TY - GEN
T1 - Stippling by example
AU - Kim, Sung Ye
AU - Maciejewski, Ross
AU - Isenberg, Tobias
AU - Andrews, William M.
AU - Chen, Wei
AU - Sousa, Mario Costa
AU - Ebert, David S.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Computer-generated stippling
KW - Statistical similarity
KW - Stipple mark distribution
KW - Stippling by example
KW - Texture analysis and synthesis
UR - http://www.scopus.com/inward/record.url?scp=70450194566&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70450194566&partnerID=8YFLogxK
U2 - 10.1145/1572614.1572622
DO - 10.1145/1572614.1572622
M3 - Conference contribution
AN - SCOPUS:70450194566
SN - 9781605586045
T3 - NPAR Symposium on Non-Photorealistic Animation and Rendering
SP - 41
EP - 50
BT - Proceedings of NPAR 2009
T2 - NPAR 2009: The 7th International Symposium on Non-Photorealistic Animation and Rendering
Y2 - 1 August 2009 through 2 August 2009
ER -