Generating Pointillism
Paintings Based on Seurat’s Color Composition
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Our result (click to
see the image in full size) |
Input photo (click to
see the original image) |
Overview
This paper presents a novel example-based stippling technique that
employs a simple and intuitive concept to convert a color image into a
pointillism painting. Our method relies on analyzing and imitating the color
distributions of Seurat’s paintings to obtain a statistical color model. Then,
this model can be easily combined with the modified multi-class blue noise
sampling to stylize an input image with characteristics of color composition in
Seurat’s paintings. The blue noise property of the output image also ensures
that the color points are randomly located but remain spatially uniform. In our
experiments, the multivariate goodness-of-fit tests were adopted to
quantitatively analyze the results of the proposed and previous methods,
further confirming that the color composition of our results are more similar
to Seurat’s painting style than that of previous approaches. Additionally, we
also conducted a user study participated by artists to qualitatively evaluate
the synthesized images of the proposed method.
Publications
·
Yi-Chian Wu, Yu-Ting Tsai,
Wen-Chieh Lin, and Wen-Hsin
Li, “Generating Pointillism Paintings Based on
Seurat's Color Composition,” Eurographics Symposium
on Rendering 2013.
Additional results