Abstract

Wavelet packet decomposition not only has the decompose effect at low-frequency by using wavelet decomposition, but also has the decompose effect at high-frequency where can not do by using wavelet decomposition. In this paper, the wavelet packet decomposition algorithm was proposed and applied to glass-image recognition. Compared with other feature extracting technologies such as Zernike’s moments and wavelet transformation, the experiments proved that the wavelet packet decomposition was the best on both precision and efficiency

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