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Temporal Structure Tree in Digital Linear Scale Space

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Scale Space Methods in Computer Vision (Scale-Space 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2695))

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Abstract

This paper focuses on the computation of stationary curves, which are sometimes called fingerprints for one dimensional real signals in the linear scale space. Images for the analysis in the linear scale space are expressed as digital images for each quantized scale. Therefore, we develop a discrete version of the linear scale space analysis, employing the results of digital image analysis. For the application of linear scale space analysis to the time-varying images and objects, our method has advantages, because our method is based on the digital geometry on a plane which is suitable for the computation in digital computers.

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References

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Author information

Authors and Affiliations

  1. National Institute of Informatics, Japan

    Atsushi Imiya

  2. Institute of Media and Information Technology, Chiba University, Japan

    Atsushi Imiya & Tomoya Sakai

  3. School of Science and Technology, Chiba University, Japan

    Tateshi Sugiura

  4. Department of Information and Image Sciences, Chiba University, Japan

    Yuichiro Kato

Authors
  1. Atsushi Imiya
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  2. Tateshi Sugiura
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  3. Tomoya Sakai
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  4. Yuichiro Kato
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Editor information

Editors and Affiliations

  1. Imaging Sciences, 5th Floor Thomas Guy House, Guy’s Campus, London, SE1 9RT, UK

    Lewis D. Griffin

  2. IT University of Copenhagen, Glentevej 67-69, Copenhagen NV, Denmark

    Martin Lillholm

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© 2003 Springer-Verlag Berlin Heidelberg

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Imiya, A., Sugiura, T., Sakai, T., Kato, Y. (2003). Temporal Structure Tree in Digital Linear Scale Space. In: Griffin, L.D., Lillholm, M. (eds) Scale Space Methods in Computer Vision. Scale-Space 2003. Lecture Notes in Computer Science, vol 2695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44935-3_25

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  • DOI: https://doi.org/10.1007/3-540-44935-3_25

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40368-5

  • Online ISBN: 978-3-540-44935-5

  • eBook Packages: Springer Book Archive

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