Difference between revisions of "Publications/chen.21.dgmm"
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| authors = Yizi Chen, Edwin Carlinet, Joseph Chazalon, Clément Mallet, Bertrand Duménieu, Julien Perret |
| authors = Yizi Chen, Edwin Carlinet, Joseph Chazalon, Clément Mallet, Bertrand Duménieu, Julien Perret |
||
| title = Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation |
| title = Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation |
||
− | | booktitle = IAPR International Conference on Discrete Geometry and Mathematical Morphology (DGMM) |
+ | | booktitle = Proceedings of the IAPR International Conference on Discrete Geometry and Mathematical Morphology (DGMM) |
| series = Lecture Notes in Computer Science |
| series = Lecture Notes in Computer Science |
||
+ | | volume = 12708 |
||
| address = Uppsala, Sweden |
| address = Uppsala, Sweden |
||
| publisher = Springer |
| publisher = Springer |
||
+ | | pages = 79 to 92 |
||
− | | abstract = The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization |
+ | | abstract = The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization step, i.e., the extraction of vector shapes of the objects of interest from raster images of maps. We are particularly interested in closed shape detection such as buildings, building blocksgardens, rivers, etc. in order to monitor their temporal evolution. Historical map images present significant pattern recognition challenges. The extraction of closed shapes by using traditional Mathematical Morphology (MM) is highly challenging due to the overlapping of multiple map features and texts. Moreover, state-of-the-art Convolutional Neural Networks (CNN) are perfectly designed for content image filtering but provide no guarantee about closed shape detection. Also, the lack of textural and color information of historical maps makes it hard for CNN to detect shapes that are represented by only their boundaries. Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task. The evaluation of our approach on a public dataset shows its effectiveness for extracting the closed boundaries of objects in historical maps. |
| lrdepaper = http://www.lrde.epita.fr/dload/papers/chen.2021.dgmm.pdf |
| lrdepaper = http://www.lrde.epita.fr/dload/papers/chen.2021.dgmm.pdf |
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| lrdekeywords = Image |
| lrdekeywords = Image |
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| type = inproceedings |
| type = inproceedings |
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| id = chen.21.dgmm |
| id = chen.21.dgmm |
||
+ | | identifier = doi:10.1007/978-3-030-76657-3_5 |
||
| bibtex = |
| bibtex = |
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@InProceedings<nowiki>{</nowiki> chen.21.dgmm, |
@InProceedings<nowiki>{</nowiki> chen.21.dgmm, |
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title = <nowiki>{</nowiki>Combining Deep Learning and Mathematical Morphology for |
title = <nowiki>{</nowiki>Combining Deep Learning and Mathematical Morphology for |
||
Historical Map Segmentation<nowiki>}</nowiki>, |
Historical Map Segmentation<nowiki>}</nowiki>, |
||
− | booktitle = <nowiki>{</nowiki>IAPR International Conference on |
+ | booktitle = <nowiki>{</nowiki>Proceedings of the IAPR International Conference on |
− | Mathematical Morphology (DGMM)<nowiki>}</nowiki>, |
+ | Discrete Geometry and Mathematical Morphology (DGMM)<nowiki>}</nowiki>, |
year = <nowiki>{</nowiki>2021<nowiki>}</nowiki>, |
year = <nowiki>{</nowiki>2021<nowiki>}</nowiki>, |
||
series = <nowiki>{</nowiki>Lecture Notes in Computer Science<nowiki>}</nowiki>, |
series = <nowiki>{</nowiki>Lecture Notes in Computer Science<nowiki>}</nowiki>, |
||
+ | volume = <nowiki>{</nowiki>12708<nowiki>}</nowiki>, |
||
month = may, |
month = may, |
||
address = <nowiki>{</nowiki>Uppsala, Sweden<nowiki>}</nowiki>, |
address = <nowiki>{</nowiki>Uppsala, Sweden<nowiki>}</nowiki>, |
||
publisher = <nowiki>{</nowiki>Springer<nowiki>}</nowiki>, |
publisher = <nowiki>{</nowiki>Springer<nowiki>}</nowiki>, |
||
+ | pages = <nowiki>{</nowiki>79--92<nowiki>}</nowiki>, |
||
abstract = <nowiki>{</nowiki>The digitization of historical maps enables the study of |
abstract = <nowiki>{</nowiki>The digitization of historical maps enables the study of |
||
ancient, fragile, unique, and hardly accessible information |
ancient, fragile, unique, and hardly accessible information |
||
Line 54: | Line 59: | ||
extracting the closed boundaries of objects in historical |
extracting the closed boundaries of objects in historical |
||
maps.<nowiki>}</nowiki>, |
maps.<nowiki>}</nowiki>, |
||
− | note = <nowiki>{</nowiki>Accepted<nowiki>}</nowiki> |
+ | note = <nowiki>{</nowiki>Accepted<nowiki>}</nowiki>, |
+ | doi = <nowiki>{</nowiki>10.1007/978-3-030-76657-3_5<nowiki>}</nowiki> |
||
<nowiki>}</nowiki> |
<nowiki>}</nowiki> |
||
Latest revision as of 10:55, 8 September 2021
- Authors
- Yizi Chen, Edwin Carlinet, Joseph Chazalon, Clément Mallet, Bertrand Duménieu, Julien Perret
- Where
- Proceedings of the IAPR International Conference on Discrete Geometry and Mathematical Morphology (DGMM)
- Place
- Uppsala, Sweden
- Type
- inproceedings
- Publisher
- Springer
- Keywords
- Image
- Date
- 2021-02-16
Abstract
The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization step, i.e., the extraction of vector shapes of the objects of interest from raster images of maps. We are particularly interested in closed shape detection such as buildings, building blocksgardens, rivers, etc. in order to monitor their temporal evolution. Historical map images present significant pattern recognition challenges. The extraction of closed shapes by using traditional Mathematical Morphology (MM) is highly challenging due to the overlapping of multiple map features and texts. Moreover, state-of-the-art Convolutional Neural Networks (CNN) are perfectly designed for content image filtering but provide no guarantee about closed shape detection. Also, the lack of textural and color information of historical maps makes it hard for CNN to detect shapes that are represented by only their boundaries. Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task. The evaluation of our approach on a public dataset shows its effectiveness for extracting the closed boundaries of objects in historical maps.
Documents
Bibtex (lrde.bib)
@InProceedings{ chen.21.dgmm, author = {Yizi Chen and Edwin Carlinet and Joseph Chazalon and Cl\'ement Mallet and Bertrand Dum\'enieu and Julien Perret}, title = {Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation}, booktitle = {Proceedings of the IAPR International Conference on Discrete Geometry and Mathematical Morphology (DGMM)}, year = {2021}, series = {Lecture Notes in Computer Science}, volume = {12708}, month = may, address = {Uppsala, Sweden}, publisher = {Springer}, pages = {79--92}, abstract = {The digitization of historical maps enables the study of ancient, fragile, unique, and hardly accessible information sources. Main map features can be retrieved and tracked through the time for subsequent thematic analysis. The goal of this work is the vectorization step, i.e., the extraction of vector shapes of the objects of interest from raster images of maps. We are particularly interested in closed shape detection such as buildings, building blocks, gardens, rivers, etc. in order to monitor their temporal evolution. Historical map images present significant pattern recognition challenges. The extraction of closed shapes by using traditional Mathematical Morphology (MM) is highly challenging due to the overlapping of multiple map features and texts. Moreover, state-of-the-art Convolutional Neural Networks (CNN) are perfectly designed for content image filtering but provide no guarantee about closed shape detection. Also, the lack of textural and color information of historical maps makes it hard for CNN to detect shapes that are represented by only their boundaries. Our contribution is a pipeline that combines the strengths of CNN (efficient edge detection and filtering) and MM (guaranteed extraction of closed shapes) in order to achieve such a task. The evaluation of our approach on a public dataset shows its effectiveness for extracting the closed boundaries of objects in historical maps.}, note = {Accepted}, doi = {10.1007/978-3-030-76657-3_5} }