Skip to main content
SpringerLink
Log in

Application of the Polar–Fourier Greyscale Descriptor to the Automatic Traffic Sign Recognition

  • Conference paper
  • First Online:
Image Analysis and Recognition (ICIAR 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9164))

Included in the following conference series:

Abstract

An object extracted from a digital image has to be represented using particular features, e.g. shape, colour, texture. In the paper the Polar–Fourier Greyscale Descriptor is employed for this purpose, which applies the information about silhouette and intensity of an object. Its properties are experimentally analysed using the images of traffic signs extracted from real video sequences. These objects were selected, because in many cases the images of traffic signs are strongly distorted, which hampers the proper recognition. During the experiments 500 images were used for each of the 20 classes, which resulted in 10000 instances. The average recognition rate was above \(89\,\%\).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Frejlichowski, D.: An algorithm for binary contour objects representation and recognition. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2008. LNCS, vol. 5112, pp. 537–546. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  2. Lombardi, T., Cha S.-H., Tappert, C.: A Graphical user interface for a fine-art painting image retrieval system. In: Proceedings of the 6th ACM SIGMM International Workshop On Multimedia Information Retrieval, MIR 2004, pp. 107–112 (2004)

    Google Scholar 

  3. Verma, A., Sharma, D.: Content based image retrieval using color, texture and shape features. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(5), 383–389 (2014)

    Google Scholar 

  4. Proença, H., Santos, G.: Fusing color and shape descriptors in the recognition of degraded iris images scquired at visible wavelengths. Comput. Vis. Image Unders. 116(2), 167–178 (2012)

    Google Scholar 

  5. Lange, M., Ganebnykh, S., Lange, A.: Moment-based pattern representation using shape and grayscale features. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4477, pp. 523–530. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Shi, M., Fujisawa, Y., Wakabayashi, T., Kimura, F.: Handwritten numeral recognition using gradient and curvature of gray scale image. Pattern Recog. 35(1), 2051–2059 (2002)

    Article  MATH  Google Scholar 

  7. Wakahara, T., Kimura, Y., Tomono, A.: Affine-invariant recognition of gray-scale characters using global affine transformation correlation. IEEE Trans. Pattern Anal. Mach. Intel. 23(4), 384–395 (2001)

    Article  Google Scholar 

  8. de Araujo, S.A., Kim, H.Y.: Rotation, scale and translation-invariant segmentation-free grayscale shape recognition using mathematical morphology. In: Proceedings of the 8th International Symposium on Mathematical Morphology, October 10–13, 2007, Rio de Janeiro, Brazil, vol. 2, pp. 61–62 (2007)

    Google Scholar 

  9. Gudigar, A., Chokkadi, S., Raghavendra, U.: A review on automatic detection and recognition of traffic sign. Multimedia Tools and Appl. 71, 1363–1380 (2014). doi:10.1007/s11042-014-2293-7

    Article  Google Scholar 

  10. Hazelhoff, L., Creusen, I.M., de With, P.H.N.: Exploiting street-level panoramic images for large-scale automated surveying of traffic signs. Mach. Vis. Appl. 25, 1893–1911 (2014)

    Article  Google Scholar 

  11. Timofte, R., Zimmermann, K., van Gool, L.: Multi-view traffic sign detection, recognition, and 3D localisation. Mach. Vis. Appl. 25, 633–647 (2014)

    Article  Google Scholar 

  12. Escalera, S., Pujol, O., Radeva, P.: Traffic sign recognition system with \(\beta \) -correction. Mach. Vis. Appl. 21, 99–111 (2010)

    Article  Google Scholar 

  13. Gao, X.W., Podladchikova, L., Shaposhnikov, D., Hong, K., Shevtsova, N.: Recognition of traffic signs based on their colour and shape features extracted using human vision models. J. Vis. Commun. Image Repres. 17, 675–685 (2006)

    Article  Google Scholar 

  14. Pazhoumand-dar, H., Yaghoobi, M.: A new approach in road sign recognition based on fast fractal coding. Neural Computing and Applications 22, 615–625 (2013)

    Article  Google Scholar 

  15. Lillo-Castellano, J.M., Mora-Jimenez, I., Figuera-Pozuelo, C., Rojo-Alvarez, J.L.: Traffic Sign segmentation and classification using statistical learning methods. Neurocomputing 153, 286–299 (2015)

    Article  Google Scholar 

  16. de la Escalera, A., Armingol, J.M., Mata, M.: Traffic sign recognition and analysis for intelligent vehicles. Image Vis. Comput. 21, 247–258 (2003)

    Article  Google Scholar 

  17. Zaklouta, F., Stanciulescu, B.: Real-time traffic sign recognition in three stages. Robot. Autonom. Syst. 62, 16–24 (2014)

    Article  Google Scholar 

  18. Ruta, A., Li, Y., Liu, X.: Real-time traffic sign recognition from video by class-specific discriminative features. Pattern Recog. 43, 416–430 (2010)

    Article  MATH  Google Scholar 

  19. Jimenez, P.G., Bascon, S.M., Moreno, H.G., Arroyo, S.L., Ferreras, F.L.: Traffic sign shape classification and localization based on the normalized fft of the signature of blobs and 2D homographies. Sig. Proces. 88(18), 2943–2955 (2008)

    Article  MATH  Google Scholar 

  20. Koncar, A., Janssen, H., Halgamuge, S.: Gabor wavelet similarity maps for optimising hierarchical road sign classifiers. Pattern Recog. Lett. 28, 260–267 (2007)

    Article  Google Scholar 

  21. Fleyeh, H., Dougherty, M., Aenugula, D., Baddam, S.: Invariant road sign recognition with fuzzy artmap and zernike moments. In: Proceedings of the IEEE Intelligent Vehicles Symposium, pp. 31–36 (2007)

    Google Scholar 

  22. Frejlichowski, D.: Identification of erythrocyte types in greyscale MGG images for computer-assisted diagnosis. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 636–643. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  23. Frejlichowski, D.: Application of the polar-fourier greyscale descriptor to the problem of identification of persons based on ear images. In: Choraś, R.S. (ed.) Image Processing and Communications Challenges 3. AISC, vol. 102, pp. 5–12. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  24. Frejlichowski, D.: An experimental evaluation of the polar-fourier greyscale descriptor in the recognition of objects with similar silhouettes. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2012. LNCS, vol. 7594, pp. 363–370. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  25. Kukharev, G.: Digital Image Processing and Analysis. Szczecin University of Technology Press (1998)

    Google Scholar 

  26. Stallkamp, J., V, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multi-class classification competition. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1453–1460 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Żołnierska 52, 71-210, Szczecin, Poland

    Dariusz Frejlichowski

Authors
  1. Dariusz Frejlichowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dariusz Frejlichowski .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, Ontario, Canada

    Mohamed Kamel

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Frejlichowski, D. (2015). Application of the Polar–Fourier Greyscale Descriptor to the Automatic Traffic Sign Recognition. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-20801-5_56

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20800-8

  • Online ISBN: 978-3-319-20801-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation