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Segment- and Arc-Based Vectorizations by Multi-scale/Irregular Tangential Covering

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Geometry and Vision (ISGV 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1386))

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Abstract

In this paper, we propose an original manner to employ a tangential cover algorithm - minDSS - in order to vectorize noisy digital contours. To do so, we exploit the representation of graphical objects by maximal primitives we have introduced in previous work. By calculating multi-scale and irregular isothetic representations of the contour, we obtained 1-D (one-dimensional) intervals, and achieved afterwards a decomposition into maximal line segments or circular arcs. By adapting minDSS to this sparse and irregular data of 1-D intervals supporting the maximal primitives, we are now able to reconstruct the input noisy objects into cyclic contours made of lines or arcs with a minimal number of primitives. We explain our novel complete pipeline in this work, and present its experimental evaluation by considering both synthetic and real image data.

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Notes

  1. 1.

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Author information

Authors and Affiliations

  1. Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, 63000, Clermont-Ferrand, France

    Antoine Vacavant

  2. Univ Lyon, Lyon 2, LIRIS, 69676, Lyon, France

    Bertrand Kerautret

  3. Université Clermont Auvergne, CNRS, ENSMSE, LIMOS, 63000, Clermont-Ferrand, France

    Fabien Feschet

Authors
  1. Antoine Vacavant
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  2. Bertrand Kerautret
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  3. Fabien Feschet
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Corresponding author

Correspondence to Antoine Vacavant .

Editor information

Editors and Affiliations

  1. Auckland University of Technology, Auckland, New Zealand

    Minh Nguyen

  2. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

  3. Auckland Bioengineering House, Auckland, New Zealand

    Harvey Ho

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Vacavant, A., Kerautret, B., Feschet, F. (2021). Segment- and Arc-Based Vectorizations by Multi-scale/Irregular Tangential Covering. In: Nguyen, M., Yan, W.Q., Ho, H. (eds) Geometry and Vision. ISGV 2021. Communications in Computer and Information Science, vol 1386. Springer, Cham. https://doi.org/10.1007/978-3-030-72073-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-72073-5_15

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  • Print ISBN: 978-3-030-72072-8

  • Online ISBN: 978-3-030-72073-5

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