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Iterative Brush Path Extraction Algorithm for Aiding Flock Brush Simulation of Stroke-Based Painterly Rendering

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Book cover Evolutionary and Biologically Inspired Music, Sound, Art and Design (EvoMUSART 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9596))

Abstract

Painterly algorithms form an important part of non-photorealistic rendering (NPR) techniques where the primary aim is to incorporate expressive and stylistic qualities in the output. Extraction, representation and analysis of brush stroke parameters are essential for mapping artistic styles in stroke based rendering (SBR) applications. In this paper, we present a novel iterative method for extracting brush stroke regions and paths for aiding a particle swarm based SBR process. The algorithm and its implementation aspects are discussed in detail. Experimental results are presented showing the painterly rendering of input images and the extracted brush paths.

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References

  1. Gooch, B., Gooch, A.: Non-photorealistic Rendering. AK Peters Ltd., Natick (2001)

    MATH  Google Scholar 

  2. Hertzmann, A.: A Survey of Stroke-based rendering. In: IEEE Computer Graphics and Application, vol. 4, pp. 70–81. IEEE Press, New York (2003)

    Google Scholar 

  3. Whitted, T.: Anti-aliased line drawing using brush extrusion. Assoc. Comput. Machi. Spec. Interest Group Comput. Graph. Interact. Techn. Comput. Graph. 17(3), 151–156 (1983). ACM

    Google Scholar 

  4. Strassman, S.: Hairy brush. Assoc. Comput. Machi. Spec. Interest Group Comput. Graph. Interact. Techn. Comput. Graph. 20(4), 225–232 (1986)

    Google Scholar 

  5. Pham, B.: Expressive brush strokes. Comput. Vis. Graph. Image Process. Graph. Models Image Process. 53(1), 1–6 (1991). Elsevier

    MATH  Google Scholar 

  6. Pudet, T.: Real time fitting of hand-sketched pressure brushstrokes. Comput. Graph. Forum 13(3), 205–220 (1994). Wiley Online Library

    Article  Google Scholar 

  7. Hertzmann, A.: Painterly rendering with curved brush strokes of multiple sizes. In: 25th Annual Conference on Computer graphics and Interactive Techniques 1998, pp. 453–460. ACM (1998)

    Google Scholar 

  8. Huang, H.E., Lim, M.H., Chen, X., Ho, C.S.: Interactive GA flock brush for non-photorealistic rendering. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds.) SEAL 2012. LNCS, vol. 7673, pp. 480–490. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  9. Li, J., Yao, L., Hendriks, E., Wang, J.Z.: Rhythmic brushstrokes distinguish Van Gogh from his contemporaries: findings via automated brushstroke extraction. IEEE Trans. Pattern Anal. Mach. Intell. 34(6), 1159–1176 (2012). IEEE

    Article  Google Scholar 

  10. Meer, P., Georgescu, B.: Edge detection with embedded confidence. IEEE Trans. Pattern Anal. Machine Intell. 23(12), 1351–1365 (2001). IEEE

    Article  Google Scholar 

  11. Johnson Jr., C.R., Hendriks, E., Berezhnoy, I.J., Brevdo, E., Hughes, S.M., Daubechies, I., Li, J., Postma, E., Wang, J.Z.: Image processing for artist identification. IEEE Sig. Process. Mag. 25(4), 37–48 (2008). IEEE

    Article  Google Scholar 

  12. Berezhnoy, I.E., Postma, E., Van Den Herik, H.J.: Automatic extraction of brushstroke orientation from paintings. Mach. Vis. Appl. 20(1), 1–9 (2009). Springer

    Article  MATH  Google Scholar 

  13. Seo, S., Lee, H.: Pixel based stroke generation for painterly effect using maximum homogeneity neighbor filter. Multimedia Tools Appl. 74(10), 3317–3328 (2015). Springer

    Article  MathSciNet  Google Scholar 

  14. Gooch, B., Coombe, G., Shirley, P.: Artistic vision: painterly rendering using computer vision techniques. In: 2nd International Symposium on Non-photorealistic Animation and Rendering 2002, pp. 83-ff. ACM (2002)

    Google Scholar 

  15. Obaid, M., Mukundan, R., Bell, T.: Enhancement of moment based painterly rendering using connected components. In: International Conference on Computer Graphics, Imaging and Visualization 2006, Sydney, pp 378–383. IEEE (2006)

    Google Scholar 

  16. Reinhard, E., Khan, E.A., Akyuz, A.O., Johnson, G.: Colour Imaging: Fundamentals and Applications. AK Peters Ltd, Wellesley (2008)

    Google Scholar 

  17. Connolly, C., Fleiss, T.: A study of efficiency and accuracy in the transformation from RGB to CIELAB colour space. IEEE Trans. Image Process. 6(7), 1046–1048 (1997). IEEE

    Article  Google Scholar 

  18. Huang, H. E., Ong, Y. S., Chen, X.: Autonomous flock brush for non-photorealistic rendering. In: IEEE Congress on Evolutionary Computation 2012, pp. 1–8. IEEE (2012)

    Google Scholar 

  19. Kennedy, J., Kennedy, J.F., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  20. Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput. Graph. 4(21), 25–34 (1987). ACM

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Canterbury, Christchurch, New Zealand

    Tieta Putri & Ramakrishnan Mukundan

Authors
  1. Tieta Putri
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  2. Ramakrishnan Mukundan
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Corresponding author

Correspondence to Tieta Putri .

Editor information

Editors and Affiliations

  1. University of Kent, Canterbury, United Kingdom

    Colin Johnson

  2. RMIT University, Melbourne, Victoria, Australia

    Vic Ciesielski

  3. University of Coimbra, Coimbra, Portugal

    João Correia

  4. University of Coimbra, Coimbra, Portugal

    Penousal Machado

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© 2016 Springer International Publishing Switzerland

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Putri, T., Mukundan, R. (2016). Iterative Brush Path Extraction Algorithm for Aiding Flock Brush Simulation of Stroke-Based Painterly Rendering. In: Johnson, C., Ciesielski, V., Correia, J., Machado, P. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2016. Lecture Notes in Computer Science(), vol 9596. Springer, Cham. https://doi.org/10.1007/978-3-319-31008-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-31008-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31007-7

  • Online ISBN: 978-3-319-31008-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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