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Parametric Shape Descriptor Based on a Scalable Boundary-Skeleton Model

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Intelligent Data Processing (IDP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 794))

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Abstract

A parametric shape descriptor based on the relation between contour convexities and skeleton’s branches of a shape is suggested. The descriptor contains the set of a polygonal figure convex vertices approximating a raster image and estimations of significance for curvature features corresponding to the vertices. The estimations are calculated with use of a boundary-skeleton shape models family generated by the polygonal figure. The applications of the suggested shape descriptor to the face profile line segmentation and content based image retrieval are described.

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Acknowledgements

The research was supported by the Russian Foundation for Basic Research (projects No. 14-07-00736, 17-07-01432, 17-20-02222).

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Authors and Affiliations

  1. Federal Research Center “Computer Science and Control” of RAS, Moscow, Russia

    Ivan Reyer & Ksenia Aminova

Authors
  1. Ivan Reyer
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  2. Ksenia Aminova
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Correspondence to Ivan Reyer .

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Editors and Affiliations

  1. Moscow Institute of Physics and Technology, Dolgoprudny, Russia

    Vadim V. Strijov

  2. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  3. Yandex School of Data Analysis, Moscow, Russia

    Konstantin V. Vorontsov

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Reyer, I., Aminova, K. (2019). Parametric Shape Descriptor Based on a Scalable Boundary-Skeleton Model. In: Strijov, V., Ignatov, D., Vorontsov, K. (eds) Intelligent Data Processing. IDP 2016. Communications in Computer and Information Science, vol 794. Springer, Cham. https://doi.org/10.1007/978-3-030-35400-8_11

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  • DOI: https://doi.org/10.1007/978-3-030-35400-8_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35399-5

  • Online ISBN: 978-3-030-35400-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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