The Visual Computer

, Volume 33, Issue 6–8, pp 761–768 | Cite as

Feature-preserving procedural texture

  • HyeongYeop Kang
  • Junghyun HanEmail author
Original Article


This paper presents how to synthesize a texture in a procedural way that preserves the features of the input exemplar. The exemplar is analyzed in both spatial and frequency domains to be decomposed into feature and non-feature parts. Then, the non-feature parts are reproduced as a procedural noise, whereas the features are independently synthesized. They are combined to output a non-repetitive texture that also preserves the exemplar’s features. The proposed method allows the user to control the extent of extracted features and also enables a texture to edited quite effectively.


Procedural texturing Feature preservation Texture analysis Noise by example 



This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIP) (No. NRF-2016R1A2B3014319) and by Institute for Information and Communications Technology Promotion (IITP) Grant funded by the Korea government (MSIP) (No. R0115-16-1011).

Supplementary material

Supplementary material 1 (mp4 8698 KB)


  1. 1.
    Cook, R.L., DeRose, T.: Wavelet noise. In: ACM Transactions on Graphics (TOG), vol. 24, pp. 803–811. ACM (2005)Google Scholar
  2. 2.
    Dischler, J.M., Ghazanfarpour, D.: A procedural description of geometric textures by spectral and spatial analysis of profiles. In: Computer Graphics Forum, vol. 16, pp. C129–C139. Wiley (1997)Google Scholar
  3. 3.
    Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 341–346. ACM (2001)Google Scholar
  4. 4.
    Galerne, B., Lagae, A., Lefebvre, S., Drettakis, G.: Gabor noise by example. ACM Trans. Graph. (TOG) 31(4), 73 (2012)CrossRefGoogle Scholar
  5. 5.
    Gardner, G.Y.: Visual simulation of clouds. In: ACM SIGGRAPH Computer Graphics, vol. 19, pp. 297–304. ACM (1985)Google Scholar
  6. 6.
    Ghazanfarpour, D., Dischler, J.M.: Spectral analysis for automatic 3-d texture generation. Comput. Graph. 19(3), 413–422 (1995)CrossRefGoogle Scholar
  7. 7.
    Gilet, G., Dischler, J.M., Ghazanfarpour, D.: Multiple kernels noise for improved procedural texturing. Vis. Comput. 28(6–8), 679–689 (2012)CrossRefGoogle Scholar
  8. 8.
    Gilet, G., Sauvage, B., Vanhoey, K., Dischler, J.M., Ghazanfarpour, D.: Local random-phase noise for procedural texturing. ACM Trans. Graph. (TOG) 33(6), 195 (2014)CrossRefGoogle Scholar
  9. 9.
    Goldberg, A., Zwicker, M., Durand, F.: Anisotropic noise. In: ACM Transactions on Graphics (TOG), vol. 27, p. 54. ACM (2008)Google Scholar
  10. 10.
    Horner, J.L., Gianino, P.D.: Phase-only matched filtering. Appl. Opt. 23(6), 812–816 (1984)CrossRefGoogle Scholar
  11. 11.
    Kaspar, A., Neubert, B., Lischinski, D., Pauly, M., Kopf, J.: Self tuning texture optimization. In: Computer Graphics Forum, vol. 34, pp. 349–359. Wiley (2015)Google Scholar
  12. 12.
    Lagae, A., Lefebvre, S., Cook, R., DeRose, T., Drettakis, G., Ebert, D.S., Lewis, J.P., Perlin, K., Zwicker, M.: A survey of procedural noise functions. In: Computer Graphics Forum, vol. 29, pp. 2579–2600. Wiley (2010)Google Scholar
  13. 13.
    Lagae, A., Lefebvre, S., Drettakis, G., Dutré, P.: Procedural noise using sparse Gabor convolution. In: ACM Transactions on Graphics (TOG), vol. 28, p. 54. ACM (2009)Google Scholar
  14. 14.
    Lagae, A., Vangorp, P., Lenaerts, T., Dutré, P.: Procedural isotropic stochastic textures by example. Comput. Graph. 34(4), 312–321 (2010)CrossRefGoogle Scholar
  15. 15.
    Levi, A., Stark, H.: Signal restoration from phase by projections onto convex sets. JOSA 73(6), 810–822 (1983)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lewis, J.P.: Algorithms for solid noise synthesis. In: ACM SIGGRAPH Computer Graphics, vol. 23, pp. 263–270. ACM (1989)Google Scholar
  17. 17.
    Lockerman, Y.D., Sauvage, B., Allègre, R., Dischler, J.M., Dorsey, J., Rushmeier, H.: Multi-scale label-map extraction for texture synthesis. ACM Trans. Graph. (SIGGRAPH’16 Tech. Pap.) 35(4), 140 (2016)Google Scholar
  18. 18.
    Morrone, M.C., Burr, D.: Feature detection in human vision: a phase-dependent energy model. Proc. R. Soc. Lond. B Biol. Sci. 235(1280), 221–245 (1988)CrossRefGoogle Scholar
  19. 19.
    Nicoll, A., Meseth, J., Müller, G., Klein, R.: Fractional Fourier texture masks: guiding near-regular texture synthesis. In: Computer Graphics Forum, vol. 24, pp. 569–579. Wiley (2005)Google Scholar
  20. 20.
    Oppenheim, A.V., Lim, J.S.: The importance of phase in signals. Proc. IEEE 69(5), 529–541 (1981)CrossRefGoogle Scholar
  21. 21.
    Perlin, K.: An image synthesizer. ACM SIGGRAPH Comput. Graph. 19(3), 287–296 (1985)CrossRefGoogle Scholar
  22. 22.
    Piotrowski, L.N., Campbell, F.W.: A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase. Perception 11(3), 337–346 (1982)CrossRefGoogle Scholar
  23. 23.
    Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graph. Appl. 21(5), 34–41 (2001)CrossRefGoogle Scholar
  24. 24.
    Ruderman, D.L., Cronin, T.W., Chiao, C.C.: Statistics of cone responses to natural images: implications for visual coding. JOSA A 15(8), 2036–2045 (1998)CrossRefGoogle Scholar
  25. 25.
    Wu, F., Dong, W., Kong, Y., Mei, X., Yan, D.M., Zhang, X., Paul, J.C.: Feature-aware natural texture synthesis. Vis. Comput. 32(1), 43–55 (2016)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Korea UniversitySeoulSouth Korea

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