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Implementation of Plant Leaf Recognition System on ARM Tablet Based on Local Ternary Pattern

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9226))

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Abstract

The Local Binary Pattern (LBP) and its variants is powerful in capturing image features and computational simplicity, However LBP’s sensitivity to noise, particularly in near-uniform image regions has stimulated many transformations of LBP to improve the ability of feature description. The Local Ternary Pattern (LTP) extends the conventional LBP to ternary codes and makes a significant improvement. LTP is more resistant to noise, but no longer strictly invariant to gray-level transformations. In this paper, by adopting the Average Local Gray Level (ALG) to take place of the traditional gray value of the center pixel and taking an auto-adaptive strategy on the selection of the threshold, we propose the Enhanced Local Ternary Pattern (ELTP) to improve the performance of LTP and implement an android application to recognize plant-leaf image and identify the species of the plant.

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References

  1. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  2. Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with center-symmetric local binary patterns. In: Kalra, P.K., Peleg, S. (eds.) ICVGIP 2006. LNCS, vol. 4338, pp. 58–69. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  3. Liao, S., Law, M.W.K., Chung, S.: Dominant local binary patterns fortexture classification. IEEE Trans. Image Process. 18(5), 1107–1118 (2009)

    Article  MathSciNet  Google Scholar 

  4. Tan, X.Y., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)

    Article  MathSciNet  Google Scholar 

  5. Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: Face recognition with high-order local pattern descriptor. IEEE Trans. Image Process. 19(2), 533–544 (2010)

    Article  MathSciNet  Google Scholar 

  6. Zhao, Y., Huang, D.-S., Jia, W.: Completed local binary count for rotation invariant texture. IEEE Trans. on Image Process. 21(10), 4492–4497 (2012)

    Article  MathSciNet  Google Scholar 

  7. Zhao, Y., Jia, W., Hu, R.-X., Min, H.: Completed robust local binary pattern for texture classification. Neurocomputing 106, 68–76 (2013)

    Article  Google Scholar 

  8. Ojala, T., Maenpaa, T., Pietikainen, M., Viertola, J., Kyllönen, J., Huovinen, S.: Outex - new framework for empirical evaluation of texture analysis algorithms. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 1, pp. 701–706 (2002)

    Google Scholar 

  9. Dana, K.J., van Ginneken, B., Nayar, S.K., Koenderink, J.J.: Reflectance and texture of real world surfaces. ACM Trans. Graph. 18(1), 1–34 (1999)

    Article  Google Scholar 

  10. Varma, M., Zisserman, A.: A statistical approach to texture classification from single images. Int. J. Comput. Vis. 62(1–2), 61–81 (2005)

    Article  Google Scholar 

  11. Li, B., Huang, D.S.: Locally linear discriminant embedding: an efficient method for face recognition. Pattern Recognit. 41(12), 3813–3821 (2008)

    Article  MATH  Google Scholar 

  12. Huang, D.S.: Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recognit. Artif. Intell. 13(7), 1083–1101 (1999)

    Article  Google Scholar 

  13. Huang, D.S., Ma, S.D.: Linear and nonlinear feed forward neural network classifiers: a comprehensive understanding. J. Intell. Syst. 9(1), 1–38 (1999)

    Article  MathSciNet  Google Scholar 

  14. Huang, D.S.: A constructive approach for finding arbitrary roots of polynomials by neural networks. IEEE Trans. Neural Netw. 15(2), 477–491 (2004)

    Article  Google Scholar 

  15. Huang, D.S., Horace, HS.Ip, Chi, Z.-R.: A neural root finder of polynomials based on root moments. Neural Comput. 16(8), 1721–1762 (2004)

    Article  MATH  Google Scholar 

  16. Zhou, H., Wang, R., Wang, C.: A novel extended local binary pattern operator for texture analysis. Inf. Sci. 178(22), 4314–4325 (2008)

    Article  MATH  Google Scholar 

  17. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Zhou, S., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 168–182. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  18. Zhao, Y., Huang, D.-S., Jia, W.: Completed local binary count for rotation invariant texture. IEEE Trans. Image Process. 21(10), 4492–4497 (2012)

    Article  MathSciNet  Google Scholar 

  19. Heikkilä, M., Pietikäinen, M., Schmid, C.: Description of interest regions with local binary patterns. Pattern Recognit. 42(3), 425–436 (2009)

    Article  MATH  Google Scholar 

  20. Tan, X., Triggs, B.: Fusing Gabor and LBP feature Sets for Kernel-based Face Recognition. In: Proceedings of the IEEE International Workshop on Analysis and Modeling of Face and Gesture, pp. 235–249 (2007)

    Google Scholar 

  21. Huang, D.S.: Systematic Theory of Neural Networks for Pattern Recognition (in Chinese). Publishing House of Electronic Industry of China, Beijing (1996)

    Google Scholar 

  22. Huang, D.S., Yu, H.-J.: Normalized feature vectors: a novel alignment-free sequence comparison method based on the numbers of adjacent amino acids. IEEE/ACM Trans. Comput. Biol. Bioinf. 10(2), 457–467 (2013)

    Article  Google Scholar 

  23. Huang, D.S., Jiang, W.: A general CPL-AdS methodology for fixing dynamic parameters in dual environments. IEEE Trans. Syst. Man Cybern. Part B 42(5), 1489–1500 (2012)

    Article  Google Scholar 

  24. Wang, X.-F., Huang, D.S., Xu, H.: An efficient local Chan-Vese model for image segmentation. Pattern Recognit. 43(3), 603–618 (2010)

    Article  MATH  Google Scholar 

  25. Huang, D.S., Du, J.-X.: A constructive hybrid structure optimization methodology for radial basis probabilistic neural networks. IEEE Trans. Neural Netw. 19(12), 2099–2115 (2008)

    Article  Google Scholar 

  26. Huang, D.S.: Radial basis probabilistic neural networks: model and application. Int. J. Pattern Recognit. Artif. Intell. 13(7), 1083–1101 (1999)

    Article  Google Scholar 

  27. Wang, X.-F., Huang, D.S.: A novel density-based clustering framework by using level set method. IEEE Trans. Knowl. Data Eng. 21(11), 1515–1531 (2009)

    Article  Google Scholar 

  28. Shang, L., Huang, D.S., Du, J.-X., Zheng, C.-H.: Palmprint recognition using FastICA algorithm and radial basis probabilistic neural network. Neurocomputing 69(13–15), 1782–1786 (2006)

    Article  Google Scholar 

  29. Huang, D.S., Horace, H.S.Ip, Chi, Z.: A neural root finder of polynomials based on root moments. Neural Comput. 16(8), 1721–1762 (2004)

    Article  MATH  Google Scholar 

  30. Huang, D.S.: A constructive approach for finding arbitrary roots of polynomials by neural networks. IEEE Trans. Neural Netw. 15(2), 477–491 (2004)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the grants of the National Science Foundation of China, Nos. 61133010, 61373105, 61303111, 61411140249, 61402334, 61472282, 61472280, 61472173, 61373098 and 61272333, China Postdoctoral Science Foundation Grant, Nos. 2014M561513, and partly supported by the National High-Tech R&D Program (863) (2014AA021502 & 2015AA020101), and the grant from the Ph.D. Programs Foundation of Ministry of Education of China (No. 20120072110040), and the grant from the Outstanding Innovative Talent Program Foundation of Henan Province, No. 134200510025.

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Tongji University, Shanghai, China

    Gong-Sheng Xu, Jing-Hua Yuan & Xiao-Ping Zhang

  2. Department of Communication Technology, College of Electronic Information Engineering, Suzhou Vocational University, Suzhou, 215104, Jiangsu, China

    Li Shang

  3. College of Mechanical and Electrical Engineering, Nanchang Institute of Technology, Nanchang, 330099, Jiangxi, China

    Zhi-Kai Huang

  4. College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, China

    Hao-Dong Zhu & Yong Gan

Authors
  1. Gong-Sheng Xu
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  2. Jing-Hua Yuan
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  3. Xiao-Ping Zhang
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  4. Li Shang
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  5. Zhi-Kai Huang
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  6. Hao-Dong Zhu
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  7. Yong Gan
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Corresponding author

Correspondence to Gong-Sheng Xu .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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Xu, GS. et al. (2015). Implementation of Plant Leaf Recognition System on ARM Tablet Based on Local Ternary Pattern. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_15

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  • DOI: https://doi.org/10.1007/978-3-319-22186-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22185-4

  • Online ISBN: 978-3-319-22186-1

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