Advertisement

The Visual Computer

, Volume 32, Issue 1, pp 43–55 | Cite as

Feature-aware natural texture synthesis

  • Fuzhang Wu
  • Weiming DongEmail author
  • Yan Kong
  • Xing Mei
  • Dong-Ming Yan
  • Xiaopeng Zhang
  • Jean-Claude Paul
Original Article

Abstract

This article presents a framework for natural texture synthesis and processing. This framework is motivated by the observation that given examples captured in natural scene, texture synthesis addresses a critical problem, namely, that synthesis quality can be affected adversely if the texture elements in an example display spatially varied patterns, such as perspective distortion, the composition of different sub-textures, and variations in global color pattern as a result of complex illumination. This issue is common in natural textures and is a fundamental challenge for previously developed methods. Thus, we address it from a feature point of view and propose a feature-aware approach to synthesize natural textures. The synthesis process is guided by a feature map that represents the visual characteristics of the input texture. Moreover, we present a novel adaptive initialization algorithm that can effectively avoid the repeat and verbatim copying artifacts. Our approach improves texture synthesis in many images that cannot be handled effectively with traditional technologies.

Keywords

Texture synthesis Texture feature analysis Composite texture Feature-aware synthesis 

Notes

Acknowledgments

We thank anonymous reviewers for their valuable input. We thank Chongyang Ma for providing some results and valuable comments in the preparation of this paper. This work is supported by National Natural Science Foundation of China under project Nos. 61172104, 61271430, 61201402, 61372184, 61372168, and 61331018.

Supplementary material

371_2014_1054_MOESM1_ESM.pdf (26.2 mb)
Supplementary material 1 (pdf 26806 KB)

References

  1. 1.
    Ashikhmin, M.: Synthesizing natural textures. In: SI3D ’01: Proceedings of the 2001 symposium on Interactive 3D graphics, pp. 217–226. ACM Press, New York, NY, USA (2001)Google Scholar
  2. 2.
    Bonet, J.S.D.: Multiresolution sampling procedure for analysis and synthesis of texture images. In: SIGGRAPH ’97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques, pp. 361–368. ACM Press/Addison-Wesley Publishing Co., New York, NY, USA (1997)Google Scholar
  3. 3.
    Dong, W., Zhou, N., Paul, J.C.: Perspective-aware texture analysis and synthesis. Vis. Comput. 24(7–9), 515–523 (2008)CrossRefGoogle Scholar
  4. 4.
    Dong, W., Zhou, N., Paul, J.C.: Interactive example-based natural scene synthesis. In: Third International Symposium on Plant growth modeling, simulation, visualization and applications (PMA), pp. 409–416 (2009)Google Scholar
  5. 5.
    Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: SIGGRAPH ’01: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pp. 341–346. ACM Press, New York, NY, USA (2001)Google Scholar
  6. 6.
    Efros, A.A., Leung, T.K.: Texture synthesis by non-parametric sampling. In: ICCV ’99: Proceedings of the International Conference on Computer vision, vol. 2, p. 1033. IEEE Computer Society, Washington, DC, USA (1999)Google Scholar
  7. 7.
    Eisenacher, C., Lefebvre, S., Stamminger, M.: Texture synthesis from photographs. Comput. Graph. Forum 27(2), 419–428 (2008)CrossRefGoogle Scholar
  8. 8.
    Han, C., Risser, E., Ramamoorthi, R., Grinspun, E.: Multiscale texture synthesis. ACM Trans. Graph. 27(3), 1–8 (2008)CrossRefGoogle Scholar
  9. 9.
    Hoang, M.A., Geusebroek, J.M., Smeulders, A.W.M.: Color texture measurement and segmentation. Signal Process. 85(2), 265–275 (2005)CrossRefzbMATHGoogle Scholar
  10. 10.
    Kim, V.G., Lipman, Y., Funkhouser, T.: Symmetry-guided texture synthesis and manipulation. ACM Trans. Graph. 31(3), 22:1–22:14 (2012)CrossRefGoogle Scholar
  11. 11.
    Kwatra, V., Essa, I., Bobick, A., Kwatra, N.: Texture optimization for example-based synthesis. ACM Trans. Graph. 24(3), 795–802 (2005)CrossRefGoogle Scholar
  12. 12.
    Kwatra, V., Schödl, A., Essa, I., Turk, G., Bobick, A.: Graphcut textures: image and video synthesis using graph cuts. ACM Trans. Graph. 22(3), 277–286 (2003)CrossRefGoogle Scholar
  13. 13.
    Lefebvre, S., Hoppe, H.: Parallel controllable texture synthesis. ACM Trans. Graph. 24(3), 777–786 (2005)CrossRefGoogle Scholar
  14. 14.
    Lefebvre, S., Hoppe, H.: Appearance-space texture synthesis. ACM Trans. Graph. 25(3), 541–548 (2006)CrossRefGoogle Scholar
  15. 15.
    Li, L., Jin, L., Xu, X., Song, E.: Unsupervised color-texture segmentation based on multiscale quaternion gabor filters and splitting strategy. Signal Process. 93(9), 2559–2572 (2013)CrossRefGoogle Scholar
  16. 16.
    Liu, Y., Lin, W.C., Hays, J.: Near-regular texture analysis and manipulation. ACM Trans. Graph. 23(3), 368–376 (2004)CrossRefGoogle Scholar
  17. 17.
    Ma, C., Wei, L.Y., Lefebvre, S., Tong, X.: Dynamic element textures. ACM Trans. Graph. 32(4), 90:1–90:10 (2013)CrossRefGoogle Scholar
  18. 18.
    Ma, C., Wei, L.Y., Tong, X.: Discrete element textures. ACM Trans. Graph. 30(4), 62:1–62:10 (2011)CrossRefGoogle Scholar
  19. 19.
    Park, H., Byun, H., Kim, C.: Multi-exemplar inhomogeneous texture synthesis. Comput. Graph. 37(1–2), 54–64 (2013)CrossRefGoogle Scholar
  20. 20.
    Rosenberger, A., Cohen-Or, D., Lischinski, D.: Layered shape synthesis: automatic generation of control maps for non-stationary textures. ACM Trans. Graph. 28(5), 107:1–107:9 (2009)CrossRefGoogle Scholar
  21. 21.
    Rubner, Y., Tomasi, C., Guibas, L.J.: The earth mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)CrossRefzbMATHGoogle Scholar
  22. 22.
    Tong, X., Zhang, J., Liu, L., Wang, X., Guo, B., Shum, H.Y.: Synthesis of bidirectional texture functions on arbitrary surfaces. ACM Trans. Graph. 21(3), 665–672 (2002)CrossRefGoogle Scholar
  23. 23.
    Wei, L.Y.: Multi-class blue noise sampling. ACM Trans. Graph. 29(4), 79:1–79:8 (2010)Google Scholar
  24. 24.
    Wei, L.Y., Lefebvre, S., Kwatra, V., Turk, G.: State of the art in example-based texture synthesis. In: Eurographics 2009, State of the Art Report, EG-STAR, pp. 93–117. Eurographics Association (2009)Google Scholar
  25. 25.
    Wei, L.Y., Levoy, M.: Fast texture synthesis using tree-structured vector quantization. In: SIGGRAPH ’00: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pp. 479–488. ACM Press/Addison-Wesley Publishing Co., New York, NY, USA (2000)Google Scholar
  26. 26.
    Wu, Q., Yu, Y.: Feature matching and deformation for texture synthesis. ACM Trans. Graph. 23(3), 364–367 (2004)CrossRefGoogle Scholar
  27. 27.
    Yan D-M., Wonka P.: Gap Processing for Adaptive Maximal Poisson-disk Sampling. ACM Trans. Graph. 32(5), 148:1–148:15 (2013)Google Scholar
  28. 28.
    Zalesny, A., Ferrari, V., Caenen, G., Van Gool, L.: Composite texture synthesis. Int. J. Comput. Vis. 62(1–2), 161–176 (2005)CrossRefGoogle Scholar
  29. 29.
    Zhang, J., Zhou, K., Velho, L., Guo, B., Shum, H.Y.: Synthesis of progressively-variant textures on arbitrary surfaces. In: SIGGRAPH ’03: ACM SIGGRAPH 2003 Papers, pp. 295–302. ACM, New York, NY, USA (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Fuzhang Wu
    • 1
  • Weiming Dong
    • 1
    Email author
  • Yan Kong
    • 1
  • Xing Mei
    • 1
  • Dong-Ming Yan
    • 1
    • 2
  • Xiaopeng Zhang
    • 1
  • Jean-Claude Paul
    • 3
  1. 1.LIAMA-NLPRInstitute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.Visual Computing Center, King Abdullah University of Science and TechnologyThuwalKingdom of Saudi Arabia
  3. 3.Project CAD, INRIAParisFrance

Personalised recommendations