Difference between revisions of "Publications/boutry.21.dgmm.2"
From LRDE
(Created page with "{{Publication | published = true | date = 2021-03-02 | authors = Nicolas Boutry, Thierry Géraud | title = A New Matching Algorithm between Trees of Shapes and its Application...") |
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| title = A New Matching Algorithm between Trees of Shapes and its Application to Brain Tumor Segmentation |
| title = A New Matching Algorithm between Trees of Shapes and its Application to Brain Tumor Segmentation |
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| booktitle = Proceedings of the IAPR International Conference on Discrete Geometry and Mathematical Morphology (DGMM) |
| booktitle = Proceedings of the IAPR International Conference on Discrete Geometry and Mathematical Morphology (DGMM) |
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+ | | pages = 67 to 78 |
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| address = Uppsala, Sweden |
| address = Uppsala, Sweden |
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+ | | publisher = Springer |
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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. 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. |
| 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. |
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+ | | lrdepaper = http://www.lrde.epita.fr/dload/papers/boutry.21.dgmm.2.pdf |
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| lrdeprojects = Olena |
| lrdeprojects = Olena |
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| lrdenewsdate = 2021-03-02 |
| lrdenewsdate = 2021-03-02 |
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− | | note = To appear |
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| type = inproceedings |
| type = inproceedings |
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| id = boutry.21.dgmm.2 |
| id = boutry.21.dgmm.2 |
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+ | | identifier = doi:10.1007/978-3-030-76657-3_4 |
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| bibtex = |
| bibtex = |
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@InProceedings<nowiki>{</nowiki> boutry.21.dgmm.2, |
@InProceedings<nowiki>{</nowiki> boutry.21.dgmm.2, |
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year = 2021, |
year = 2021, |
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month = <nowiki>{</nowiki>May<nowiki>}</nowiki>, |
month = <nowiki>{</nowiki>May<nowiki>}</nowiki>, |
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+ | 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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+ | publisher = <nowiki>{</nowiki>Springer<nowiki>}</nowiki>, |
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abstract = <nowiki>{</nowiki>Many approaches exist to compute the distance between two |
abstract = <nowiki>{</nowiki>Many approaches exist to compute the distance between two |
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trees in pattern recognition. These trees can be structures |
trees in pattern recognition. These trees can be structures |
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provide only a segmentation but also the tree of shapes |
provide only a segmentation but also the tree of shapes |
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corresponding to the segmentation and its depth map.<nowiki>}</nowiki>, |
corresponding to the segmentation and its depth map.<nowiki>}</nowiki>, |
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− | + | doi = <nowiki>{</nowiki>10.1007/978-3-030-76657-3_4<nowiki>}</nowiki> |
|
<nowiki>}</nowiki> |
<nowiki>}</nowiki> |
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Revision as of 20:00, 21 May 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. 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.
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}, 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} }