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Enhanced Local Ternary Pattern for Texture Classification

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Intelligent Computing Theory (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8588))

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Abstract

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. To improve the performance of LTP, this paper proposes the Enhanced Local Ternary Pattern (ELTP) by adopting the Average Local Gray Level (ALG) to take place of the traditional gray value of the center pixel, taking an auto-adaptive strategy on the selection of the threshold and introducing a novel coding process. Finally, the Completed Enhanced Ternary Pattern (CELTP) is also presented.

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Author information

Authors and Affiliations

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

    Jing-Hua Yuan

  2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, Henan, China

    Hao-Dong Zhu & Yong Gan

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

    Li Shang

Authors
  1. Jing-Hua Yuan
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  2. Hao-Dong Zhu
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  3. Yong Gan
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  4. Li Shang
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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. Electrical and Electronics Department, Politecnico of Bari, Via Orabona, 4,, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongon, 2522, North Wollongon, NSW, Australia

    Prashan Premaratne

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© 2014 Springer International Publishing Switzerland

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Yuan, JH., Zhu, HD., Gan, Y., Shang, L. (2014). Enhanced Local Ternary Pattern for Texture Classification. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_48

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  • DOI: https://doi.org/10.1007/978-3-319-09333-8_48

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09332-1

  • Online ISBN: 978-3-319-09333-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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