### Abstract

Thinning is a frequently applied skeletonization technique: border points that satisfy certain topological and geometric constraints are deleted in iteration steps. 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 the existing 2D fully parallel thinning algorithm proposed by Manzanera et al. is equivalent to a topology-preserving sequential thinning algorithm with the same deletion rule.

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 | 91-100 |

Number of pages | 10 |

Volume | 8466 LNCS |

ISBN (Print) | 9783319071473 |

DOIs | |

Publication status | Published - 2014 |

Event | 16th International Workshop on Combinatorial Image Analysis, IWCIA 2014 - Brno, Czech Republic Duration: May 28 2014 → May 30 2014 |

### 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 | 8466 LNCS |

ISSN (Print) | 03029743 |

ISSN (Electronic) | 16113349 |

### Other

Other | 16th International Workshop on Combinatorial Image Analysis, IWCIA 2014 |
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Country | Czech Republic |

City | Brno |

Period | 5/28/14 → 5/30/14 |

### Fingerprint

### Keywords

- Digital Topology
- Discrete Geometry
- Equivalent Thinning Algorithms
- 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. 8466 LNCS, pp. 91-100). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8466 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-07148-0_9

**Equivalent 2D sequential and parallel thinning algorithms.** / Palágyi, K.

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. 8466 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8466 LNCS, Springer Verlag, pp. 91-100, 16th International Workshop on Combinatorial Image Analysis, IWCIA 2014, Brno, Czech Republic, 5/28/14. https://doi.org/10.1007/978-3-319-07148-0_9

}

TY - GEN

T1 - Equivalent 2D sequential and parallel thinning algorithms

AU - Palágyi, K.

PY - 2014

Y1 - 2014

N2 - Thinning is a frequently applied skeletonization technique: border points that satisfy certain topological and geometric constraints are deleted in iteration steps. 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 the existing 2D fully parallel thinning algorithm proposed by Manzanera et al. is equivalent to a topology-preserving sequential thinning algorithm with the same deletion rule.

AB - Thinning is a frequently applied skeletonization technique: border points that satisfy certain topological and geometric constraints are deleted in iteration steps. 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 the existing 2D fully parallel thinning algorithm proposed by Manzanera et al. is equivalent to a topology-preserving sequential thinning algorithm with the same deletion rule.

KW - Digital Topology

KW - Discrete Geometry

KW - Equivalent Thinning Algorithms

KW - Thinning

UR - http://www.scopus.com/inward/record.url?scp=84901673995&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901673995&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-07148-0_9

DO - 10.1007/978-3-319-07148-0_9

M3 - Conference contribution

AN - SCOPUS:84901673995

SN - 9783319071473

VL - 8466 LNCS

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

SP - 91

EP - 100

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

PB - Springer Verlag

ER -