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Texture Editing Using Frequency Swap Strategy

  • Conference paper
Computer Analysis of Images and Patterns (CAIP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5702))

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Abstract

A fully automatic colour texture editing method is proposed, which allows to synthesise and enlarge an artificial texture sharing anticipated properties from its parent textures. The edited colour texture maintains its original colour spectrum while its frequency is modified according to one or more target template textures. Edited texture is synthesised using a fast recursive model-based algorithm. The algorithm starts with edited and target colour texture samples decomposed into a multi-resolution grid using the Gaussian-Laplacian pyramid. Each band pass colour factors are independently modelled by their dedicated 3D causal autoregressive random field models (CAR). We estimate an optimal contextual neighbourhood and parameters for each of the CAR submodel. The synthesised multi-resolution Laplacian pyramid of the edited colour texture is replaced by the synthesised template texture Laplacian pyramid. Finally the modified texture pyramid is collapsed into the required fine resolution colour texture. The primary benefit of these multigrid texture editing models is their ability to produce realistic novel textures with required visual properties capable of enhancing realism in various texture application areas.

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References

  1. Brooks, S., Dodgson, N.A.: Integrating procedural textures with replicated image editing. In: Proceedings of the 3rd International Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast Asia 2005, Dunedin, New Zealand, November 29 - December 2, pp. 277–280. ACM, New York (2005)

    Chapter  Google Scholar 

  2. Ashikhmin, M.: Synthesizing natural textures. In: ACM Symposium on Interactive 3D Graphics, pp. 217–226 (2001)

    Google Scholar 

  3. Bar-Joseph, Z., El-Yaniv, R., Lischinski, D., Werman, M.: Texture mixing and texture movie synthesis using statistical learning. IEEE Transactions on Visualization and Computer Graphics 7, 120–135 (2001)

    Article  Google Scholar 

  4. Liang, L., Liu, C., Xu, Y.Q., Guo, B., Shum, H.Y.: Real-time texture synthesis by patch-based sampling. ACM Transactions on Graphics (TOG) 20, 127–150 (2001)

    Article  Google Scholar 

  5. Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. ACM Trans. Graph., 327–340 (2001)

    Google Scholar 

  6. Wiens, A.L., Ross, J.: Gentropy: evolving 2d textures. Computers & Graphics 26, 75–88 (2002)

    Article  Google Scholar 

  7. Wang, X., Wang, L., Liu, L., Hu, S., Guo, B.: Interactive modeling of tree bark. In: Proc. 11th Pacific Conf. on Comp. Graphics and Appl., pp. 83–90. IEEE, Los Alamitos (2003)

    Google Scholar 

  8. Brooks, S., Cardle, M., Dodgson, N.A.: Enhanced texture editing using self similarity. In: VVG, pp. 231–238 (2003)

    Google Scholar 

  9. Brooks, S., Dodgson, N.A.: Self-similarity based texture editing. ACM Trans. Graph 21, 653–656 (2002)

    Article  Google Scholar 

  10. Khan, E.A., Reinhard, E., Fleming, R.W., Bülthoff, H.H.: Image-based material editing. ACM Trans. Graph 25, 654–663 (2006)

    Article  Google Scholar 

  11. Besag, J.: Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, Series B B-36, 192–236 (1974)

    MATH  MathSciNet  Google Scholar 

  12. Kashyap, R.: Analysis and synthesis of image patterns by spatial interaction models. In: Kanal, L., Rosenfeld, A. (eds.) Progress in Pattern Recognition, vol. 1. North-Holland, Elsevier (1981)

    Google Scholar 

  13. Haindl, M., Havlíček, V.: Multiresolution colour texture synthesis. In: Dobrovodský, K. (ed.) Proceedings of the 7th International Workshop on Robotics in Alpe-Adria-Danube Region, Bratislava, ASCO Art, pp. 297–302 (1998)

    Google Scholar 

  14. Bennett, J., Khotanzad, A.: Multispectral random field models for synthesis and analysis of color images. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 327–332 (1998)

    Article  Google Scholar 

  15. Bennett, J., Khotanzad, A.: Maximum likelihood estimation methods for multispectral random field image models. IEEE Trans. on Pattern Analysis and Machine Intelligence 21, 537–543 (1999)

    Article  Google Scholar 

  16. Haindl, M., Havlíček, V.: A multiresolution causal colour texture model. In: Amin, A., Pudil, P., Ferri, F., Iñesta, J.M. (eds.) SPR 2000 and SSPR 2000. LNCS, vol. 1876, pp. 114–122. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  17. Haindl, M.: Texture synthesis. CWI Quarterly 4, 305–331 (1991)

    MATH  Google Scholar 

  18. Haindl, M.: Texture modelling. In: Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, Orlando, USA, vol. VII, pp. 634–639. International Institute of Informatics and Systemics (2000)

    Google Scholar 

  19. Haindl, M., Havlíček, V.: A simple multispectral multiresolution markov texture model. In: Texture 2002, The 2nd international workshop on texture analysis and synthesis, Copenhagen, pp. 63–66. Heriot-Watt University (2003)

    Google Scholar 

  20. Haindl, M., Havlíček, V.: A multiscale colour texture model. In: Proceedings of the 16th International Conference on Pattern Recognition, pp. 255–258. IEEE Computer Society, Los Alamitos (2002)

    Google Scholar 

  21. Haindl, M., Filip, J.: Fast BTF texture modelling. In: Texture 2003. Proceedings, Edinburgh, pp. 47–52. IEEE Press, Los Alamitos (2003)

    Google Scholar 

  22. Haindl, M., Filip, J., Arnold, M.: BTF image space utmost compression and modelling method. In: Proceedings of the 17th IAPR International Conference on Pattern Recognition, vol. III, pp. 194–197. IEEE, Los Alamitos (2004)

    Chapter  Google Scholar 

  23. Haindl, M., Filip, J.: A fast probabilistic bidirectional texture function model. LNCS, pp. 298–305. Springer, Heidelberg (2004)

    Google Scholar 

  24. Haindl, M., Filip, J.: Extreme compression and modeling of bidirectional texture function. IEEE Transactions on Pattern Analysis and Machine Intelligence 29, 1859–1865 (2007)

    Article  Google Scholar 

  25. Filip, J., Haindl, M., Chetverikov, D.: Fast synthesis of dynamic colour textures. In: Proceedings of the 18th International Conference on Pattern Recognition, ICPR 2006, vol. IV, pp. 25–28. IEEE Computer Society, Los Alamitos (2006)

    Chapter  Google Scholar 

  26. Vision texture (vistex) database. Technical report, Vision and Modeling Group, http://www-white.media.mit.edu/vismod/

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Haindl, M., Havlíček, V. (2009). Texture Editing Using Frequency Swap Strategy. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_139

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  • DOI: https://doi.org/10.1007/978-3-642-03767-2_139

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03766-5

  • Online ISBN: 978-3-642-03767-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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