A Compact Model for Viewpoint Dependent Texture Synthesis

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.

[1]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[2]  B. Julesz,et al.  Early visual perception. , 1981, Annual review of psychology.

[3]  Songde Ma,et al.  Sequential synthesis of natural textures , 1985, Comput. Vis. Graph. Image Process..

[4]  Georgy L. Gimel'farb,et al.  Low‐level bayesian segmentation of piecewise‐homogeneous noisy and textured images , 1991, Int. J. Imaging Syst. Technol..

[5]  Georgy L. Gimel'farb,et al.  Probabilistic models of digital region maps based on Markov random fields with short- and long-range interaction , 1993, Pattern Recognit. Lett..

[6]  Georgy L. Gimel'farb,et al.  Markov Random Fields with Short- and Long-Range Interaction for Modelling Gray-Scale Textured Images , 1993, CAIP.

[7]  Alexey Zalesny,et al.  Homogeneity & texture. General approach , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[8]  Jeremy S. De Bonet,et al.  Multiresolution sampling procedure for analysis and synthesis of texture images , 1997, SIGGRAPH.

[9]  Roland Wilson,et al.  A two-component model of texture for analysis and synthesis , 1998, IEEE Trans. Image Process..

[10]  Georgy L. Gimel'farb On the maximum likelihood potential estimates for Gibbs random field image models , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[11]  Shree K. Nayar,et al.  Reflectance and texture of real-world surfaces , 1999, TOGS.

[12]  Jitendra Malik,et al.  Recognizing surfaces using three-dimensional textons , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[13]  Shree K. Nayar,et al.  Correlation model for 3D texture , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[14]  Alexei A. Efros,et al.  Texture synthesis by non-parametric sampling , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[15]  Song-Chun Zhu,et al.  Equivalence of Julesz and Gibbs texture ensembles , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[16]  Eero P. Simoncelli,et al.  Texture modeling and synthesis using joint statistics of complex wavelet coefficients , 1999 .

[17]  Georgy L. Gimel'farb,et al.  Image Textures and Gibbs Random Fields , 1999, Computational Imaging and Vision.

[18]  A. Zalesny Analysis and Synthesis of Textures With Pairwise Signal Interactions , 2002 .