Project Description:
With
the explosion of simulated and acquired data in many areas ranging from
scientific communities to industrial regions, visualization is employed to
help users to explore and gain insight into the data with effective graphical
representations. Recently, the need to effectively visualize
multi-dimensional data arises in various fields such as environmental
studies, climatology and geology. For multi-variate
data display, it is necessary to design the methods to depict these data in a
singe display to facilitate users to develop an integrated understanding of
the whole data distributions and find out the possible correlations between
different attributes. In this paper, we propose to address this problem in
terms of the technique of texture synthesis for which many useful algorithms
have been developed these years to make it possible to be applied in multi-variate data encoding.
Textures
are ubiquitous visual phenomena in our life. The observation of textures
usually only involves low-level visual system, which means we can
differentiate textures very rapidly and accurately without the need for
focused attention. In this paper we present a novel controllable multi-layer
texture synthesis method from which the synthesized results combine the
underlying changes of different data attributes to encode multi-variate data in a single output image.
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