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Auteur Nikos Paragios |
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Learning grammars for architecture-specific facade parsing / Raghudeep Gadde in International journal of computer vision, vol 117 n° 3 (May 2016)
[article]
Titre : Learning grammars for architecture-specific facade parsing Type de document : Article/Communication Auteurs : Raghudeep Gadde, Auteur ; Renaud Marlet, Auteur ; Nikos Paragios, Auteur Année de publication : 2016 Article en page(s) : pp 290 – 316 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse de données
[Termes IGN] apprentissage automatique
[Termes IGN] architecture
[Termes IGN] façade
[Termes IGN] réalité de terrainRésumé : (auteur) Parsing facade images requires optimal handcrafted grammar for a given class of buildings. Such a handcrafted grammar is often designed manually by experts. In this paper, we present a novel framework to learn a compact grammar from a set of ground-truth images. To this end, parse trees of ground-truth annotated images are obtained running existing inference algorithms with a simple, very general grammar. From these parse trees, repeated subtrees are sought and merged together to share derivations and produce a grammar with fewer rules. Furthermore, unsupervised clustering is performed on these rules, so that, rules corresponding to the same complex pattern are grouped together leading to a rich compact grammar. Experimental validation and comparison with the state-of-the-art grammar-based methods on four different datasets show that the learned grammar helps in much faster convergence while producing equal or more accurate parsing results compared to handcrafted grammars as well as grammars learned by other methods. Besides, we release a new dataset of facade images following the Art-deco style and demonstrate the general applicability and extreme potential of the proposed framework. Numéro de notice : A2016--149 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007%2Fs11263-016-0887-4 En ligne : https://doi.org/10.1007/s11263-016-0887-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85917
in International journal of computer vision > vol 117 n° 3 (May 2016) . - pp 290 – 316[article]