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Neural Techniques in Logo Recognition

  • Conference paper
Hybrid Information Systems

Part of the book series: Advances in Soft Computing ((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

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References

  1. 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.

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Author information

Authors and Affiliations

  1. Knowledge-Based Intelligent Engineering Systems Center, University of South Australia, Adelaide, South Australia

    Vaijayanti Joshi & Lakhmi C. Jain

  2. University of Magdeburg IESK, Germany

    Udo Seiffert

  3. Defence Science and Technology Organisation, Adelaide, Australia

    Kathleen Zyga & Richard Price

  4. Vienna University of Technology, Austria

    Friedrich Leisch

Authors
  1. Vaijayanti Joshi
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  2. Lakhmi C. Jain
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  3. Udo Seiffert
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  4. Kathleen Zyga
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  5. Richard Price
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  6. Friedrich Leisch
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Editor information

Editors and Affiliations

  1. School of Computing and Information Technology, Monash University, Gippsland Campus, 3842, Churchill, VIC, Australia

    Ajith Abraham

  2. Department of Pattern Recognition, Fraunhofer IPK Berlin, Pascalstraße 8-9, 10587, Berlin, Germany

    Mario Köppen

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© 2002 Springer-Verlag Berlin Heidelberg

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Cite this paper

Joshi, V., Jain, L.C., Seiffert, U., Zyga, K., Price, R., Leisch, F. (2002). Neural Techniques in Logo Recognition. In: Abraham, A., Köppen, M. (eds) Hybrid Information Systems. Advances in Soft Computing, vol 14. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1782-9_3

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  • DOI: https://doi.org/10.1007/978-3-7908-1782-9_3

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1480-4

  • Online ISBN: 978-3-7908-1782-9

  • eBook Packages: Springer Book Archive

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