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Stippling by example
| Content Provider | ACM Digital Library |
|---|---|
| Author | Maciejewski, Ross Kim, Sung Ye Isenberg, Tobias Sousa, Mario Costa Andrews, William M. Ebert, David S. Chen, Wei |
| Abstract | 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. |
| Starting Page | 41 |
| Ending Page | 50 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781605586045 |
| DOI | 10.1145/1572614.1572622 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-08-01 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Stipple mark distribution Texture analysis and synthesis Statistical similarity Computer-generated stippling |
| Content Type | Text |
| Resource Type | Article |
