Higher-dimensional segmentation by minimum-cut algorithm

Hiroshi Ishikawa, Davi Geiger

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

1 Citation (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005
Pages488-491
Number of pages4
Publication statusPublished - 2005
Externally publishedYes
Event9th IAPR Conference on Machine Vision Applications, MVA 2005 - Tsukuba Science City
Duration: 2005 May 162005 May 18

Other

Other9th IAPR Conference on Machine Vision Applications, MVA 2005
CityTsukuba Science City
Period05/5/1605/5/18

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Ishikawa, H., & Geiger, D. (2005). Higher-dimensional segmentation by minimum-cut algorithm. In 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.

Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005. 2005. p. 488-491.

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

Ishikawa, H & Geiger, D 2005, Higher-dimensional segmentation by minimum-cut algorithm. in 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.
Ishikawa H, Geiger D. Higher-dimensional segmentation by minimum-cut algorithm. In Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005. 2005. p. 488-491
Ishikawa, Hiroshi ; Geiger, Davi. / Higher-dimensional segmentation by minimum-cut algorithm. Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005. 2005. pp. 488-491
@inproceedings{a8b1724e9f3a4aa99e9197b25d7a7c85,
title = "Higher-dimensional segmentation by minimum-cut algorithm",
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.",
author = "Hiroshi Ishikawa and Davi Geiger",
year = "2005",
language = "English",
isbn = "4901122045",
pages = "488--491",
booktitle = "Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005",

}

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

SN - 4901122045

SN - 9784901122047

SP - 488

EP - 491

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

ER -