2D thinning algorithms with revised endpixel preservation

Gábor Németh, Péter Kardos, Kálmán Palágyi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Skeletons are shape descriptors that summarize the general forms of objects. Thinning is a frequently applied technique for digital binary pictures to extract skeleton-like shape features. Most of the existing thinning algorithms preserve endpixels that provide relevant geometrical information relative to the shape of the objects. The drawback of this approach is that it may produce numerous unwanted side branches. In this paper we propose a novel strategy to overcome this problem. We present a thinning strategy, where some endpixels can be deleted.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings
EditorsMohamed Kamel, Aurélio Campilho
PublisherSpringer Verlag
Pages65-72
Number of pages8
ISBN (Electronic)9783319117577
DOIs
Publication statusPublished - Jan 1 2014
Event11th International Conference on Image Analysis and Recognition, ICIAR 2014 - Vilamoura, Portugal
Duration: Oct 22 2014Oct 24 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8814
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other11th International Conference on Image Analysis and Recognition, ICIAR 2014
CountryPortugal
CityVilamoura
Period10/22/1410/24/14

Keywords

  • Endpixel revision
  • Shape representation
  • Thinning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Németh, G., Kardos, P., & Palágyi, K. (2014). 2D thinning algorithms with revised endpixel preservation. In M. Kamel, & A. Campilho (Eds.), Image Analysis and Recognition - 11th International Conference, ICIAR 2014, Proceedings (pp. 65-72). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8814). Springer Verlag. https://doi.org/10.1007/978-3-319-11758-4_8