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| Features Extraction and Classification of Rice Paper Images Based on Wavelet Transform |
| Weixin Xie, Hongbin Huang, Haotian Zhai, Weiping Liu |
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Abstract Based on wavelet transform for the classification of image features,
a new method for the classification of image texture features is put
forward. In our study, the images of rice paper have been acquired
using a digital image system. The images of rice paper were
decomposed respectively using Debaucheries and Gabor wavelet
transforms. The subband of low frequency was selected to extract 11
kinds of classic characteristic value of Gray-level Co-occurrence
Matrix (GLCM). Then the texture feature values were classified by
the Support Vector Machine (SVM). In order to evaluate the
classification accuracy, feature values of the original images and
images processed by wavelet decomposition were sent into SVM
individually. The classification rate of rice paper texture images
was only 84.1\% using characteristic values of original images, but
reached 93.0\% by using Gabor wavelet. The overall results show that
wavelet transform is a highly efficient method for paper
classification. In summary, the method of using wavelet
decomposition for the recognition of rice image provides a new
nondestructive and fast method for rice paper classification.
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| DOI:10.12733/jics20105736 |
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