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A skyline-based approach for mobile augmented reality / Mehdi Ayadi in The Visual Computer, vol 37 n° 4 (April 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 4 (April 2021) . - pp 789 - 804 Titre : A skyline-based approach for mobile augmented reality Type de document : Article/Communication Auteurs : Mehdi Ayadi, Auteur ; Mihaela Scuturici, Auteur ; Chokri Ben Amar, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 789 - 804 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] appariement géométrique [Termes IGN] base de données d'images [Termes IGN] CityGML [Termes IGN] détection de contours [Termes IGN] estimation de pose [Termes IGN] extraction de traits caractéristiques [Termes IGN] Lyon [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] paysage urbain [Termes IGN] réalité augmentée [Termes IGN] superposition d'images [Termes IGN] téléphone intelligent [Termes IGN] vision par ordinateur [Vedettes matières IGN] Géovisualisation Résumé : (auteur) This paper presents a skyline-based approach to enhance the visualization of a new construction project in augmented reality. We propose to process the video stream acquired with a mobile phone to register the real buildings with a 3D city model. We first combine the data acquired with the device’s instruments to estimate a rough user’s pose in the world coordinates system. Then, we use this estimated pose to generate a synthetic image of the user’s view from which we calculate a virtual skyline. In parallel, we extract a real skyline from the real-time video stream. Finally, we match these real and virtual skylines to correct the user’s pose (six degrees of freedom) and thus generate a more realistic augmented reality view. We evaluate the precision and the processing time of our approach using 2D and 3D registration algorithms, as well as with a novel double 2D strategy. Numéro de notice : A2021-342 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-020-01830-8 Date de publication en ligne : 06/03/2020 En ligne : https://doi.org/10.1007/s00371-020-01830-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97581 [article]

3-D deep learning approach for remote sensing image classification / Amina Ben Hamida in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 8 (August 2018) . - pp 4420 - 4434 Titre : 3-D deep learning approach for remote sensing image classification Type de document : Article/Communication Auteurs : Amina Ben Hamida, Auteur ; Alexandre Benoit, Auteur ; Patrick Lambert, Auteur ; Chokri Ben Amar, Auteur Année de publication : 2018 Article en page(s) : pp 4420 - 4434 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] image hyperspectrale [Termes IGN] qualité géométrique (image) [Termes IGN] valeur radiométrique Résumé : (Auteur) Recently, a variety of approaches have been enriching the field of remote sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited to the rich spatiospectral content of today's large data sets. It would seem intriguing to resort to deep learning (DL)-based approaches at this stage with regard to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS data sets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is first to explore the performance of DL architectures for the RS hyperspectral data set classification and second to introduce a new 3-D DL approach that enables a joint spectral and spatial information process. A set of 3-D schemes is proposed and evaluated. Experimental results based on well-known hyperspectral data sets demonstrate that the proposed method is able to achieve a better classification rate than state-of-the-art methods with lower computational costs. Numéro de notice : A2018-470 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2818945 Date de publication en ligne : 20/04/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2818945 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91252 [article]