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Hatching by Example: a Statistical Approach
Author(s): Pierre-Marc Jodoin, Emric Epstein, Martin Granger-Piche, Victor Ostromoukhov.
Proceedings: 2nd International Symposium on Non-Photorealistic Animation and Rendering (NPAR'02), Annecy, France, June 3-5,
2002.
[BibTeX] 
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.