TY - JOUR
T1 - Rhythmic brushstrokes distinguish van gogh from his contemporaries
T2 - Findings via automated brushstroke extraction
AU - Li, Jia
AU - Yao, Lei
AU - Hendriks, Ella
AU - Wang, James Z.
N1 - Funding Information:
During the four-year period of this research, J. Li was supported by the US National Science Foundation (NSF) under Grant Nos. 0936948 and 0705210, and The Pennsylvania State University. L. Yao and J.Z. Wang were supported under NSF Grant No. 0347148 and The Pennsylvania State University. The computational infrastructure was provided under NSF Grant No. 0821527. The Van Gogh and the Kröller-Müller Museums provided the photographs of the original paintings for this research. C.R. Johnson Jr., E. Hendriks, and L. van Tilborgh prepared the challenges before the Second International Workshop on Image Processing for Artist Identification, Van Gogh Museum, October 2008. The authors are also indebted to Eric Postma, Igor Berezhnoy, Eugene Brevdo, Shannon Hughes, Ingrid Daubechies, and David Stork for assistance with image data preparation and for useful discussions. They thank John Schleicher at Penn State University for producing the manually marked brushstrokes. J. Li would like to thank C.R. Rao and Robert M. Gray for encouragement, and her toddler daughter Justina for enduring her occasional late working hours. Portions of the work were done when J.Z. Wang was a visiting professor of robotics at Carnegie Mellon University in 2008, and when he was a program manager at the NSF in 2011. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Foundation. He would like to thank Takeo Kanade, Gio Wiederhold, Dennis A. Hejhal, Maria Zeman-kova, and Stephen Griffin for encouragement. The authors thank the reviewers and the associate editor for constructive comments.
PY - 2012
Y1 - 2012
N2 - Art historians have long observed the highly characteristic brushstroke styles of Vincent van Gogh and have relied on discerning these styles for authenticating and dating his works. In our work, we compared van Gogh with his contemporaries by statistically analyzing a massive set of automatically extracted brushstrokes. A novel extraction method is developed by exploiting an integration of edge detection and clustering-based segmentation. Evidence substantiates that van Gogh's brushstrokes are strongly rhythmic. That is, regularly shaped brushstrokes are tightly arranged, creating a repetitive and patterned impression. We also found that the traits that distinguish van Gogh's paintings in different time periods of his development are all different from those distinguishing van Gogh from his peers. This study confirms that the combined brushwork features identified as special to van Gogh are consistently held throughout his French periods of production (1886-1890).
AB - Art historians have long observed the highly characteristic brushstroke styles of Vincent van Gogh and have relied on discerning these styles for authenticating and dating his works. In our work, we compared van Gogh with his contemporaries by statistically analyzing a massive set of automatically extracted brushstrokes. A novel extraction method is developed by exploiting an integration of edge detection and clustering-based segmentation. Evidence substantiates that van Gogh's brushstrokes are strongly rhythmic. That is, regularly shaped brushstrokes are tightly arranged, creating a repetitive and patterned impression. We also found that the traits that distinguish van Gogh's paintings in different time periods of his development are all different from those distinguishing van Gogh from his peers. This study confirms that the combined brushwork features identified as special to van Gogh are consistently held throughout his French periods of production (1886-1890).
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U2 - 10.1109/TPAMI.2011.203
DO - 10.1109/TPAMI.2011.203
M3 - Article
C2 - 22516651
AN - SCOPUS:84860239000
SN - 0162-8828
VL - 34
SP - 1159
EP - 1176
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
JF - IEEE Transactions on Pattern Analysis and Machine Intelligence
IS - 6
M1 - 6042878
ER -