3D parallel thinning algorithms based on isthmuses

Gábor Németh, K. Palágyi

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

4 Citations (Scopus)

Abstract

Thinning is a widely used technique to obtain skeleton-like shape features (i.e., centerlines and medial surfaces) from digital binary objects. Conventional thinning algorithms preserve endpoints to provide important geometric information relative to the object to be represented. An alternative strategy is also proposed that preserves isthmuses (i.e., generalization of curve/surface interior points). In this paper we present ten 3D parallel isthmus-based thinning algorithm variants that are derived from some sufficient conditions for topology preserving reductions.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages325-335
Number of pages11
Volume7517 LNCS
DOIs
Publication statusPublished - 2012
Event14th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2012 - Brno, Czech Republic
Duration: Sep 4 2012Sep 7 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7517 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other14th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2012
CountryCzech Republic
CityBrno
Period9/4/129/7/12

Fingerprint

Thinning
Shape Feature
Topology
Interior Point
Skeleton
Binary
Curve
Sufficient Conditions
Alternatives
Object

Keywords

  • Feature extraction
  • Shape analysis
  • Skeletons
  • Thinning algorithms
  • Topology preservation

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Németh, G., & Palágyi, K. (2012). 3D parallel thinning algorithms based on isthmuses. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7517 LNCS, pp. 325-335). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7517 LNCS). https://doi.org/10.1007/978-3-642-33140-4_29

3D parallel thinning algorithms based on isthmuses. / Németh, Gábor; Palágyi, K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7517 LNCS 2012. p. 325-335 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7517 LNCS).

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

Németh, G & Palágyi, K 2012, 3D parallel thinning algorithms based on isthmuses. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7517 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7517 LNCS, pp. 325-335, 14th International Conference on Advanced Concepts for Intelligent Vision Systems, ACIVS 2012, Brno, Czech Republic, 9/4/12. https://doi.org/10.1007/978-3-642-33140-4_29
Németh G, Palágyi K. 3D parallel thinning algorithms based on isthmuses. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7517 LNCS. 2012. p. 325-335. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-33140-4_29
Németh, Gábor ; Palágyi, K. / 3D parallel thinning algorithms based on isthmuses. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7517 LNCS 2012. pp. 325-335 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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