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Texture classification by local surface fitting

  • Image Segmentation
  • Conference paper
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Book cover Pattern Recognition (PAR 1988)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 301))

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Abstract

We present in this paper a new algorithm for texture classification based on local surface fitting of images. At the first step, surface fitting is defined in a predefined local neighborhood and is done at every pixel generating a number of coefficient data fields or texture feature images; Then, texture features are extracted from these feature images and used for texture classification. Initial experimental results show that the algorithm is simple, compact and flexible. It is also suitable for parallel implementation.

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References

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Authors and Affiliations

  1. Signal Processing Section, Dept. of Electrical Engineering, Imperial College, SW7 2BT, London

    Tieniu Tan & Anthony G Constantinides

Authors
  1. Tieniu Tan
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  2. Anthony G Constantinides
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Editor information

J. Kittler

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© 1988 Springer-Verlag Berlin Heidelberg

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Tan, T., Constantinides, A.G. (1988). Texture classification by local surface fitting. In: Kittler, J. (eds) Pattern Recognition. PAR 1988. Lecture Notes in Computer Science, vol 301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-19036-8_4

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  • DOI: https://doi.org/10.1007/3-540-19036-8_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-19036-3

  • Online ISBN: 978-3-540-38947-7

  • eBook Packages: Springer Book Archive

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