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Enlacement and Interlacement Shape Descriptors

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Pattern Recognition and Artificial Intelligence (ICPRAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12068))

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Abstract

We propose a novel approach to characterize complex 2D shapes based on enlacement and interlacement directional spatial relations. This new relational concept allows to assess in a polar space how the concave parts of objects are intertwined following a set of directions. In addition, such a spatial relationship has an interesting behavior considering the common properties in pattern recognition such as translation, rotation, scale and symmetry. A shape descriptor is defined by considering the enlacement of its own shape and the disk area that surrounds it. An experimental study carried out on two datasets of binary shapes highlights the discriminating ability of these new shape descriptors.

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Author information

Authors and Affiliations

  1. Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI UMR 5800, 33400, Talence, France

    Michaël Clément

  2. Université de Paris, LIPADE, 75006, Paris, France

    Camille Kurtz & Laurent Wendling

Authors
  1. Michaël Clément
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  2. Camille Kurtz
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  3. Laurent Wendling
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Corresponding author

Correspondence to Michaël Clément .

Editor information

Editors and Affiliations

  1. East China Normal University, Shanghai, China

    Yue Lu

  2. Paris Descartes University, Paris, France

    Nicole Vincent

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Pong Chi Yuen

  4. Sun Yat-sen University, Guangzhou, China

    Wei-Shi Zheng

  5. Polytechnique Montréal, Montreal, QC, Canada

    Farida Cheriet

  6. Concordia University, Montreal, QC, Canada

    Ching Y. Suen

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Clément, M., Kurtz, C., Wendling, L. (2020). Enlacement and Interlacement Shape Descriptors. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_45

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  • DOI: https://doi.org/10.1007/978-3-030-59830-3_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59829-7

  • Online ISBN: 978-3-030-59830-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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