Vehicle Recognition Using Curvelet Transform and Thresholding

  • Farhad Mohamad Kazemi
  • Hamid Reza Pourreza
  • Reihaneh Moravejian
  • Ehsan Mohamad Kazemi


This paper proposes the performance of a new algorithm for recognition vehicle’s system. This recognition system is based on extracted features on the performance of image’s curvelet transform & achieving standard deviation of curvelet coefficients matrix in different scales & various orientations. The curvelet transform is a multiscale transform with frame elements indexed by location, scale and orientation parameters, and have time-frequency localization properties of wavelets but also shows a very high degree of directionality and anisotropy.The used classifier in this paper is called k nearest-neighbor.In addition, the proposed recognition system is obtained by using different scales information as feature vector. So, we could clarify the most important scales in aspect of having useful information. The results of this test show, the right recognition rate of vehicle’s model in this recognition system, at the time of using the total scales information numbers 2,3&4 curvelet coefficients matrix is about 95%. We’ve gathered a data set that includes of 300 images from 5 different classes of vehicles. These 5 classes of vehicles include of: PEUGEOT 206, PEUGEOT 405, Pride, RENULT5 and Peykan. We’ve examined 230 pictures as our train data set and 70 pictures as our test data set.


Feature Vector Recognition Rate Tight Frame Deformable Model Vehicle Detection 
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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  1. [1]
    MP. Dubuisson-Jolly, S. Lakshmanan, and A. Jain. Vehicle segmentation and classification using deformable templates. IEEE Transactions Pattern Analysis and Machine Intelligence, 18(3):293–308, 1996.CrossRefGoogle Scholar
  2. [2]
    J. Ferryman, A . Worral, G. Sulliva, and K. Baker.A generic deformable model for vehicle recognition. In British Machine Vision Conference, pages 127–136. British Machine Vision Association, 1995.Google Scholar
  3. [3]
    W. Wei, Q. Zhang, and M. Wang. A method of vehicle classification using models and neural networks. In IEEE Vehicular Technology Conference. IEEE, 2001.Google Scholar
  4. [4]
    E.J. Candes, D.L. Donoho,“Curvelets - A surprisingly effective nonadaptive representation for objects with edges”, Curve and Surface Fitting, Vanderbilt Univ.Press 1999.Google Scholar
  5. [5]
    T. Kato, Y. Ninomiya, and I. Masaki. Preceding vehicle recognition based on learning from sample images. IEEE Transactions on Intelligent Transportation Systems, 3(4):252–260, 2002.CrossRefGoogle Scholar
  6. [6]
    A. Lai, G. Fung, and N Yung. Vehicle type classification from visual-based dimension estimation. In IEEE Intelligent Transportation Systems Conference, pages 201–206. IEEE, 2001.Google Scholar
  7. [7]
    N. Matthews, P. An, D. Charnley, and C. Harris. Vehicle detection and recognition in greyscale imagery. Control Engineering Practice, 4(4):472–479, 1996.CrossRefGoogle Scholar
  8. [8]
    V.S.Petrovic and T.F.Cootes. Analysis of Features for Rigid Structure Vehicle Type Recognition.2003.Google Scholar
  9. [9]
    Video-based Car Surveillance: License Plate, Make, and Model Recognition,Thesis , Masters of Science in Computer Science University of California, San Diego, 2005.Google Scholar
  10. [10]
    E.J. Candes, D.L. Donoho,“New Tight Frames of Curvelets and Optimal Representations of Objects with Smooth Singularities”, Technical Report, Stanford University, 2002.Google Scholar
  11. [11]
    D.L. Donoho,“De-noising by soft-thresholding”, IEEE Transactions on Information Theory, 1995.Google Scholar
  12. [12]
    B.S. Kashin, V.N. Temlyakov,“On best m-term approximations and the entropy of sets in the space L1” Mathematical Notes 56, 1137-1157, 1994.CrossRefMathSciNetMATHGoogle Scholar
  13. [13]
    E.J. Candes, L. Demanet, D.L. Donoho, L. Ying,“Fast Discrete Curvelet Transforms” Technical Report, Cal Tech, 2005.Google Scholar
  14. [14]
    S. Theodoridis, K. Koutroumbas, Pattern Recognition, Academic Press, New York, 1999.Google Scholar
  15. [15]
    F. M.Kazemi, S. Samadi,“Vehicle Recognition Based on Fourier, Wavelet and Curvelet Transforms - a Comparative Study,” IEEE, International Conference on Information Technology (ITNG’07), USA, pp. 939-940, 2007.Google Scholar
  16. [16]
    F. M.Kazemi, S. Samadi “Vehicle Recognition Based on Fourier, Wavelet and Curvelet Transforms - a Comparative Study” , IJCSNS International Journal of Computer Science and Network Security, VOL.7 No.2, pp. 130-135, 2007.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  • Farhad Mohamad Kazemi
    • 1
  • Hamid Reza Pourreza
    • 2
  • Reihaneh Moravejian
    • 1
  • Ehsan Mohamad Kazemi
    • 1
  1. 1.Young Researchers ClubIslamic Azad UniversityMashadIran
  2. 2.Computer DepartmentFerdowsi University MashadIran

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