Topology preserving parallel smoothing for 3D binary images

Gábor Németh, Péter Kardos, K. 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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages287-298
Number of pages12
Volume6026 LNCS
DOIs
Publication statusPublished - 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)03029743
ISSN (Electronic)16113349

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

Fingerprint

Binary images
Binary Image
3D Image
Smoothing
Topology
Mathematical operators
Iteration
Smoothing Algorithm
Thinning
Operator
Pruning
Efficient Implementation
Correctness

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Németh, G., Kardos, P., & Palágyi, K. (2010). Topology preserving parallel smoothing for 3D binary images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6026 LNCS, 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

Topology preserving parallel smoothing for 3D binary images. / Németh, Gábor; Kardos, Péter; Palágyi, K.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6026 LNCS 2010. p. 287-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6026 LNCS).

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

Németh, G, Kardos, P & Palágyi, K 2010, Topology preserving parallel smoothing for 3D binary images. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 6026 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6026 LNCS, pp. 287-298, 2nd International Symposium on Computational Modeling of Objects Represented in Images, Fundamentals, Methods and Applications, CompIMAGE 2010, Buffalo, NY, United States, 5/5/10. https://doi.org/10.1007/978-3-642-12712-0_26
Németh G, Kardos P, Palágyi K. Topology preserving parallel smoothing for 3D binary images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6026 LNCS. 2010. p. 287-298. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-12712-0_26
Németh, Gábor ; Kardos, Péter ; Palágyi, K. / Topology preserving parallel smoothing for 3D binary images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6026 LNCS 2010. pp. 287-298 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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