@inproceedings{Jodoin:2002:HBE,
optnote = {},
optorganization = {},
author = {Pierre-Marc Jodoin and Emric Epstein and Martin Granger-Pich{\'e}
and Victor Ostromoukhov},
optkey = {},
optannote = {},
optseries = {},
editor = {Adam Finkelstein},
address = {New York},
localfile = {papers/Jodoin.2002.HBE.pdf },
publisher = {ACM Press},
doi = {http://doi.acm.org/10.1145/508530.508536},
optmonth = {},
citeseer = {http://citeseer.nj.nec.com/jodoin02hatching.html},
optcrossref = {},
booktitle = NPAR2002,
optstatus = {OK},
optvolume = {},
optnumber = {},
title = {{H}atching by {E}xample: a {S}tatistical {A}pproach},
abstract = {We present a new approach to synthetic (computer-aided) drawing
with patches of strokes. Grouped strokes convey the local
intensity level that is desired in drawing. The key point of our
approach is learning by example: the system does not know a priori
the distribution of the strokes. Instead, by analyzing a sample
(training) patch of strokes, our system is able to synthesize
freely an arbitrary sequence of strokes that "looks like" the
given sample. Strokes are considered as parametrical curves
represented by a vector of random variables following a Markovian
distribution. Our method is based on Shannon's N-gram approach and
is a direct extension of Efros's texture synthesis models [EL99;
EF01]. Nevertheless, one major difference between our method and
traditional texture synthesis is the use of such curves as a basic
element instead of pixels. We define a statistical metric for
comparison between different patches containing various layouts of
strokes. We hope that our method performs a first step towards
capturing a very difficult notion of style in drawing --- hatching
style in our case. We illustrate our method by varied examples,
ranging from typical hatching in traditional drawing to highly
heterogeneous sets of strokes. },
year = {2002},
pages = {29--36},
}
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