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[LM+05]  Expressive Line Selection by Example

Lum:2005:ELS (Article)
Author(s)Lum E. and Ma K.L.
Title« Expressive Line Selection by Example »
JournalThe Visual Computer
Volume21
Number8--10
Page(s)811--820
Year2005

Abstract
An important problem in computer generated line drawing is determining which set of lines produces a representation that is in agreement with a userís communication goals. We describe a method that enables a user to intuitively specify which types of lines should appear in rendered images. Our method employs conventional silhouette-edge and other feature-line extraction algorithms to derive a set of candidate lines, and integrates machine learning into a user-directed line removal process using a sketching metaphor. The method features a simple and intuitive user interface that provides interactive control over the resulting line selection criteria and can be easily adapted to work in conjunction with existing line detection and rendering algorithms. Much of the methodís power comes from its ability to learn the relationships between numerous geometric attributes that define a line style. Once learned, a userís style and intent can be passed from object to object as well as from view to view.

BibTeX code
@article{Lum:2005:ELS,
  optpostscript = {},
  number = {8--10},
  month = sep,
  author = {Eric B. Lum and Kwan-Liu Ma},
  optkey = {},
  optannote = {},
  localfile = {papers/Lum.2005.ELS.pdf},
  optkeywords = {},
  doi = {http://dx.doi.org/10.1007/s00371-005-0342-y},
  optciteseer = {},
  journal = j-TVC,
  opturl = {},
  volume = {21},
  optwww = {},
  title = {{E}xpressive {L}ine {S}election by {E}xample},
  abstract = {An important problem in computer generated line drawing is
              determining which set of lines produces a representation that is
              in agreement with a userís communication goals. We describe a
              method that enables a user to intuitively specify which types of
              lines should appear in rendered images. Our method employs
              conventional silhouette-edge and other feature-line extraction
              algorithms to derive a set of candidate lines, and integrates
              machine learning into a user-directed line removal process using a
              sketching metaphor. The method features a simple and intuitive
              user interface that provides interactive control over the
              resulting line selection criteria and can be easily adapted to
              work in conjunction with existing line detection and rendering
              algorithms. Much of the methodís power comes from its ability to
              learn the relationships between numerous geometric attributes that
              define a line style. Once learned, a userís style and intent can
              be passed from object to object as well as from view to view.},
  pages = {811--820},
  year = {2005},
}

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