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Verfasst von:Hu, Xuke [VerfasserIn]   i
 Fan, Hongchao [VerfasserIn]   i
 Noskov, Alexey [VerfasserIn]   i
 Zipf, Alexander [VerfasserIn]   i
 Wang, Zhiyong [VerfasserIn]   i
 Shang, Jianga [VerfasserIn]   i
Titel:Feasibility of using grammars to infer room semantics
Verf.angabe:Xuke Hu, Hongchao Fan, Alexey Noskov, Alexander Zipf, Zhiyong Wang and Jianga Shang
E-Jahr:2019
Jahr:28 June 2019
Umfang:27 S.
Fussnoten:Gesehen am 23.01.2020
Titel Quelle:Enthalten in: Remote sensing
Ort Quelle:Basel : MDPI, 2009
Jahr Quelle:2019
Band/Heft Quelle:11(2019,13) Artikel-Nummer 1535, 27 Seiten
ISSN Quelle:2072-4292
Abstract:Current indoor mapping approaches can detect accurate geometric information but are incapable of detecting the room type or dismiss this issue. This work investigates the feasibility of inferring the room type by using grammars based on geometric maps. Specifically, we take the research buildings at universities as examples and create a constrained attribute grammar to represent the spatial distribution characteristics of different room types as well as the topological relations among them. Based on the grammar, we propose a bottom-up approach to construct a parse forest and to infer the room type. During this process, Bayesian inference method is used to calculate the initial probability of belonging an enclosed room to a certain type given its geometric properties (e.g., area, length, and width) that are extracted from the geometric map. The approach was tested on 15 maps with 408 rooms. In 84% of cases, room types were defined correctly. It, to a certain degree, proves that grammars can benefit semantic enrichment (in particular, room type tagging).
DOI:doi:10.3390/rs11131535
URL:Kostenfrei: Volltext ; Verlag: https://doi.org/10.3390/rs11131535
 Kostenfrei: Volltext: https://www.mdpi.com/2072-4292/11/13/1535
 DOI: https://doi.org/10.3390/rs11131535
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Bayesian inference
 grammar
 indoor mapping
 room type tagging
 semantic enrichment
K10plus-PPN:168817768X
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