Circular Projection for Pattern Recognition

  • Guangyi Chen
  • Tien Dai Bui
  • Sridhar Krishnan
  • Shuling Dai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7951)


There are a number of methods that transform 2-D shapes into periodic 1-D signals so that faster recognition can be achieved. However, none of these methods are both noise-robust and scale invariant. In this paper, we propose a circular projection method for transforming 2-D shapes into periodic 1-D signals. We then apply a number of feature extraction methods to the 1-D signals. Our method is invariant to the translation, rotation and scaling of the 2-D shapes. Also, our method is robust to Gaussian white noise. In addition, it performs very well in terms of classification rates for a well-known shape dataset.


Circular projection Ramanujan Sums (RS) invariant features pattern recognition Gaussian white noise fast Fourier transform (FFT) 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guangyi Chen
    • 1
    • 2
  • Tien Dai Bui
    • 1
  • Sridhar Krishnan
    • 2
  • Shuling Dai
    • 3
  1. 1.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada
  2. 2.Department of Electrical and Computer EngineeringRyerson UniversityTorontoCanada
  3. 3.State Key Lab. of Virtual Reality Technology and SystemsBeihang UniversityBeijingP.R. China

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