Reference : Interpreting Thermal 3D Models of Indoor Environments for Energy Efficiency
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/18053
Interpreting Thermal 3D Models of Indoor Environments for Energy Efficiency
English
Demisse, Girum mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Borrman, Dorit [University of Wurzburg > Robotics and Telematics]
Nuchter, Andreas [University of Wurzburg > Robotics and Telematics]
26-Aug-2014
In Journal of Intelligent and Robotic Systems, Springer
Springer
Yes (verified by ORBilu)
International
0921-0296
[en] 3D Thermal modelling ; Eneregy efficiency ; Window labeling
[en] In recent years 3D models of buildings are used in maintenance and inspection, preservation, and other building related applications. However, the usage of these models is limited because most models are pure representations with no or little associated semantics. In this paper we present a pipeline of techniques used for interior interpretation, object detection, and adding energy related semantics to windows of a 3D thermal model. A sequence of algorithms is presented for building the fundamental semantics of a 3D model. Among other things, these algorithms enable the system to differentiate between objects in a room and objects that are part of the room, e.g. floor, windows. Subsequently, the thermal information is used to construct a stochastic mathematical model - namely Markov Random Field - of the temperature distribution of the detected windows. As a result, the MAP (Maximum a posteriori) framework is used to further label the windows as either open, closed or damaged based upon their temperature distribution. The experimental results showed the robustness of the techniques. Furthermore, a strategy to optimize the free parameters is described, in cases where there is ample training dataset.
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/18053

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