A Multiresolution Causal Colour Texture Model
An efficient recursive algorithm for realistic colour texture synthesis is proposed. The algorithm starts with spectral factorization of an input colour texture image using the Karhunen-Loeve decorrelation. Single orthogonal monospectral components are further decomposed into a multi-resolution grid and each resolution data are independently modeled by their dedicated simultaneous causal autoregressive random field model (CAR). We estimate an optimal contextual neighbourhood and parameters for each CAR submodel. Finally single synthesized monospectral texture pyramids are collapsed into the fine resolution images and using the inverse Karhunen-Loeve transformation we obtain the required colour texture. The benefit of the multigrid approach is the replacement of a large neighbourhood CAR model with a set of several simpler CAR models which are easy to synthesize and wider application area of these multigrid models capable of reproducing realistic textures for enhancing realism in texture application areas.
KeywordsTexture Synthesis Texture Model Colour Texture Laplacian Pyramid Spectral Factorization
- 3.Haindl, M.:Texture Synthesis. Research Report no. CS-R9139, Centrum voor Wis-kunde en Informatica, Amsterdam, 1991Google Scholar
- 5.Haindl, M., Havlíček, V.: Multiresolution Colour Texture Synthesis. In: Dobro-vodský, K. (ed.): Proceedings 7th Int. Workshop RAAD’98, ISBN: 80-967962-7-5, ASCO Art & Science, Bratislava (1998) 339–344Google Scholar
- 6.Kashyap, R.L.: Analysis and Synthesis of Image Patterns by Spatial Interaction Models. In: Progress in Pattern Recognition 1, (Eds.) L.N. Kanal A. Rosenfeld, Elsevier, North-Holland (1981)Google Scholar