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Combining Deep Learning and Mathematical Morphology for Historical Map Segmentation
- Source :
- Discrete Geometry and Mathematical Morphology, First International Joint Conference, DGMM 2021, Uppsala, Sweden, May 24–27, 2021, Proceedings, Discrete Geometry and Mathematical Morphology, First International Joint Conference, DGMM 2021, Uppsala, Sweden, May 24–27, 2021, Proceedings, 12708, pp 79-92, 2021, Lecture notes in computer science, ⟨10.1007/978-3-030-76657-3_5⟩, Lecture Notes in Computer Science ISBN: 9783030766566, DGMM
- Publication Year :
- 2021
- Publisher :
- HAL CCSD, 2021.
-
Abstract
- Supplementary material (code, extra figures) available at https://github.com/soduco/paper-dgmm2021/; International audience; 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.
- Subjects :
- [INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC]
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
Mathematical morphology
[INFO.INFO-CG]Computer Science [cs]/Computational Geometry [cs.CG]
Convolutional neural network
Edge detection
0202 electrical engineering, electronic engineering, information engineering
Image tracing
Segmentation
business.industry
Deep learning
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
020207 software engineering
Pattern recognition
computer.file_format
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR]
[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]
Pattern recognition (psychology)
020201 artificial intelligence & image processing
Artificial intelligence
Raster graphics
business
computer
Subjects
Details
- Language :
- English
- ISBN :
- 978-3-030-76656-6
- ISBNs :
- 9783030766566
- Database :
- OpenAIRE
- Journal :
- Discrete Geometry and Mathematical Morphology, First International Joint Conference, DGMM 2021, Uppsala, Sweden, May 24–27, 2021, Proceedings, Discrete Geometry and Mathematical Morphology, First International Joint Conference, DGMM 2021, Uppsala, Sweden, May 24–27, 2021, Proceedings, 12708, pp 79-92, 2021, Lecture notes in computer science, ⟨10.1007/978-3-030-76657-3_5⟩, Lecture Notes in Computer Science ISBN: 9783030766566, DGMM
- Accession number :
- edsair.doi.dedup.....a22cb27ec6eb1f9694692e40a6db1710