@phdthesis{Lu:2005:VI,
www = {http://wwwlib.umi.com/dissertations/fullcit/3185689},
optaddress = {},
author = {Aidong Lu},
optkey = {},
optannote = {},
opttype = {},
url = {http://docs.lib.purdue.edu/dissertations/AAI3185689/},
abstract = {With the fast growing size and dimensionality of scientific
datasets, exploring and rendering data features has become an
important topic in visualization. Scientific illustrations have
been widely used as visual representations in science and
engineering because of their capability to display a large amount
of information in a relatively succinct manner. This dissertation
investigates new efficient rendering algorithms to improve the
visual representation qualities of scientific datasets by
integrating the effectiveness of scientific; illustrations with
visualization techniques. The main contribution is a volume
illustration framework that can visualize volumetric datasets
efficiently through conveying object features and simulating
multiple illustrative styles. Specifically, a stipple rendering
algorithm explores a set of feature enhancements to improve the
general understanding of scientific datasets; multiple
illustrations styles are achieved through non-periodic 3D pattern
and texture generation methods based on Wang Cubes: and an
example-based approach creates 3D rendering from 2D illustration
examples to simulate professional scientific illustrations. This
volume illustration framework can be used to explore features from
a dataset interactively and express them efficiently. By taking
advantage of geometry-based and hardware-accelerated rendering
techniques, important features can be highlighted in an
illustrative way at an interactive rendering speed, with a small
storage overhead and short preprocessing delay.},
title = {{V}olume {I}llustration},
school = {Purdue University},
localfile = {papers/Lu.2005.VI.pdf},
optmonth = {},
year = {2005},
}
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