Texture Editing Using Frequency Swap Strategy

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.

[2]  Baining Guo,et al.  Real-time texture synthesis by patch-based sampling , 2001, TOGS.

[3]  Brian J. Ross,et al.  Gentropy: evolving 2D textures , 2002, Comput. Graph..

[4]  Dmitry Chetverikov,et al.  Fast Synthesis of Dynamic Colour Textures , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[5]  J. Besag Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[6]  Alireza Khotanzad,et al.  Maximum Likelihood Estimation Methods for Multispectral Random Field Image Models , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Alireza Khotanzad,et al.  Multispectral Random Field Models for Synthesis and Analysis of Color Images , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Michal Haindl,et al.  A Multiresolution Causal Colour Texture Model , 2000, SSPR/SPR.

[9]  Neil A. Dodgson,et al.  Integrating procedural textures with replicated image editing , 2005, GRAPHITE '05.

[10]  Neil A. Dodgson,et al.  Enhanced Texture Editing using Self Similarity , 2003, VVG.

[11]  Erik Reinhard,et al.  Image-based material editing , 2005, SIGGRAPH '05.

[12]  Michael Ashikhmin,et al.  Synthesizing natural textures , 2001, I3D '01.

[13]  M. Haindl,et al.  Multiresolution Colour Texture Synthesis , 1998 .

[14]  R. L. Kashyap,et al.  Analysis and Synthesis of Image Patterns by Spatial Interaction Models , 1981 .

[15]  Baining Guo,et al.  Interactive modeling of tree bark , 2003, 11th Pacific Conference onComputer Graphics and Applications, 2003. Proceedings..

[16]  Jirí Filip,et al.  A Fast Probabilistic Bidirectional Texture Function Model , 2004, ICIAR.

[17]  Michal Haindl,et al.  BTF image space utmost compression and modelling method , 2004, ICPR 2004.

[18]  Michal Haindl,et al.  A multiscale colour texture model , 2002, Object recognition supported by user interaction for service robots.

[19]  R. Casey,et al.  Advances in Pattern Recognition , 1971 .

[20]  Neil A. Dodgson,et al.  Self-similarity based texture editing , 2002, SIGGRAPH.

[21]  Dani Lischinski,et al.  Texture Mixing and Texture Movie Synthesis Using Statistical Learning , 2001, IEEE Trans. Vis. Comput. Graph..

[22]  Jirí Filip,et al.  Extreme Compression and Modeling of Bidirectional Texture Function , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  David Salesin,et al.  Image Analogies , 2001, SIGGRAPH.