Standard
Segmentation of 2D and 3D Objects with Intrinsically Similarity Invariant Shape Regularisers. / Hansen, Jacob Daniel Kirstejn; Lauze, François.
Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings. ed. / Jan Lellmann; Jan Modersitzki; Martin Burger. Springer, 2019. p. 369-380 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11603 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
Hansen, JDK
& Lauze, F 2019,
Segmentation of 2D and 3D Objects with Intrinsically Similarity Invariant Shape Regularisers. in J Lellmann, J Modersitzki & M Burger (eds),
Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11603 LNCS, pp. 369-380, 7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019, Hofgeismar, Germany,
30/06/2019.
https://doi.org/10.1007/978-3-030-22368-7_29APA
Hansen, J. D. K.
, & Lauze, F. (2019).
Segmentation of 2D and 3D Objects with Intrinsically Similarity Invariant Shape Regularisers. In J. Lellmann, J. Modersitzki, & M. Burger (Eds.),
Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings (pp. 369-380). Springer. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11603 LNCS
https://doi.org/10.1007/978-3-030-22368-7_29Vancouver
Hansen JDK
, Lauze F.
Segmentation of 2D and 3D Objects with Intrinsically Similarity Invariant Shape Regularisers. In Lellmann J, Modersitzki J, Burger M, editors, Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings. Springer. 2019. p. 369-380. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11603 LNCS).
https://doi.org/10.1007/978-3-030-22368-7_29Author
Hansen, Jacob Daniel Kirstejn ; Lauze, François. / Segmentation of 2D and 3D Objects with Intrinsically Similarity Invariant Shape Regularisers. Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings. editor / Jan Lellmann ; Jan Modersitzki ; Martin Burger. Springer, 2019. pp. 369-380 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11603 LNCS).
Bibtex
@inproceedings{fd33d213593243d3b79191a64c828e32,
title = "Segmentation of 2D and 3D Objects with Intrinsically Similarity Invariant Shape Regularisers",
abstract = "This paper presents a 2D and 3D variational segmentation approach based on a similarity invariant, i.e., translation, scaling, and rotation invariant shape regulariser. Indeed, shape moments of order up{\^A} to 2 for shapes with limited symmetries can be combined to provide a shape normalisation for the group of similarities. In order to obtain a segmentation objective function, a two-means or two-local-means data term is added to it. Segmentation is then obtained by standard gradient descent on it. We demonstrate the capabilities of the approach on a series of experiments, of different complexity levels. We specifically target rat brain shapes in MR scans, where the setting is complex, because of bias field and complex anatomical structures. Our last experiments show that our approach is indeed capable of recovering brain shapes automatically.",
author = "Hansen, {Jacob Daniel Kirstejn} and Fran{\c c}ois Lauze",
year = "2019",
doi = "10.1007/978-3-030-22368-7_29",
language = "English",
isbn = "9783030223670",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "369--380",
editor = "Jan Lellmann and Jan Modersitzki and Martin Burger",
booktitle = "Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings",
address = "Switzerland",
note = "7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019 ; Conference date: 30-06-2019 Through 04-07-2019",
}
RIS
TY - GEN
T1 - Segmentation of 2D and 3D Objects with Intrinsically Similarity Invariant Shape Regularisers
AU - Hansen, Jacob Daniel Kirstejn
AU - Lauze, François
PY - 2019
Y1 - 2019
N2 - This paper presents a 2D and 3D variational segmentation approach based on a similarity invariant, i.e., translation, scaling, and rotation invariant shape regulariser. Indeed, shape moments of order up to 2 for shapes with limited symmetries can be combined to provide a shape normalisation for the group of similarities. In order to obtain a segmentation objective function, a two-means or two-local-means data term is added to it. Segmentation is then obtained by standard gradient descent on it. We demonstrate the capabilities of the approach on a series of experiments, of different complexity levels. We specifically target rat brain shapes in MR scans, where the setting is complex, because of bias field and complex anatomical structures. Our last experiments show that our approach is indeed capable of recovering brain shapes automatically.
AB - This paper presents a 2D and 3D variational segmentation approach based on a similarity invariant, i.e., translation, scaling, and rotation invariant shape regulariser. Indeed, shape moments of order up to 2 for shapes with limited symmetries can be combined to provide a shape normalisation for the group of similarities. In order to obtain a segmentation objective function, a two-means or two-local-means data term is added to it. Segmentation is then obtained by standard gradient descent on it. We demonstrate the capabilities of the approach on a series of experiments, of different complexity levels. We specifically target rat brain shapes in MR scans, where the setting is complex, because of bias field and complex anatomical structures. Our last experiments show that our approach is indeed capable of recovering brain shapes automatically.
UR - http://www.scopus.com/inward/record.url?scp=85068482213&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-22368-7_29
DO - 10.1007/978-3-030-22368-7_29
M3 - Article in proceedings
AN - SCOPUS:85068482213
SN - 9783030223670
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 369
EP - 380
BT - Scale Space and Variational Methods in Computer Vision - 7th International Conference, SSVM 2019, Proceedings
A2 - Lellmann, Jan
A2 - Modersitzki, Jan
A2 - Burger, Martin
PB - Springer
T2 - 7th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2019
Y2 - 30 June 2019 through 4 July 2019
ER -
