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International Conference on Artificial Neural Networks

ICANN 2006: Artificial Neural Networks – ICANN 2006 pp 558–567Cite as

Rotation Invariant Recognition of Road Signs with Ensemble of 1-NN Neural Classifiers

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4132))

Abstract

The paper presents a parallel system of two compound classifiers for recognition of the circular shape road signs. Each of the two classifiers is built of an ensemble of 1-nearest-neighbour (1-NN) classifiers and the arbitration unit operating in the winner-takes-all mode. For the 1-NN we employed the Hamming neural network (HNN) which accepts the binary input. Each HNN is responsible for classification within a single group of deformable prototypes of the road signs. Each of the two compound classifiers has the same structure, however they accept features from different domains: the spatial and the log-polar spaces. The former has an ability of precise classification for shifted but non-rotated objects. The latter exhibits good abilities to register the rotated shapes and also to reject the non road sign objects due to its high false negative detection properties. The combination of the two outperformed each of the single versions what was verified experimentally. The system is characterized by fast learning and recognition rates.

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References

  1. Abu-Mostafa, Y.S.: Hints. Neural Computations (7), 639–671 (1995)

    Article  Google Scholar 

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

    Google Scholar 

  3. 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 

  4. 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 

  5. Cyganek, B.: Object Detection in Multi-Channel and Multi-Scale Images Based on the Structural Tensor. In: Gagalowicz, A., Philips, W. (eds.) CAIP 2005. LNCS, vol. 3691, pp. 570–578. Springer, Heidelberg (2005)

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

  8. Duch, W., Grudziński, K.: A framework for similarity-based methods. Second Polish Conference on Theory and Applications of Artificial Intelligence, 33–60 (1998)

    Google Scholar 

  9. Escalera, A., Moreno, L., Salichs, M.A., Armingol, J.M.: Road traffic sign detection and classification. IEEE Trans. Ind. Electron 44, 848–859 (1997)

    Article  Google Scholar 

  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. Floréen, P.: Computational Complexity Problems in Neural Associative Memories. PhD Thesis, University of Helsinki, Department of Computer Science, Finland (1992)

    Google Scholar 

  12. Hastie, T., Tibshirani, R., Friedman: The Elements of Statistical Learning. Springer, Heidelberg (2001)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  14. Lippman, R.: An introduction to computing with neural nets. IEEE Transactions on Acoustic, Speech, and Signal Processing ASSP-4, 4–22 (1987)

    Google Scholar 

  15. Luo, R.C., Potlapalli, H.: Landmark recognition using projection learning for mobile robot navigation. In: Proc. IEEE Int. Conf. Neural Networks, vol. 4, pp. 2703–2708 (1994)

    Google Scholar 

  16. Klette, R., Rosenfeld, A.: Digital Geometry. Morgan Kaufman, San Francisco (2004)

    MATH  Google Scholar 

  17. 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 

  18. Road Signs and Signalization. Directive of the Polish Ministry of Infrastructure, Internal Affairs and Administration (Dz. U. Nr 170, poz. 1393) (2002)

    Google Scholar 

  19. 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 

  20. 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  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. AGH – University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Bogusław Cyganek

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  1. Bogusław Cyganek
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Editor information

Editors and Affiliations

  1. School of Electrical and Computer Engineering, Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, 157 80, Zographou, GR, Greece

    Stefanos Kollias

  2. Department of Electrical and Computer Engineering, National Technical University of Athens, 15780, Zographou, Greece

    Andreas Stafylopatis

  3. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  4. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

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© 2006 Springer-Verlag Berlin Heidelberg

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Cyganek, B. (2006). Rotation Invariant Recognition of Road Signs with Ensemble of 1-NN Neural Classifiers. In: Kollias, S., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840930_58

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  • DOI: https://doi.org/10.1007/11840930_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38871-5

  • Online ISBN: 978-3-540-38873-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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