method,marginal detection and geometry detection, etc. while these algorithms are no more than elementary methods, they are limited to printed paper, and not applicable to other material surface which Data Matrix is punched on. To solve the drawbacks mentioned above, this paper presents machine methods integrated into cascade filter methods to position the region of 2D code, then employ clustering growth method. Our experiments reveals, compared with traditional method, our methods have achieved higher rate of detection with good robustness.

Keywords

Local Binary Pattern Connected Region Cluster Growth License Plate Recognition Cascade Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Arnould, S., Awcock, G.J., Thomas, R.: Remote barcode localization using mathematical morphology. In: IEE Conference, pp. 642–646 (1999)Google Scholar
  2. 2.
    Viard-Gaudin, C., Normand, N., Barba, D.: Bar code location algorithm using a two-dimensional approach. In: Proceedings of the 2nd International Conference on Document Analysis and Recognition, pp. 45–48 (1993)Google Scholar
  3. 3.
    Jain, A.K., Chen, Y.: Bar code localization using texture analysis. In: Proceedings of the 2nd International Conference on Document Analysis and Recognition, pp. 41–44 (1993)Google Scholar
  4. 4.
    Alec, C., Chung, L.C., Han, H.K.: Neural networks and Fourier descriptors for part positioning using bar code features in material handling systems. Computers & Industrial Engineering 32(2), 467–476 (1997)CrossRefGoogle Scholar
  5. 5.
    Viola, P., Jones, M.: Fast and robust classification using asymmetric adaboost and a detector cascade. In: Proceedings from NIPS 2001 (2001)Google Scholar
  6. 6.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  7. 7.
    Levi, K., Weiss, Y.: Learning Object Detection from a Small Number of Examples:The Importance of Good Features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2004), pp. 53–60 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wei Wang
    • 1
  • Wei-ping He
    • 1
  • Lei Lei
    • 1
  • Wen-tao Li
    • 1
  1. 1.Contemporary Key Lab of Design & Integrated Manufacturing TechnologyNorthwest Polytechnical UniversityXi’anChina

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