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International Conference on Intelligent Computing

ICIC 2011: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence pp 435–440Cite as

A New Wood Recognition Method Based on Gabor Entropy

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6839))

Abstract

Correct wood recognition has an important meaning in rational use of wood resources. Automatic wood recognition based on wood stereogram are studied in this paper. According to the wood stereogram characteristics, a method of image normalization is presented firstly. Then wood texture features are extracted using Gabor wavelet with analyzing the best scale and orientation parameters. In addition to the mean and standard deviation on the Gabor filter bank, entropy, contrast and other statistical features are used for classification. Experimental results show that the entropy can better extract texture features on Gabor wavelet, which greatly improve the wood recognition rate.

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References

  1. Wang, H., Zhang, G., Qi, H., et al.: A Review of the Research on Wood Recognition Technology. Journal of Zhejiang Forestry College 26(6), 896–902 (2009)

    Google Scholar 

  2. Wang, H., Qi, H., Li, W., et al.: A GA-based Automatic Pore Segmentation Algorithm. In: Proceedings of the First ACM/SIGEVO Summit on Genetic and Evolutionary Computation, Shanghai, China, pp. 985–988 (2009)

    Google Scholar 

  3. Wang, H., Zhang, G., Qi, H., et al.: Multi-objective Optimization on Pore Segmentation. In: Proceedings of the 5th International Conference on Natural Computation, Tianjin, China, pp. 613–617 (2009)

    Google Scholar 

  4. Daugman, J.: Uncertainty Relation for Resolution in Space, Spatial Frequency, and Orientation Optimized by Two-dimensional Visual Cortical Flters. Journal of the Optical Society of America A 2(7), 1160–1169 (1985)

    Article  Google Scholar 

  5. Tao, D., Li, X., Wu, X., Maybank, S.J.: General Tensor Discriminant Analysis and Gabor Features for Gait Recognition. IEEE Trans. PAMI 29(10), 1700–1715 (2007)

    Article  Google Scholar 

  6. Bianconi, F., Fernández, A.: Evaluation of the Effects of Gabor Filter Parameters on Texture Classification. Pattern Recognition 40(12), 3325–3335 (2007)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, 230027, Hefei, China

    Hang-jun Wang

  2. Hefei Institute of Intelligent Machines, Chinese Academy of Science, 230031, Hefei, China

    Hang-jun Wang & Xiao-Feng Wang

  3. School of Information Engineering, Zhejiang A&F University, 311300, Lin’an, China

    Hang-jun Wang & Heng-nian Qi

  4. Key Lab of Network and Intelligent Information Processing, Department of Computer Science and Technology, Hefei University, Hefei, 230601, China

    Xiao-Feng Wang

Authors
  1. Hang-jun Wang
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  2. Heng-nian Qi
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  3. Xiao-Feng Wang
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, No. 5, Dongfeng Road, Jinshui District, 450002, Zhengzhou, Henan, China

    Yong Gan

  3. Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Phalguni Gupta

  4. Department of Biotechnology, Indian Institute of Technology Madras, 600 036, Chennai, Tamilnadu, India

    M. Michael Gromiha

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Wang, Hj., Qi, Hn., Wang, XF. (2012). A New Wood Recognition Method Based on Gabor Entropy. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_56

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  • DOI: https://doi.org/10.1007/978-3-642-25944-9_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25943-2

  • Online ISBN: 978-3-642-25944-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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