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Facade repetition detection in a fronto-parallel view with fiducial lines extraction / Hongfei Xiao in Neurocomputing, vol 273 (January 2018)  

Public [article]  inNeurocomputing > vol 273 (January 2018) Titre : Facade repetition detection in a fronto-parallel view with fiducial lines extraction Type de document : Article/Communication Auteurs : Hongfei Xiao, Auteur ; Gaofeng Meng, Auteur ; Lingfeng Wang, Auteur ; Chunhong Pan, Auteur Année de publication : 2018 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] façade [Termes IGN] programmation dynamique Résumé : (auteur) Detecting repetitive structures on building facades plays an important role in facade image analysis. Observing that repetitions are usually horizontally and vertically aligned, and thereby can be localized by the horizontal and vertical lines passing along the repetition boundaries, we propose to detect repetitions by extracting these fiducial lines. Firstly, candidate lines are detected, containing both the fiducial lines and some mistaken lines passing across facade wall or repetitive structures. Secondly, to pick out the fiducial lines, we formulate a maximum a posterior problem to measure the probabilities that the lines can localize the repetitions. Finally, a dynamic programming based algorithm is developed to solve the problem efficiently. To evaluate the proposed approach, we implement a series of experiments on a dataset containing 60 facade images as well as the public Ecole Central Paris facade dataset. Both qualitative and quantitative results demonstrate the effectiveness of our approach. Numéro de notice : A2017-559 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.neucom.2017.07.040 En ligne : https://doi.org/10.1016/j.neucom.2017.07.040 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86636 [article]