Topology preserving parallel smoothing for 3D binary images

Gábor Németh, Péter Kardos, Kálmán Palágyi

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

2 Citations (Scopus)

Abstract

This paper presents a new algorithm for smoothing 3D binary images in a topology preserving way. Our algorithm is a reduction operator: some border points that are considered as extremities are removed. The proposed method is composed of two parallel reduction operators. We are to apply our smoothing algorithm as an iteration-by-iteration pruning for reducing the noise sensitivity of 3D parallel surface-thinning algorithms. An efficient implementation of our algorithm is sketched and its topological correctness for (26,6) pictures is proved.

Original languageEnglish
Title of host publicationComputational Modeling of Objects Represented in Images - Second International Symposium, CompIMAGE 2010, Proceedings
Pages287-298
Number of pages12
DOIs
Publication statusPublished - May 21 2010
Event2nd International Symposium on Computational Modeling of Objects Represented in Images, Fundamentals, Methods and Applications, CompIMAGE 2010 - Buffalo, NY, United States
Duration: May 5 2010May 7 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6026 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Symposium on Computational Modeling of Objects Represented in Images, Fundamentals, Methods and Applications, CompIMAGE 2010
CountryUnited States
CityBuffalo, NY
Period5/5/105/7/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Németh, G., Kardos, P., & Palágyi, K. (2010). Topology preserving parallel smoothing for 3D binary images. In Computational Modeling of Objects Represented in Images - Second International Symposium, CompIMAGE 2010, Proceedings (pp. 287-298). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6026 LNCS). https://doi.org/10.1007/978-3-642-12712-0_26