### Abstract

Thinning is a frequently applied technique for extracting skeletons or medial surfaces from volumetric binary objects. It is an iterative object reduction: border points that satisfy certain topological and geometric constraints are deleted in a thinning phase. Sequential thinning algorithms may alter just one point at a time, while parallel algorithms can delete a set of border points simultaneously. Two thinning algorithms are said to be equivalent if they can produce the same result for each input binary picture. This work shows that it is possible to construct subiteration-based equivalent sequential and parallel surface-thinning algorithms. The proposed four pairs of algorithms can be implemented directly on a conventional sequential computer or on a parallel computing device. All of them preserve topology for (26, 6) pictures.

Original language | English |
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Title of host publication | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |

Publisher | Springer Verlag |

Pages | 31-45 |

Number of pages | 15 |

Volume | 9448 |

ISBN (Print) | 9783319261447 |

DOIs | |

Publication status | Published - 2015 |

Event | 17th International Workshop on Combinatorial Image Analysis, IWCIA 2015 - Kolkata, India Duration: Nov 24 2015 → Nov 27 2015 |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 9448 |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 17th International Workshop on Combinatorial Image Analysis, IWCIA 2015 |
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Country | India |

City | Kolkata |

Period | 11/24/15 → 11/27/15 |

### Fingerprint

### Keywords

- Discrete geometry
- Discrete topology
- Equivalent thinning algorithms
- Skeletons
- Subiteration-based thinning

### ASJC Scopus subject areas

- Computer Science(all)
- Theoretical Computer Science

### Cite this

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)*(Vol. 9448, pp. 31-45). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9448). Springer Verlag. https://doi.org/10.1007/978-3-319-26145-4_3

**Equivalent sequential and parallel subiteration-based surface-thinning algorithms.** / Palágyi, K.; Németh, Gábor; Kardos, Péter.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).*vol. 9448, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9448, Springer Verlag, pp. 31-45, 17th International Workshop on Combinatorial Image Analysis, IWCIA 2015, Kolkata, India, 11/24/15. https://doi.org/10.1007/978-3-319-26145-4_3

}

TY - GEN

T1 - Equivalent sequential and parallel subiteration-based surface-thinning algorithms

AU - Palágyi, K.

AU - Németh, Gábor

AU - Kardos, Péter

PY - 2015

Y1 - 2015

N2 - Thinning is a frequently applied technique for extracting skeletons or medial surfaces from volumetric binary objects. It is an iterative object reduction: border points that satisfy certain topological and geometric constraints are deleted in a thinning phase. Sequential thinning algorithms may alter just one point at a time, while parallel algorithms can delete a set of border points simultaneously. Two thinning algorithms are said to be equivalent if they can produce the same result for each input binary picture. This work shows that it is possible to construct subiteration-based equivalent sequential and parallel surface-thinning algorithms. The proposed four pairs of algorithms can be implemented directly on a conventional sequential computer or on a parallel computing device. All of them preserve topology for (26, 6) pictures.

AB - Thinning is a frequently applied technique for extracting skeletons or medial surfaces from volumetric binary objects. It is an iterative object reduction: border points that satisfy certain topological and geometric constraints are deleted in a thinning phase. Sequential thinning algorithms may alter just one point at a time, while parallel algorithms can delete a set of border points simultaneously. Two thinning algorithms are said to be equivalent if they can produce the same result for each input binary picture. This work shows that it is possible to construct subiteration-based equivalent sequential and parallel surface-thinning algorithms. The proposed four pairs of algorithms can be implemented directly on a conventional sequential computer or on a parallel computing device. All of them preserve topology for (26, 6) pictures.

KW - Discrete geometry

KW - Discrete topology

KW - Equivalent thinning algorithms

KW - Skeletons

KW - Subiteration-based thinning

UR - http://www.scopus.com/inward/record.url?scp=84955304523&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84955304523&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-26145-4_3

DO - 10.1007/978-3-319-26145-4_3

M3 - Conference contribution

AN - SCOPUS:84955304523

SN - 9783319261447

VL - 9448

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 31

EP - 45

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

ER -