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Auteur Semir Elezovikj |
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Semantic-aware label placement for augmented reality in street view / Jianqing Jia in The Visual Computer, vol 37 n° 7 (July 2021)
[article]
Titre : Semantic-aware label placement for augmented reality in street view Type de document : Article/Communication Auteurs : Jianqing Jia, Auteur ; Semir Elezovikj, Auteur ; Heng Fan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1805 - 1819 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image Streetview
[Termes IGN] information sémantique
[Termes IGN] optimisation (mathématiques)
[Termes IGN] point d'intérêt
[Termes IGN] réalité augmentée
[Termes IGN] saillance
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantiqueRésumé : (auteur) In an augmented reality (AR) application, placing labels in a manner that is clear and readable without occluding the critical information from the real world can be a challenging problem. This paper introduces a label placement technique for AR used in street view scenarios. We propose a semantic-aware task-specific label placement method by identifying potentially important image regions through a novel feature map, which we refer to as guidance map. Given an input image, its saliency information, semantic information and the task-specific importance prior are integrated in the guidance map for our labeling task. To learn the task prior, we created a label placement dataset with the users’ labeling preferences, as well as use it for evaluation. Our solution encodes the constraints for placing labels in an optimization problem to obtain the final label layout, and the labels will be placed in appropriate positions to reduce the chances of overlaying important real-world objects in street view AR scenarios. The experimental validation shows clearly the benefits of our method over previous solutions in the AR street view navigation and similar applications. Numéro de notice : A2021-542 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01939-w Date de publication en ligne : 02/08/2020 En ligne : https://doi.org/10.1007/s00371-020-01939-w Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98022
in The Visual Computer > vol 37 n° 7 (July 2021) . - pp 1805 - 1819[article]