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Unsupervised statistical Sketching for Non-Photorealistic Rendering Models
Author(s): Max Mignotte.
Proceedings: 10th IEEE International Conference on Image Processing (ICIP'03), Vol. 3, pp. 573--577, Barcelona, Spain, September,
2003.
[BibTeX] 
Abstract:
This paper investigates the use of the Bayesian inference for devising
an unsupervised sketch rendering procedure. As likelihood
model of this inference, we exploit the recent statistical model of
the gradient vector field distribution proposed by Destrempes et al.
for contour detection. A global prior deformation model for each
pencil stroke is also considered. In this Bayesian framework, the
placement of each stroke is viewed as the search of the Maximum
A Posteriori estimation of the posterior distribution of its deformations.
We use a stochastic optimization algorithm in order to find
these optimal deformations. This yields an unsupervised method
to create realistic hand-sketched pencil drawings. Combined with
an example-based local rendering model, used to transfer the textural
tone value of a given depiction style, the proposed scheme
allows to simulate automatic synthesis of various artistic illustration
styles.