Abstract
An artist’s painting is affected by factors such as how he observes objects, his skill in using a brush and materials, and the experience that allows him to correctly apply his skills. The process inevitably results in mistakes contrary to the painter’s original intention. This is a distinguishing factor between painting and photography, but this is the essence of the beauty of painting. The inadequacy of a human being to make a painting exactly as he pleases (as a photograph creates a direct representation of itself) is the starting point of creating a creative work. This paper explains the algorithm that reproduces human errors, as well as the stroke data collection method. Although the results of this research are mainly stylized renderings of modern oil paintings, they have unlimited scalability, in that they can play the role to perform a basic framework. These allow the experimentation with many painting styles through the modification of input data and error generation algorithms.
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References
Baxter W, Wendt J, Lin MC (2004) Impasto—a realistic, interactive model for paint. In: Proc. NPAR’04, pp 45–56
DeCarlo D, Santella A (2002) Stylization and abstraction of photographs. ACM Trans Graph 21(3):769–776
Gooch B, Coombe G, Shirley P (2002) Artistic vision: painterly rendering using computer vision techniques. In: Proc. NPAR’02, pp 83–90
Gooch B, Gooch A (2001) Non-photorealistic rendering. A.K. Peters, Natic
Grosser M (1951) Painter’s eye. Rinehart
Haeberli P (1990) Paint by numbers: abstract image representations. ACM SIGGRAPH Comput Graph 24(4):207–214
Hays J, Essa I (2004) Image and video based painterly animation. In: Proc. NPAR’04, pp 113–120
Hertzmann A (1998) Painterly rendering with curved brush strokes of multiple sizes. In: Proc. SIGGRAPH’98, pp 453–460
Hertzmann A (2002) Fast paint texture. In: Proc. of NPAR’02, pp 91–96
Hertzmann A, Perlin K (2000) Painterly rendering for video and interaction. In: Proc. NPAR’00, pp 7–12
Kasao A, Miyata K (2006) Algorithmic painter: a npr method to generate various styles of painting. Vis Comput 22(1):14–27
Litwinowicz PC (1997) Processing images and video for an impressionist effect. In: Proc. SIGGRAPH’97, pp 407–414
Nehab D, Velho L (2002) Multiscale moment-based painterly rendering. In: Proc. of SIBGRAPI’02, pp 244–251
Park Y, Yoon K (2008) Painterly animation using motion maps. Graph Models 70(1–2):1–15
Rewald J (1990) History of impressionism. Harry N. Abrams, New York
Salisbury MP, Wong MT, Hughes JF, Salesin DH (1997) Orientable textures for image-based pen-and-ink illustration. In: SIGGRAPH 97 conference proceedings, pp 401–406
Schlechtweg S, Germer T, Strothotte T (2005) Renderbots—multi agent systems for direct image generation. Comput Graphics Forum 24(2):283–290
Seo S, Yoon K (2010) Color juxtaposition for pointillism based on an artistic color model and a statistical analysis. Vis Comput 26(6–8):421–431
Shiraishi M, Yamaguchi Y (2000) An algorithm for automatic painterly rendering based on local source image approximation. In: Proc. NPAR’00, pp 53–58
Strothotte T, Schlechtweg S (2002) Non-photorealistic computer graphics: modeling, rendering and animation. Morgan Kaufmann, San Francisco
Tucker P (1986) Monet at Argenteuil. Yale University Press, New Haven
Acknowledgements
This work was supported by the Postdoctoral Research Program of Chung-Ang University 2010 year and the Korean Science and Engineering Foundation (KOSEF) grant funded by the Korean government (MEST) (No.20100018445).
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Seo, S., Ryoo, S. & Park, J. Interactive painterly rendering with artistic error correction. Multimed Tools Appl 65, 221–237 (2013). https://doi.org/10.1007/s11042-011-0796-z
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DOI: https://doi.org/10.1007/s11042-011-0796-z