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4 June 2004 Visualizing weather with synthetic high-dynamic range images
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The appearance of the sky has a fundamental effect on the way human beings perceive an environment. This paper presents a method to compute synthetic high-dynamic-range fisheye images from weather parameter data sets. These images can then be used in global-illumination systems (e. g. Radiance) to define the lighting conditions at an arbitrary weather state. Applications of this technology can be found in flight simulators and in architectural visualization. The method combines artificial neural networks and principal component analysis to associate the appearance of the sky with the state of a weather parameter vector. A model is trained with examples of sky images and weather data from a period of seven months. This model is then used to generate artificial sky images corresponding to a specific weather parameter vector. This is a novel method which contrary to many previous methods is able to synthesize a sky image which varies with the current weather state. The results show that, although it is not possible to represent the cloud details, it is possible to distinguish between different weather states.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bjorn Olsson, Anders Ynnerman, and Reiner Lenz "Visualizing weather with synthetic high-dynamic range images", Proc. SPIE 5295, Visualization and Data Analysis 2004, (4 June 2004);


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