@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 user’s 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 method’s power comes from its ability to
learn the relationships between numerous geometric attributes that
define a line style. Once learned, a user’s 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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