Invariant Object Recognition Using Radon and Fourier Transforms

  • Guangyi Chen
  • Tien Dai Bui
  • Adam Krzyzak
  • Yongjia Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7951)


In this paper, an invariant algorithm for object recognition is proposed by using the Radon and Fourier transforms. It has been shown that this algorithm is invariant to the translation and rotation of pattern images. The scaling invariance can be achieved by the standard normalization techniques. Our algorithm works even when the center of the pattern object is not aligned well. This advantage is because the Fourier spectra are invariant to spatial shift in the radial direction whereas existing methods assume the centroids are aligned exactly. Experimental results show that the proposed method is better than the Zernike’s moments, the dual-tree complex wavelet (DTCWT) moments, and the auto-correlation wavelet moments for one aircraft database and one shape database.


Radon transform Fourier transform Zernike’s moments object recognition pattern recognition Gaussian white noise 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Prokop, R.J., Reeves, A.P.: A survey of moments-based techniques for unoccluded object representation and recognition. CVGIP: Graphical Models Image Processing 54(5), 438–460 (1992)CrossRefGoogle Scholar
  2. 2.
    Hu, M.K.: Visual pattern recognition by moment invariants. IRE Transactions on Information Theory 8, 179–187 (1962)zbMATHGoogle Scholar
  3. 3.
    Khotanzad, A., Hong, Y.H.: Invariant image recognition by Zernike moments. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(5), 489–497 (1990)CrossRefGoogle Scholar
  4. 4.
    Chen, G.Y., Xie, W.F.: Wavelet-based moment invariants for pattern recognition. Optical Engineering 50(7), 077205 (2011)CrossRefGoogle Scholar
  5. 5.
    Chen, G.Y., Bhattacharya, P.: Invariant pattern recognition using ridgelet packets and the Fourier transform. International Journal of Wavelets, Multiresolution and Information Processing 7(2), 215–228 (2009)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Hassanieh, H., Indyk, P., Katabi, D., Price, E.: Simple and Practical Algorithm for Sparse Fourier Transform. In: SODA (January 2012)Google Scholar
  7. 7.
    Hassanieh, H., Indyk, P., Katabi, D., Price, E.: Nearly Optimal Sparse Fourier Transform. In: STOC (May 2012)Google Scholar
  8. 8.
    Wang, X., Xiao, B., Ma, J.F., Bi, X.L.: Scaling and rotation invariant analysis approach to object recognition based on Radon and Fourier-Mellin transforms. Pattern Recognition 40, 3503–3508 (2007)zbMATHCrossRefGoogle Scholar
  9. 9.
    Sebastian, T.B., Klein, P.N., Kimia, B.B.: Recognition of Shapes by Editing Shock Graphs. In: International Conference on Computer Vision, ICCV (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guangyi Chen
    • 1
  • Tien Dai Bui
    • 1
  • Adam Krzyzak
    • 1
  • Yongjia Zhao
    • 2
  1. 1.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada
  2. 2.State Key Lab. of Virtual Reality Technology and SystemsBeihang UniversityBeijingP.R. China

Personalised recommendations