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ICDAR 2021, 16th International Conference on Document Analysis and Recognition 05/09/2021 10/09/2021 Lausanne Suisse
nom du congrès :
ICDAR 2021, 16th International Conference on Document Analysis and Recognition
début du congrès :
05/09/2021
fin du congrès :
10/09/2021
ville du congrès :
Lausanne
pays du congrès :
Suisse
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Documents disponibles (2)
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Titre : ICDAR 2021 competition on historical map segmentation Type de document : Article/Communication Auteurs : Joseph Chazalon, Auteur ; Edwin Carlinet, Auteur ; Yizi Chen , Auteur ; Julien Perret
Editeur : Le Kremlin Bicêtre : Ecole pour l'Informatique et les Techniques Avancées EPITA Année de publication : 2021 Projets : 1-Pas de projet / Conférence : ICDAR 2021, 16th International Conference on Document Analysis and Recognition 05/09/2021 10/09/2021 Lausanne Suisse Importance : 15 p. Note générale : bibliographie Langues : Anglais (eng) Résumé : (auteur) This paper presents the final results of the ICDAR 2021 Competition on Historical Map Segmentation (MapSeg), encouraging research on a series of historical atlases of Paris, France, drawn at 1/5000 scale between 1894 and 1937. The competition featured three tasks, awarded separately. Task 1 consists in detecting building blocks and was won by the L3IRIS team using a DenseNet-121 network trained in a weakly supervised fashion. This task is evaluated on 3 large images containing hundreds of shapes to detect. Task 2 consists in segmenting map content from the larger map sheet, and was won by the UWB team using a U-Net-like FCN combined with a binarization method to increase detection edge accuracy. Task 3 consists in locating intersection points of geo-referencing lines, and was also won by the UWB team who used a dedicated pipeline combining binarization, line detection with Hough transform, candidate filtering, and template matching for intersection refinement. Tasks 2 and 3 are evaluated on 95 map sheets with complex content. Dataset, evaluation tools and results are available under permissive licensing at https://icdar21-mapseg.github.io/. Numéro de notice : C2021-022 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : https://hal.science/hal-03256193 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98032 Vectorization of historical maps using deep edge filtering and closed shape extraction / Yizi Chen (2021)
Titre : Vectorization of historical maps using deep edge filtering and closed shape extraction Type de document : Article/Communication Auteurs : Yizi Chen Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Année de publication : 2021 Projets : SODUCO / Perret, Julien Conférence : ICDAR 2021, 16th International Conference on Document Analysis and Recognition 05/09/2021 10/09/2021 Lausanne Suisse Importance : 17 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] apprentissage profond
[Termes IGN] carte ancienne
[Termes IGN] chaîne de traitement
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtrage numérique d'image
[Termes IGN] traitement d'image
[Termes IGN] vectorisationRésumé : (auteur) Maps have been a unique source of knowledge for centuries. Such historical documents provide invaluable information for analyzing the complex spatial transformation of landscapes over important time frames. This is particularly true for urban areas that encompass multiple interleaved research domains (social sciences, economy, etc.). The large amount and significant diversity of map sources call for automatic image processing techniques in order to extract the relevant objects under a vectorial shape. The complexity of maps (text, noise, digiti-zation artifacts, etc.) has hindered the capacity of proposing a versatile and efficient raster-to-vector approaches for decades. We propose alearnable, reproducible, and reusable solution for the automatic transformation of raster maps into vector objects (building blocks, streets,rivers). It is built upon the complementary strength of mathematical morphology and convolutional neural networks through efficient edge filtering. Even more, we modify ConnNet and combine with deep edgefiltering architecture to make use of pixel connectivity information and built an end-to-end system without requiring any post-processing techniques. In this paper, we focus on the comprehensive benchmark on various architectures on multiple datasets coupled with a novel vectorization step. Our experimental results on a new public dataset using COCO Panoptic metric exhibit very encouraging results confirmedby a qualitative analysis of the success and failure cases of our approach. Code, dataset, results and extra illustrations are freely available at https://github.com/soduco/ICDAR-2021-Vectorization Numéro de notice : C2021-011 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : sans En ligne : https://hal.science/hal-03256073/document Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97988
