Local mean multiphase segmentation with HMMF models

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Local mean multiphase segmentation with HMMF models. / Hansen, Jacob Daniel Kirstejn; Lauze, Francois Bernard.

Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings. ed. / François Lauze; Yiqui Dong; Anders Bjorholm Dahl. Springer, 2017. p. 396-407 (Lecture notes in computer science, Vol. 10302).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Hansen, JDK & Lauze, FB 2017, Local mean multiphase segmentation with HMMF models. in F Lauze, Y Dong & AB Dahl (eds), Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings. Springer, Lecture notes in computer science, vol. 10302, pp. 396-407, 6th International Conference on Scale Space and Variational Methods in Computer Vision, Kolding, Denmark, 04/06/2017. https://doi.org/10.1007/978-3-319-58771-4_32

APA

Hansen, J. D. K., & Lauze, F. B. (2017). Local mean multiphase segmentation with HMMF models. In F. Lauze, Y. Dong, & A. B. Dahl (Eds.), Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings (pp. 396-407). Springer. Lecture notes in computer science Vol. 10302 https://doi.org/10.1007/978-3-319-58771-4_32

Vancouver

Hansen JDK, Lauze FB. Local mean multiphase segmentation with HMMF models. In Lauze F, Dong Y, Dahl AB, editors, Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings. Springer. 2017. p. 396-407. (Lecture notes in computer science, Vol. 10302). https://doi.org/10.1007/978-3-319-58771-4_32

Author

Hansen, Jacob Daniel Kirstejn ; Lauze, Francois Bernard. / Local mean multiphase segmentation with HMMF models. Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings. editor / François Lauze ; Yiqui Dong ; Anders Bjorholm Dahl. Springer, 2017. pp. 396-407 (Lecture notes in computer science, Vol. 10302).

Bibtex

@inproceedings{143181827b184b6490e0ca950da0caa4,
title = "Local mean multiphase segmentation with HMMF models",
abstract = "This paper presents two similar multiphase segmentation methods for recovery of segments in complex weakly structured images, with local and global bias fields, because they can occur in some X-ray CT imaging modalities. Derived from the Mumford-Shah functional, the proposed methods assume a fixed number of classes. They use local image average as discriminative features. Region labels are modelled by Hidden Markov Measure Field Models. The resulting problems are solved by straightforward alternate minimisation methods, particularly simple in the case of quadratic regularisation of the labels. We demonstrate the proposed methods{\textquoteright} capabilities on synthetic data using classical segmentation criteria as well as criteria specific to geoscience. We also present a few examples using real data.",
author = "Hansen, {Jacob Daniel Kirstejn} and Lauze, {Francois Bernard}",
year = "2017",
doi = "10.1007/978-3-319-58771-4_32",
language = "English",
isbn = "978-3-319-58770-7",
series = "Lecture notes in computer science",
publisher = "Springer",
pages = "396--407",
editor = "Fran{\c c}ois Lauze and Yiqui Dong and Dahl, {Anders Bjorholm}",
booktitle = "Scale Space and Variational Methods in Computer Vision",
address = "Switzerland",
note = "null ; Conference date: 04-06-2017 Through 08-06-2017",

}

RIS

TY - GEN

T1 - Local mean multiphase segmentation with HMMF models

AU - Hansen, Jacob Daniel Kirstejn

AU - Lauze, Francois Bernard

N1 - Conference code: 6

PY - 2017

Y1 - 2017

N2 - This paper presents two similar multiphase segmentation methods for recovery of segments in complex weakly structured images, with local and global bias fields, because they can occur in some X-ray CT imaging modalities. Derived from the Mumford-Shah functional, the proposed methods assume a fixed number of classes. They use local image average as discriminative features. Region labels are modelled by Hidden Markov Measure Field Models. The resulting problems are solved by straightforward alternate minimisation methods, particularly simple in the case of quadratic regularisation of the labels. We demonstrate the proposed methods’ capabilities on synthetic data using classical segmentation criteria as well as criteria specific to geoscience. We also present a few examples using real data.

AB - This paper presents two similar multiphase segmentation methods for recovery of segments in complex weakly structured images, with local and global bias fields, because they can occur in some X-ray CT imaging modalities. Derived from the Mumford-Shah functional, the proposed methods assume a fixed number of classes. They use local image average as discriminative features. Region labels are modelled by Hidden Markov Measure Field Models. The resulting problems are solved by straightforward alternate minimisation methods, particularly simple in the case of quadratic regularisation of the labels. We demonstrate the proposed methods’ capabilities on synthetic data using classical segmentation criteria as well as criteria specific to geoscience. We also present a few examples using real data.

UR - http://www.scopus.com/inward/record.url?scp=85019761496&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-58771-4_32

DO - 10.1007/978-3-319-58771-4_32

M3 - Article in proceedings

AN - SCOPUS:85019761496

SN - 978-3-319-58770-7

T3 - Lecture notes in computer science

SP - 396

EP - 407

BT - Scale Space and Variational Methods in Computer Vision

A2 - Lauze, François

A2 - Dong, Yiqui

A2 - Dahl, Anders Bjorholm

PB - Springer

Y2 - 4 June 2017 through 8 June 2017

ER -

ID: 179559150