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

作者：Lu, Jian^*; Zou, Yuru; Ye, Zhongxing; Chen, Wensheng

来源：Chaos Solitons & Fractals, 2009, 42(2): 676-685.

DOI：10.1016/j.chaos.2009.01.042

• Additive White Gaussian noise
• Mexican Hat wavelet
• Noise robust
• Ridgelets
• Zero-crossings

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.

• 出版日期2009-10-30
• 单位上海交通大学; 深圳大学