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An Improved Directional Convexity Measure for Binary Images

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Image Analysis and Recognition (ICIAR 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10317))

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Abstract

Balázs et al. (Fundamenta Informaticae 141:151–167, 2015) proposed a measure of directional convexity of binary images based on the geometric definition of shape convexity. The measure is useful for various applications of digital image processing and pattern recognition, especially in binary tomography. Here we provide an improvement of this measure making it to follow better the intuitive concept of geometric convexity and to be more suitable to distinguish between thick and thin objects.

The research was supported by the NKFIH OTKA [grant number K112998].

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Author information

Authors and Affiliations

  1. Department of Image Processing and Computer Graphics, University of Szeged, Árpád tér 2, Szeged, H6720, Hungary

    Péter Bodnár & Péter Balázs

Authors
  1. Péter Bodnár
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  2. Péter Balázs
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Correspondence to Péter Bodnár .

Editor information

Editors and Affiliations

  1. University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

  2. University of Porto, Porto, Portugal

    Aurélio Campilho

  3. Politechnique Montreal, Montreal, Québec, Canada

    Farida Cheriet

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Bodnár, P., Balázs, P. (2017). An Improved Directional Convexity Measure for Binary Images. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_31

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  • DOI: https://doi.org/10.1007/978-3-319-59876-5_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59875-8

  • Online ISBN: 978-3-319-59876-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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