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Vega has been widely used in the Virtual Reality field. Its infrared (IR) module can implement IR simulation, but Vega IR imaging simulation's general approach does not apply to the complex scene. This article deeps into the scene's IR simulation method based on Vega. We design and realize a real time scene IR image simulation system in this article. We quantitatively define the scene as a simple and complex scene according to the scene range and whether it includes Digital Elevation Model (DEM) / Digital Surface Model (DSM) data. For the simple scene, we directly process IR image simulation according to the Vega general IR simulation process. While for the complex scene, we propose an IR image simulation method based on image classification and automatic texture material mapping technique. At the aspect of image classification, we develop a coarse to fine K-means clustering method based on the consistency of image color for color image classification and an additional Support Vector Machine (SVM) classification method based on texture features for gray level image classification. The method was tested on different scene's IR simulation. Experimental results show that the proposed approach can achieve better applicability and greater efficiency than the popular Vega IR simulation method.
Vega has been widely used in the Virtual Reality field. Its infrared (IR) module can implement IR simulation, but Vega IR imaging simulation's general approach does not apply to the complex scene. This article deeps into the scene's IR simulation method based on Vega. We design and realize a real time scene IR image simulation system in this article. We quantitatively define the scene as a simple and complex scene according to the scene range and whether it includes Digital Elevation Model (DEM) / Digital Surface Model (DSM) data. For the simple scene, we directly process IR image simulation according to the Vega general IR simulation process. While for the complex scene, we propose an IR image simulation method based on image classification and automatic texture material mapping technique. At the aspect of image classification, we develop a coarse to fine K-means clustering method based on the consistency of image color for color image classification and an additional Support Vector Machine (SVM) classification method based on texture features for gray level image classification. The method was tested on different scene's IR simulation. Experimental results show that the proposed approach can achieve better applicability and greater efficiency than the popular Vega IR simulation method.
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