Stippling by Example
In this work, we focus on stippling as an artistic style and discuss our technique for capturing and reproducing stipple features unique to an individual artist. We employ a texture synthesis algorithm based on the gray-level co-occurrence matrix (GLCM) of a texture field. This algorithm uses a texture similarity metric to generate stipple textures that are perceptually similar to input samples, allowing us to better capture and reproduce stipple distributions. First, we extract example stipple textures representing various tones in order to create an approximate tone map used by the artist. Second, we extract the stipple marks and distributions from the extracted example textures, generating both a lookup table of stipple marks and a texture representing the stipple distribution. Third, we use the distribution of stipples to synthesize similar distributions with slight variations using a numerical measure of the error between the synthesized texture and the example texture as the basis for replication. Finally, we apply the synthesized stipple distribution to a 2D grayscale image and place stipple marks onto the distribution, thereby creating a stippled image that is statistically similar to images created by the example artist.
You can download a demo of the stippling by example tool (for Win32, 18.9MB) and try it out for yourself.
|Ross Maciejewski, Tobias Isenberg, William M. Andrews, David S. Ebert, and Mario Costa Sousa (2007) Aesthetics of Hand-Drawn vs. Computer-Generated Stippling. In Douglas W. Cunningham, Gary Meyer, László Neumann, Alan Dunning, and Raquel Paricio, eds., Proceedings of Computational Aesthetics in Graphics, Visualization, and Imaging (CAe, June 20–22, Banff, Alberta, Canada). Eurographics Association, Goslar, Germany, pages 53–56, 2007. Also see the article in IEEE Computers Graphics and Applications.
This work was done at and in collaboration with the Purdue University Rendering and Perceptualization Lab (PURPL) at Purdue University, USA.