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Hybrid terrain rendering based on the external edge primitive / E.G. Paredes in International journal of geographical information science IJGIS, vol 30 n° 5-6 (May - June 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 5-6 (May - June 2016) . - pp 1095 - 1116 Titre : Hybrid terrain rendering based on the external edge primitive Type de document : Article/Communication Auteurs : E.G. Paredes, Auteur ; M. Amor, Auteur ; M. Bóo, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 1095 - 1116 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] figuré du terrain [Termes IGN] modèle numérique de terrain [Termes IGN] primitive topologique [Termes IGN] primitive volumique [Termes IGN] Triangulated Irregular Network Résumé : (Auteur) Hybrid terrain models combine large regular data sets and high-resolution irregular meshes [triangulated irregular network (TIN)] for topographically and morphologically complex terrain features such as man-made microstructures or cliffs. In this paper, a new method to generate and visualize this kind of 3D hybrid terrain models is presented. This method can integrate geographic data sets from multiple sources without a remeshing process to combine the heterogeneous data of the different models. At the same time, the original data sets are preserved without modification, and, thus, TIN meshes can be easily edited and replaced, among other features. Specifically, our approach is based on the utilization of the external edges of convexified TINs as the fundamental primitive to tessellate the space between both types of meshes. Our proposal is eminently parallel, requires only a minimal preprocessing phase, and minimizes the storage requirements when compared with the previous proposals. Numéro de notice : A2016-295 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2015.1105375 En ligne : https://doi.org/10.1080/13658816.2015.1105375 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80879 [article]

Exemplaires(2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
079-2016032	RAB	Revue	Centre de documentation	En réserve L003	Disponible
079-2016031	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A region-line primitive association framework for object-based remote sensing image analysis / Wang Min in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 2 (February 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 2 (February 2016) . - pp 149 - 159 Titre : A region-line primitive association framework for object-based remote sensing image analysis Type de document : Article/Communication Auteurs : Wang Min, Auteur ; Wang Jie, Auteur Année de publication : 2016 Article en page(s) : pp 149 - 159 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] primitive géométrique [Termes IGN] primitive topologique [Termes IGN] réseau routier [Termes IGN] segmentation d'image [Termes IGN] zone d'intérêt Résumé : (auteur) In this study, we propose a novel region-line primitive association framework (RLPAF) for OBIA. In this framework, segments (region primitive) and straight lines (line primitive) are obtained by image segmentation and straight line detection, respectively, before their corresponding intra-primitive features are extracted. An association model is built on inter-primitive topology and direction relationships. Several region-line collaborative features are also derived. Image analysis is then performed based on both region and line primitives. The advantage of RLPAF is the collaborative utilization of complementary information between regions and lines throughout the entire OBIA process: from image segmentation, to feature extraction, and finally, object recognition. To validate this framework, RLPAF is applied on road network extraction from high spatial resolution (HSR) remote sensing images. Experiments show that the proposed framework and methods refine primitive shape and spatial relationship analyses, as well as obtain higher method accuracy, than OBIAs based on only regions. Numéro de notice : A2016-056 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.2.149 En ligne : http://dx.doi.org/10.14358/PERS.82.2.149 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79657 [article]