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A noise-robust algorithm for classifying cyclic and dihedral symmetric images

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  • Lu, Jian
  • Zou, Yuru
  • Ye, Zhongxing
  • Chen, Wensheng

Abstract

A noise-robust algorithm for detection and classification of cyclic and dihedral symmetric images is presented in this paper. For a symmetric image corrupted by an additive white Gaussian noise (AWGN), the proposed algorithm is implemented by converting the symmetry information into the representation of angularly evenly spaced zero-crossing lines in Mexican-hat wavelet domain; in addition, a continuous Mexican-hat ridgelet is applied to detect those zero-crossing lines, which achieves a simple and fast discrimination between cyclic and dihedral symmetries. Experimental results show that the proposed algorithm is very robust against noise and it can automatically classify the cyclic and dihedral symmetric images.

Suggested Citation

  • Lu, Jian & Zou, Yuru & Ye, Zhongxing & Chen, Wensheng, 2009. "A noise-robust algorithm for classifying cyclic and dihedral symmetric images," Chaos, Solitons & Fractals, Elsevier, vol. 42(2), pages 676-685.
    • Handle: RePEc:eee:chsofr:v:42:y:2009:i:2:p:676-685
    DOI: 10.1016/j.chaos.2009.01.042
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    References listed on IDEAS

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    1. Yousefi, Shahriar & Weinreich, Ilona & Reinarz, Dominik, 2005. "Wavelet-based prediction of oil prices," Chaos, Solitons & Fractals, Elsevier, vol. 25(2), pages 265-275.
    2. Lu, Jian & Ye, Zhongxing & Zou, Yuru & Ye, Ruisong, 2008. "An enhanced fractal image denoising algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 38(4), pages 1054-1064.
    3. Sen, Asok K. & Litak, Grzegorz & Taccani, Rodolfo & Radu, Robert, 2008. "Wavelet analysis of cycle-to-cycle pressure variations in an internal combustion engine," Chaos, Solitons & Fractals, Elsevier, vol. 38(3), pages 886-893.
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