Abstract
A texture synthesis method is presented that generates similar texture from an example image. It is based on the emulation of simple but rather carefully chosen image intensity statistics. The resulting texture models are compact and no longer require the example image from which they were derived. They make explicit some structural aspects of the textures and the modeling allows knitting together different textures with convincingly looking transition zones. As textures are seldom flat, it is important to also model 3D effects when textures change under changing viewpoint. The simulation of such changes is supported by the model, assuming examples for the different viewpoints are given.
Acknowledgment
The authors gratefully acknowledge support by the European ISP project “MESH”.
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References
K. Dana and S. Nayar. Correlation Model for 3D Texture. Proc. Int. Conf. Computer Vision (ICCV’99), 1999, pp. 1061–1066.
K.J. Dana, B. Van Ginneken, S.K. Nayar, and J.J. Koenderink. Reflectance and Texture of Real-World Surfaces. ACM Transactions on Graphics, Vol. 18, No. 1, 1999, pp. 1–34.
J.S. De Bonet. Multiresolution Sampling Procedure for Analysis and Synthesis of Texture Images. Proc. Computer Graphics, ACM SIGGRAPH’97, 1997, pp. 361–368.
A. Efros and T. Leung. Texture synthesis by non-parametric sampling. Proc. Int. Conf. Computer Vision (ICCV’99), Vol. 2, 1999, pp. 1033–1038.
A. Gagalowicz and S.D. Ma. Sequential Synthesis of Natural Textures. Computer Vision, Graphics, and Image Processing, Vol. 30, 1985, pp. 289–315.
G. Gimel’farb. On the Maximum Likelihood Potential Estimates for Gibbs Random Field Image Models. Proc. Int. Conf. Pattern Recognition (ICPR’98), Vol. II, 1998, pp. 1598–1600.
G. Gimel’farb, Image Textures and Gibbs Random Fields. Kluwer Academic Publishers: Dordrecht, 1999, 250 p.
G. Gimel’farb and A. Zalesny. Low-Level Bayesian Segmentation of Piecewise Homogeneous Noisy and Textured Images. Int. J. of Imaging Systems and Technology, Vol. 3, No. 3, 1991, pp. 227–243.
G.L. Gimel’farb and A.V. Zalesny. Probabilistic Models of Digital Region Maps Based on Markov Random Fields with Short a Long-Range Interaction. Pattern Recognition Letters, Vol. 14, Oct. 1993, pp. 789–797.
G.L. Gimel’farb and A.V. Zalesny. Markov Random Fields with Short and Long-Range Interaction for Modeling Gray-Scale Texture Images. Proc. 5th Int. Conf. Computer Analysis of Images and Patterns (CAIP’93), Lecture Notes in Computer Science 719, Berlin: Springer-Verlag, 1993, pp. 275–282.
T.I. Hsu and R. Wilson. A Two-Component Model of Texture for Analysis and Synthesis. IEEE Trans. on Image Processing, Vol. 7, No. 10, Oct. 1998, pp. 1466–1476.
B. Julesz and R.A. Schumer. Early visual perception. Ann. Rev. Psychol. Vol. 32, 1981, pp. 575–627 (p. 594).
T. Leung and J. Malik. Recognizing surfaces using three-dimensional textons. Proc. Int. Conf. Computer Vision (ICCV’99), 1999, pp. 1010–1017.
N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller. Equations of State Calculations by Fast Computing Machines. J. Chem. Phys., Vol. 21, 1953, pp. 1087–1091.
J. Portilla and E. P. Simoncelli. Texture Modeling and Synthesis using Joint Statistics of Complex Wavelet Coefficients. Proc. of the IEEE Workshop on Statistical and Computational Theories of Vision, Fort Collins, CO, June, 1999, http://www.cis.ohiostate.edu/~szhu/SCTV99.html
Y.N. Wu, S.C. Zhu, and X. Liu. Equivalence of Julesz and Gibbs texture ensembles. Proc. Int. Conf. Computer Vision (ICCV’99), 1999, pp. 1025–1032.
A.V. Zalesny. Homogeneity & Texture. General Approach. Proc. 12th IAPR Int. Conf. Pattern Recognition (ICPR’94), Vol. 1, 1994, pp. 525–527.
A. Zalesny. Analysis and Synthesis of Textures with Pairwise Signal Interactions. Tech. Report KUL/ESAT/PSI/9902, Katholieke Universiteit Leuven, Belgium, 1999, p. 132, http://www.vision.ee.ethz.ch/~zales.
S.C. Zhu, Y.N. Wu, and D. Mumford. Filters, Random Fields And Maximum Entropy (FRAME). Int. J. Computer Vision, Vol. 27, No. 2, March/April 1998, pp. 1–20.
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Zalesny, A., Van Gool, L. (2001). A Compact Model for Viewpoint Dependent Texture Synthesis. In: Pollefeys, M., Van Gool, L., Zisserman, A., Fitzgibbon, A. (eds) 3D Structure from Images — SMILE 2000. SMILE 2000. Lecture Notes in Computer Science, vol 2018. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45296-6_9
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DOI: https://doi.org/10.1007/3-540-45296-6_9
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