Difference between revisions of "Publications/boutry.21.dgmm.2"
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| volume = 12708 |
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| − | | abstract = Many approaches exist to compute the distance between two trees in pattern recognition. These trees can be structures with or without values on their nodes or edges. |
+ | | abstract = Many approaches exist to compute the distance between two trees in pattern recognition. These trees can be structures with or without values on their nodes or edges. Howevernone of these distances take into account the shapes possibly associated to the nodes of the tree. For this reason, we propose in this paper a new distance between two trees of shapes based on the Hausdorff distance. This distance allows us to make inexact tree matching and to compute what we call residual trees, representing where two trees differ. We will also see that thanks to these residual trees, we can obtain good results in matter of brain tumor segmentation. This segmentation does not provide only a segmentation but also the tree of shapes corresponding to the segmentation and its depth map. |
| lrdepaper = http://www.lrde.epita.fr/dload/papers/boutry.21.dgmm.2.pdf |
| lrdepaper = http://www.lrde.epita.fr/dload/papers/boutry.21.dgmm.2.pdf |
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Discrete Geometry and Mathematical Morphology (DGMM)<nowiki>}</nowiki>, |
Discrete Geometry and Mathematical Morphology (DGMM)<nowiki>}</nowiki>, |
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year = 2021, |
year = 2021, |
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+ | month = may, |
pages = <nowiki>{</nowiki>67--78<nowiki>}</nowiki>, |
pages = <nowiki>{</nowiki>67--78<nowiki>}</nowiki>, |
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address = <nowiki>{</nowiki>Uppsala, Sweden<nowiki>}</nowiki>, |
address = <nowiki>{</nowiki>Uppsala, Sweden<nowiki>}</nowiki>, |
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Revision as of 10:54, 8 September 2021
- Authors
- Nicolas Boutry, Thierry Géraud
- Where
- Proceedings of the IAPR International Conference on Discrete Geometry and Mathematical Morphology (DGMM)
- Place
- Uppsala, Sweden
- Type
- inproceedings
- Publisher
- Springer
- Projects
- Olena
- Date
- 2021-03-02
Abstract
Many approaches exist to compute the distance between two trees in pattern recognition. These trees can be structures with or without values on their nodes or edges. Howevernone of these distances take into account the shapes possibly associated to the nodes of the tree. For this reason, we propose in this paper a new distance between two trees of shapes based on the Hausdorff distance. This distance allows us to make inexact tree matching and to compute what we call residual trees, representing where two trees differ. We will also see that thanks to these residual trees, we can obtain good results in matter of brain tumor segmentation. This segmentation does not provide only a segmentation but also the tree of shapes corresponding to the segmentation and its depth map.
Documents
Bibtex (lrde.bib)
@InProceedings{ boutry.21.dgmm.2,
author = {Nicolas Boutry and Thierry G\'eraud},
title = {A New Matching Algorithm between Trees of Shapes and its
Application to Brain Tumor Segmentation},
booktitle = {Proceedings of the IAPR International Conference on
Discrete Geometry and Mathematical Morphology (DGMM)},
year = 2021,
month = may,
pages = {67--78},
address = {Uppsala, Sweden},
series = {Lecture Notes in Computer Science},
volume = {12708},
publisher = {Springer},
abstract = {Many approaches exist to compute the distance between two
trees in pattern recognition. These trees can be structures
with or without values on their nodes or edges. However,
none of these distances take into account the shapes
possibly associated to the nodes of the tree. For this
reason, we propose in this paper a new distance between two
trees of shapes based on the Hausdorff distance. This
distance allows us to make inexact tree matching and to
compute what we call residual trees, representing where two
trees differ. We will also see that thanks to these
residual trees, we can obtain good results in matter of
brain tumor segmentation. This segmentation does not
provide only a segmentation but also the tree of shapes
corresponding to the segmentation and its depth map.},
doi = {10.1007/978-3-030-76657-3_4}
}