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Industrial Conference on Data Mining

ICDM 2011: Advances in Data Mining. Applications and Theoretical Aspects pp 147–160Cite as

Recognition of Porosity in Wood Microscopic Anatomical Images

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6870))

Abstract

The size and configuration of pores are key features for wood identification. In this paper, these features are extracted and then used for construction of a decision tree to recognize three different kinds of pore distributions in wood microscopic images. The contribution of this paper lies in three aspects. Firstly, two different sets of features about pores were proposed and extracted; Secondly, two decision trees were built with those two sets by C4.5 algorithm; Finally, the acceptable recognition results of up to 75.6% were obtained and the possibility to improve was discussed.

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Author information

Authors and Affiliations

  1. Graduate School of Information Science and Technology, Hokkaido University, Sapporo, 060-0814, Japan

    Shen Pan & Mineichi Kudo

  2. Department of Information Management and Information Systems, Hefei University of Technology, Hefei, 230009, China

    Shen Pan

Authors
  1. Shen Pan
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  2. Mineichi Kudo
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Editor information

Editors and Affiliations

  1. Intitute of Computer Vision and Applied Computer Sciences, IBaI, Kohlenstraße 2, 04107, Leipzig, Germany

    Petra Perner

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© 2011 Springer-Verlag Berlin Heidelberg

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Pan, S., Kudo, M. (2011). Recognition of Porosity in Wood Microscopic Anatomical Images. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2011. Lecture Notes in Computer Science(), vol 6870. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23184-1_12

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  • DOI: https://doi.org/10.1007/978-3-642-23184-1_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23183-4

  • Online ISBN: 978-3-642-23184-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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