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Lecture notes in Computer Science
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Affiner la recherche Interroger des sources externesCombining 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 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 : http://dx.doi.org/10.1007/978-3-030-76657-3_5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96739 Advances in Intelligent Data Analysis XVIII : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29 2020 / Michael R. Berthold (2020)
Titre : Advances in Intelligent Data Analysis XVIII : 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29 2020 Type de document : Actes de congrès Auteurs : Michael R. Berthold, Éditeur scientifique ; Ad Feelders, Éditeur scientifique ; Georg Krempl, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 12080 Conférence : IDA 2020, 18th International Symposium on Intelligent Data Analysis, Advances in Intelligent Data Analysis XVIII 27/04/2020 29/04/2020 Constance Allemagne OA Proceedings Importance : 588 p. ISBN/ISSN/EAN : 978-3-030-44584-3 Note générale : Information Systems and Applications, incl. Internet/Web, and HCI Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse d'image numérique
[Termes IGN] analyse de données
[Termes IGN] appariement d'images
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] réseau neuronal artificiel
[Termes IGN] vision par ordinateurRésumé : (Editeur) This open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation. Numéro de notice : 26312 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Actes DOI : 10.1007/978-3-030-44584-3 Date de publication en ligne : 14/05/2020 En ligne : https://www.springer.com/gp/book/9783030445836 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95127 Towards interoperable research infrastructures for environmental and earth sciences / Zhiming Zhao (2020)
Titre : Towards interoperable research infrastructures for environmental and earth sciences : a reference model guided approach for common challenges Type de document : Monographie Auteurs : Zhiming Zhao, Éditeur scientifique ; Margareta Hellström, Éditeur scientifique Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2020 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 12003 Importance : 373 p. ISBN/ISSN/EAN : 978-3-030-52829-4 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Société de l'information
[Termes IGN] données environnementales
[Termes IGN] géosciences
[Termes IGN] infrastructure
[Termes IGN] instrument de mesure
[Termes IGN] interopérabilité
[Termes IGN] monde virtuel
[Termes IGN] recherche scientifiqueRésumé : (Editeur) This open access book summarises the latest developments on data management in the EU H2020 ENVRIplus project, which brought together more than 20 environmental and Earth science research infrastructures into a single community. It provides readers with a systematic overview of the common challenges faced by research infrastructures and how a ‘reference model guided’ engineering approach can be used to achieve greater interoperability among such infrastructures in the environmental and earth sciences. The 20 contributions in this book are structured in 5 parts on the design, development, deployment, operation and use of research infrastructures. Part one provides an overview of the state of the art of research infrastructure and relevant e-Infrastructure technologies, part two discusses the reference model guided engineering approach, the third part presents the software and tools developed for common data management challenges, the fourth part demonstrates the software via several use cases, and the last part discusses the sustainability and future directions. Note de contenu : 1--Data Management in Environmental and Earth Sciences
- Supporting Cross-Domain System-Level Environmental and Earth Science / Alex Vermeulen, Helen Glaves, Sylvie Pouliquen, and Alexandra Kokkinaki
- ICT Infrastructures for Environmental and Earth Sciences / Keith Jeffery, Antti Pursula, and Zhiming Zhao
- Common Challenges and Requirements / Barbara Magagna, Paul Martin, Abraham Nieva de la Hidalga, Malcolm Atkinson, and Zhiming Zhao
2--Reference Model Guided System Design and Development
- The ENVRI Reference Model / Abraham Nieva de la Hidalga, Alex Hardisty, Paul Martin, Barbara Magagna, and Zhiming Zhao
- Reference Model Guided Engineering / Zhiming Zhao and Keith Jeffery
- Semantic and Knowledge Engineering Using ENVRI RM / Paul Martin, Xiaofeng Liao, Barbara Magagna, Markus Stocker, and Zhiming Zhao
3--Common Data Management Services in Environmental RIs
- Data Curation and Preservation / Keith Jeffery
- Data Cataloguing / Erwann Quimbert, Keith Jeffery, Claudia Martens, Paul Martin, and Zhiming Zhao
- Identification and Citation of Digital Research Resources / Margareta Hellström, Maria Johnsson, and Alex Vermeulen
- Data Processing and Analytics for Data-Centric Sciences / Leonardo Candela, Gianpaolo Coro, Lucio Lelii, Giancarlo Panichi, and Pasquale Pagano
- Virtual Infrastructure Optimisation / Spiros Koulouzis, Paul Martin, and Zhiming Zhao
- Data Provenance / Barbara Magagna, Doron Goldfarb, Paul Martin, Malcolm Atkinson, Spiros Koulouzis, and Zhiming Zhao
- Semantic Linking of Research Infrastructure Metadata / Paul Martin, Barbara Magagna, Xiaofeng Liao, and Zhiming Zhao
- Authentication, Authorization, and Accounting / Alessandro Paolini, Diego Scardaci, Nicolas Liampotis, Vincenzo Spinoso, Baptiste Grenier, and Yin Chen
- Virtual Research Environments for Environmental and Earth Sciences : Approaches and Experiences / Keith Jeffery, Leonardo Candela, and Helen Glaves
4--Case Studies
- Case Study: Data Subscriptions Using Elastic Cloud Services / Spiros Koulouzis, Thierry Carval, Jani Heikkinen, Antti Pursula and Zhiming Zhao
- Case Study: ENVRI Science Demonstrators with D4Science / Leonardo Candela, Markus Stocker, Ingemar Häggström, Carl-Fredrik Enell, Domenico Vitale, Dario Papale, Baptiste Grenier, Yin Chen, and Matthias Obst
- Case Study: LifeWatch Italy Phytoplankton VRE / Elena Stanca, Nicola Fiore, Ilaria Rosati, Lucia Vaira, Francesco Cozzoli, and Alberto Basset
5--Sustainability and Future Challenges
- Towards Cooperative Sustainability / Wouter Los
- Towards Operational Research Infrastructures with FAIR : Data and Services / Zhiming Zhao, Keith Jeffery, Markus Stocker, Malcolm Atkinson, and Andreas PetzoldNuméro de notice : 26497 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Recueil / ouvrage collectif DOI : 10.1007/978-3-030-52829-4 En ligne : https://doi.org/10.1007/978-3-030-52829-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96989
Titre : Convolutional networks with adaptive inference graphs Type de document : Article/Communication Auteurs : Andreas Veit, Auteur ; Serge Belongie, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2018 Collection : Lecture notes in Computer Science, ISSN 0302-9743 num. 11205 Conférence : ECCV 2018, 15th European Conference 08/09/2018 14/09/2018 Munich Allemagne Proceedings Springer Importance : pp 3 - 18 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] graphe
[Termes IGN] inférence
[Termes IGN] réseau neuronal convolutifRésumé : (auteur) Do convolutional networks really need a fixed feed-forward structure? What if, after identifying the high-level concept of an image, a network could move directly to a layer that can distinguish fine-grained differences? Currently, a network would first need to execute sometimes hundreds of intermediate layers that specialize in unrelated aspects. Ideally, the more a network already knows about an image, the better it should be at deciding which layer to compute next. In this work, we propose convolutional networks with adaptive inference graphs (ConvNet-AIG) that adaptively define their network topology conditioned on the input image. Following a high-level structure similar to residual networks (ResNets), ConvNet-AIG decides for each input image on the fly which layers are needed. In experiments on ImageNet, we show that ConvNet-AIG learns distinct inference graphs for different categories. Both ConvNet-AIG with 50 and 101 layers outperform their ResNet counterpart, while using 20% and 33% less computations respectively. By grouping parameters into layers for related classes and only executing relevant layers, ConvNet-AIG improves both efficiency and overall classification quality. Lastly, we also study the effect of adaptive inference graphs on the susceptibility towards adversarial examples. We observe that ConvNet-AIG shows a higher robustness than ResNets, complementing other known defense mechanisms. Numéro de notice : C2018- Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Communication DOI : 10.1007/978-3-030-01246-5_1 En ligne : http://dx.doi.org/10.1007/978-3-030-01246-5_1 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100058
contenu dans The 23rd international conference on MultiMedia Modeling, MMM 2017 / Laurent Amsaleg (2017)
Titre : Adaptive and optimal combination of local features for image retrieval Type de document : Article/Communication Auteurs : Neelanjan Bhowmik Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2017 Autre Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Collection : Lecture notes in Computer Science, ISSN 0302-9743 Projets : POEME / Da Silva, Jean-Claude Conférence : MMM 2017, 23rd international conference on Multimedia Modeling 04/01/2017 06/01/2017 Reykjavik Islande Proceedings Springer Importance : pp 76 - 88 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] modèle de régression
[Termes IGN] point d'intérêt
[Termes IGN] recherche d'image basée sur le contenuRésumé : (Auteur) With the large number of local feature detectors and descriptors in the literature of Content-Based Image Retrieval (CBIR), in this work we propose a solution to predict the optimal combination of features, for improving image retrieval performances, based on the spatial complementarity of interest point detectors. We review several complementarity criteria of detectors and employ them in a regression based prediction model, designed to select the suitable detectors combination for a dataset. The proposal can improve retrieval performance even more by selecting optimal combination for each image (and not only globally for the dataset), as well as being profitable in the optimal fitting of some parameters. The proposal is appraised on three state-of-the-art datasets to validate its effectiveness and stability. The experimental results highlight the importance of spatial complementarity of the features to improve retrieval, and prove the advantage of using this model to optimally adapt detectors combination and some parameters. Numéro de notice : C2017-021 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1007/978-3-319-51814-5_7 Date de publication en ligne : 01/06/2017 En ligne : https://doi.org/10.1007/978-3-319-51814-5_7 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88988 Documents numériques
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Adaptive and optimal combination - preprintAdobe Acrobat PDFAssessing the positional planimetric accuracy of DBpedia georeferenced resources / Abdelfettah Feliachi (2017)
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