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\(\alpha \)-Pixels for Hierarchical Analysis of Digital Objects

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Scale Space and Variational Methods in Computer Vision (SSVM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14009))

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Abstract

In this paper, we apply a scale space analysis method to digital geometry. We deal with the reconstruction of Euclidean polylines controlling the size of pixels in discrete space. Numerical examples show that the size of pixels of digital geometry processes the scale for the hierarchical reconstruction of Euclidean objects from digital objects.

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Acknowledgement

This research was supported by the Grants-in-Aid for Scientific Research funded by the Japan Society for the Promotion of Science, under 20K11881.

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Authors and Affiliations

  1. School of Science and Engineering, Chiba University, Yayoi-cho 1-33, Inage-ku, Chiba, 263-8522, Japan

    Kouki Tosaka

  2. Institute of Management and Information Technologies, Chiba University, Yayoi-cho 1-33, Inage-ku, Chiba, 263-8522, Japan

    Atsushi Imiya

Authors
  1. Kouki Tosaka
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  2. Atsushi Imiya
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Corresponding author

Correspondence to Atsushi Imiya .

Editor information

Editors and Affiliations

  1. CNRS, Université Côte d'Azur, Sophia-Antipolis, France

    Luca Calatroni

  2. University of Insubria, Como, Italy

    Marco Donatelli

  3. University of Bologna, Bologna, Italy

    Serena Morigi

  4. University of Modena and Reggio Emilia, Modena, Italy

    Marco Prato

  5. University of Genova, Genova, Italy

    Matteo Santacesaria

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Tosaka, K., Imiya, A. (2023). \(\alpha \)-Pixels for Hierarchical Analysis of Digital Objects. In: Calatroni, L., Donatelli, M., Morigi, S., Prato, M., Santacesaria, M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2023. Lecture Notes in Computer Science, vol 14009. Springer, Cham. https://doi.org/10.1007/978-3-031-31975-4_51

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  • DOI: https://doi.org/10.1007/978-3-031-31975-4_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31974-7

  • Online ISBN: 978-3-031-31975-4

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