Skip to main content

Road Signs Recognition by the Scale-Space Template Matching in the Log-Polar Domain

  • Conference paper
Pattern Recognition and Image Analysis (IbPRIA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4477))

Included in the following conference series:

Abstract

This paper presents a cascaded system for recognition of the circular road-signs. The system consists of two compound detectors-classifiers. Each operates on the Gaussian scale-space and does template matching in the log-polar domain. The first module is responsible for detection of the potential sign areas at the coarsest level of the pyramid. The second one, in turn, refines the already found places at the finest level. Thanks to this composition, as well as to the efficient matching in the log-polar domain, the system is very robust in terms of recognition of the signs with different scales and rotations, as well as under partial occlusions, poor illumination conditions, and noise.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Amit, Y.: 2D Object Detection and Recognition. MIT Press, Cambridge (2002)

    Google Scholar 

  2. Aoyagi, Y., Asakura, T.: A study on traffic sign recognition in scene image using genetic algorithms and neural networks. In: IEEE Conf. Electronics, Control, pp. 1838–1843 (1996)

    Google Scholar 

  3. Chen, X., Yang, J., Zhang, J., Waibel, A.: Automatic Detection and Recognition of Signs From Natural Scenes. IEEE Trans. on Image Proc. 13(1), 87–99 (2004)

    Article  Google Scholar 

  4. Cyganek, B.: Soft System for Road Sign Detection. Accepted to the IFSA – Theory and Applications of Fuzzy Logic and Soft Computing, Cancun, Mexico, (June 18-21, 2007)

    Google Scholar 

  5. Cyganek, B.: Rotation Invariant Recognition of Road Signs with Ensemble of 1-NN Neural Classifiers. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds.) ICANN 2006. LNCS, vol. 4132, pp. 558–567. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  6. Cyganek, B.: Recognition of Road Signs with Mixture of Neural Networks and Arbitration Modules. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 52–57. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  7. Cyganek, B.: Matching of the Multi-channel Images with Improved Nonparametric Transformations and Weighted Binary Distance Measures. In: Reulke, R., Eckardt, U., Flach, B., Knauer, U., Polthier, K. (eds.) IWCIA 2006. LNCS, vol. 4040, pp. 74–88. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Cyganek, B.: Hardware-Software System for Acceleration of Image Processing Operations. Accepted to be published in the Machine Graphics & Vision (2007)

    Google Scholar 

  9. DaimlerChrysler, The Thinking Vehicle (2002), http://www.daimlerchrysler.com

  10. Escalera, A., Armingol, J.A.: Visual Sign Information Extraction and Identification by Deformable Models. IEEE Tr. On. Int. Transportation Systems 5(2), 57–68 (2004)

    Article  Google Scholar 

  11. Forsyth, D., Ponce, J.: Computer Vision. In: A Modern Approach, Prentice-Hall, Englewood Cliffs (2003)

    Google Scholar 

  12. Kara, L.B., Stahovich, T.F.: An image-based, trainable symbol recognizer for hand-drawn sketches. Computers & Graphics 29(4), 501–517 (2005)

    Article  Google Scholar 

  13. Piccioli, G., Micheli, E.D., Parodi, P., Campani, M.: Robust method for road sign detection and recognition. Image and Vision Computing 14, 209–223 (1996)

    Article  Google Scholar 

  14. Porikli, F.: Integral Histogram: A FastWay to Extract Histograms in Cartesian Spaces. TR2005-057, Mitsubishi Electric Research Laboratories, Cambridge, MA, USA (2005)

    Google Scholar 

  15. Wandell, B.A.: Foundations of Vision. Sinauer Associates Publishers Inc., Sunderland (1995)

    Google Scholar 

  16. Zheng, Y.J., Ritter, W., Janssen, R.: An adaptive system for traffic sign recognition. In: Proc. IEEE Intelligent Vehicles Symp., pp. 165–170 (1994)

    Google Scholar 

  17. Zokai, S., Wolberg, G.: Image Registration Using Log-Polar Mappings for Recovery of Large-Scale Similarity. IEEE Transactions on Image Processing 14(10), 1422–1433 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Joan Martí José Miguel Benedí Ana Maria Mendonça Joan Serrat

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Cyganek, B. (2007). Road Signs Recognition by the Scale-Space Template Matching in the Log-Polar Domain. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_43

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72847-4_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72846-7

  • Online ISBN: 978-3-540-72847-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics