### Abstract

It is important in many applications of 3D and higher dimensional segmentation that the resulting segments of voxels are not required to have only one connected component, as in some of extant methods. Indeed, it is generally necessary to be able to automatically determine the appropriate number of connected components. More generally, for a larger class of applications, the segments should have no topological restrictions at all. For instance, each connected component should be allowed to have as many holes as appropriate to fit the data. We propose a method based on a graph algorithm to automatically segment 3D and higher-dimensional images into two segments without user intervention, with no topological restriction on the solution, and in such a way that the solution is optimal under a precisely defined optimization criterion.

Original language | English |
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Title of host publication | Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005 |

Pages | 488-491 |

Number of pages | 4 |

Publication status | Published - 2005 |

Externally published | Yes |

Event | 9th IAPR Conference on Machine Vision Applications, MVA 2005 - Tsukuba Science City Duration: 2005 May 16 → 2005 May 18 |

### Other

Other | 9th IAPR Conference on Machine Vision Applications, MVA 2005 |
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City | Tsukuba Science City |

Period | 05/5/16 → 05/5/18 |

### ASJC Scopus subject areas

- Computer Vision and Pattern Recognition

### Cite this

*Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005*(pp. 488-491)

**Higher-dimensional segmentation by minimum-cut algorithm.** / Ishikawa, Hiroshi; Geiger, Davi.

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

*Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005.*pp. 488-491, 9th IAPR Conference on Machine Vision Applications, MVA 2005, Tsukuba Science City, 05/5/16.

}

TY - GEN

T1 - Higher-dimensional segmentation by minimum-cut algorithm

AU - Ishikawa, Hiroshi

AU - Geiger, Davi

PY - 2005

Y1 - 2005

N2 - It is important in many applications of 3D and higher dimensional segmentation that the resulting segments of voxels are not required to have only one connected component, as in some of extant methods. Indeed, it is generally necessary to be able to automatically determine the appropriate number of connected components. More generally, for a larger class of applications, the segments should have no topological restrictions at all. For instance, each connected component should be allowed to have as many holes as appropriate to fit the data. We propose a method based on a graph algorithm to automatically segment 3D and higher-dimensional images into two segments without user intervention, with no topological restriction on the solution, and in such a way that the solution is optimal under a precisely defined optimization criterion.

AB - It is important in many applications of 3D and higher dimensional segmentation that the resulting segments of voxels are not required to have only one connected component, as in some of extant methods. Indeed, it is generally necessary to be able to automatically determine the appropriate number of connected components. More generally, for a larger class of applications, the segments should have no topological restrictions at all. For instance, each connected component should be allowed to have as many holes as appropriate to fit the data. We propose a method based on a graph algorithm to automatically segment 3D and higher-dimensional images into two segments without user intervention, with no topological restriction on the solution, and in such a way that the solution is optimal under a precisely defined optimization criterion.

UR - http://www.scopus.com/inward/record.url?scp=84872544674&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84872544674&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84872544674

SN - 4901122045

SN - 9784901122047

SP - 488

EP - 491

BT - Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005

ER -