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Computer visualization can handle several kinds of data though, medical examination with CT scanners, for example, supply scientific visualization with large-sized 3D data. The aim is to support the examination with a visual representation of the measured data. Such visualization techniques obviously have to be accurate, but feature highlighting may also be needed. Numerical data from real-world measurement or simulation processes are further common sources of computer visualization.
Recently, another source for visualization data is getting common, forming a new field of computer visualization. Data from business processes, finance, transaction data, data from textual analysis, etc., are the source data for so-called information visualization. In opposition to scientific visualization, the data in information visualization usually is non-spatial.
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Visualization is a process of transforming information into a visual form enabling the user to observe the information. On the computer science side, it uses techniques of computer graphics and imaging. On the human side, perceptual and cognitive capabilities of the viewer determine the conditions the process needs to take into account.
Visualization is more than pretty pictures. Successful visualization can reduce the time it takes to understand the underlying data, to find relationships, and to get the information searched. To establish visualization, a mapping of the data into a Cartesian space of two or three dimensions is needed, which represents relationships contained in the data as intuitive as possible. This should enable the viewer to use his innate abilities to understand spatial relationships. Finding a good spatial representation of the data is one of the most difficult tasks in visualization of abstract information.
Plastic function models.
Work after computer
modeling 3D
Plastic function models.
Work after computer
modeling 3D
Computer simulation of
astronomy telescope
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