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Neural Techniques in Logo Recognition

  • Vaijayanti Joshi
  • Lakhmi C. Jain
  • Udo Seiffert
  • Kathleen Zyga
  • Richard Price
  • Friedrich Leisch
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 14)

Abstract

A novel scheme is presented to detect and recognise a logo in a given document(s). Another area of interest will be dealing with distorted logos. This refers to logos, which are scaled, rotated, and have a brightness or contrast variation from the original logo. The system recognises these logos and makes correct judgements regarding their identity. The success rate for this system is about 75 to 80 percent

Keywords

Neural Network Histogram Analysis Contrast Variation Recognition Network Logo Recognition 
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

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    K. Zyga, R. Price and B.Williams. Price and B.Williams, “A Generalized Regression Neural Network for Logo Recognition”, Proceedings of the Fourth International Conference on Knowledge Based Intelligent Systems and Allied Technologies, Brighton, U.K, pp. 475–478, 2000.Google Scholar
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    U.Seiffert, “Extracting Features from Logos-First Suggestions” University of Magdeburg, Germany (2000).Google Scholar
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    Doermann, S. D., Rivlin, E. and Weiss, I., “Logo Recognition Using Geometric Invariants”, Proceedings of Second International Conference on Diagram Analysis and Recognition, Tsukuba, Japan, 894–897, October 1993.Google Scholar
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    Caprari R. S., “Logo Recognition by literal binary pattern matching”, Defence Science and Technology Organisation, South Australia, September 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Vaijayanti Joshi
    • 1
  • Lakhmi C. Jain
    • 1
  • Udo Seiffert
    • 2
  • Kathleen Zyga
    • 3
  • Richard Price
    • 3
  • Friedrich Leisch
    • 4
  1. 1.Knowledge-Based Intelligent Engineering Systems CenterUniversity of South AustraliaAdelaideSouth Australia
  2. 2.University of Magdeburg IESKGermany
  3. 3.Defence Science and Technology OrganisationAdelaideAustralia
  4. 4.Vienna University of TechnologyAustria

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