Résultat de la recherche
1 recherche sur le mot-clé libre 'Feature selection'
Ajouter le résultat dans votre panier Affiner la recherche Générer le flux rss de la recherche
Partager le résultat de cette recherche Interroger des sources externes
Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers / Martin Weinmann in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)
[article]
Titre : Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers Type de document : Article/Communication Auteurs : Martin Weinmann, Auteur ; Stefan Hinz, Auteur ; Clément Mallet , Auteur Année de publication : 2015 Article en page(s) : pp 286 - 304 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification barycentrique
[Termes IGN] compréhension de l'image
[Termes IGN] données localisées 3D
[Termes IGN] environnement de développement
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] opérateur sémantique
[Termes IGN] scène
[Termes IGN] semis de points
[Termes IGN] voisinage (relation topologique)Mots-clés libres : Point cloud Neighborhood selection Feature extraction Feature selection Classification 3D scene analysis Résumé : (auteur) 3D scene analysis in terms of automatically assigning 3D points a respective semantic label has become a topic of great importance in photogrammetry, remote sensing, computer vision and robotics. In this paper, we address the issue of how to increase the distinctiveness of geometric features and select the most relevant ones among these for 3D scene analysis. We present a new, fully automated and versatile framework composed of four components: (i) neighborhood selection, (ii) feature extraction, (iii) feature selection and (iv) classification. For each component, we consider a variety of approaches which allow applicability in terms of simplicity, efficiency and reproducibility, so that end-users can easily apply the different components and do not require expert knowledge in the respective domains. In a detailed evaluation involving 7 neighborhood definitions, 21 geometric features, 7 approaches for feature selection, 10 classifiers and 2 benchmark datasets, we demonstrate that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis. Additionally, we show that the selection of adequate feature subsets may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption. Numéro de notice : A2015-704 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.01.016 Date de publication en ligne : 27/02/2015 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.01.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78340
in ISPRS Journal of photogrammetry and remote sensing > vol 105 (July 2015) . - pp 286 - 304[article]