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[Lu05]  Volume Illustration

Lu:2005:VI (PhD thesis)
Author(s)Lu A.
Title« Volume Illustration »
SchoolPurdue University
Year2005
URLhttp://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.

BibTeX code
@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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