Détail de l'autorité
DGMM 2021, 1st International Joint Conference on Discrete Geometry and Mathematical Morphology 24/05/2021 27/05/2021 Uppsala Suède Proceedings Springer
nom du congrès :
DGMM 2021, 1st International Joint Conference on Discrete Geometry and Mathematical Morphology
début du congrès :
24/05/2021
fin du congrès :
27/05/2021
ville du congrès :
Uppsala
pays du congrès :
Suède
site des actes du congrès :
|
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Combining deep learning and mathematical morphology for historical map segmentation / Yizi Chen (2021)
Titre : Combining deep learning and mathematical morphology for historical map segmentation Type de document : Chapitre/Contribution Auteurs : Yizi Chen , Auteur ; Edwin Carlinet, Auteur ; Joseph Chazalon, Auteur ; Clément Mallet , Auteur ; Bertrand Duménieu , Auteur ; Julien Perret , Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2021 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 12708 Projets : SODUCO / Perret, Julien Conférence : DGMM 2021, 1st International Joint Conference on Discrete Geometry and Mathematical Morphology 24/05/2021 27/05/2021 Uppsala Suède Proceedings Springer Importance : pp 79 - 92 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage profond
[Termes IGN] carte ancienne
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] données maillées
[Termes IGN] morphologie mathématique
[Termes IGN] vectorisationRésumé : (auteur) 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. Numéro de notice : H2021-001 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE Nature : Chapître / contribution nature-HAL : ChOuvrScient DOI : 10.1007/978-3-030-76657-3_5 Date de publication en ligne : 16/05/2021 En ligne : https://hal.science/hal-03101578v1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96739