A Renormalization Group Approach to Image Processing Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture modelling by discrete distribution mixtures
Computational Statistics & Data Analysis
Probabilistic Discrete Mixtures Colour Texture Models
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Advanced textural representation of materials appearance
SIGGRAPH Asia 2011 Courses
Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
A plausible texture enlargement and editing compound Markovian model
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
Bidirectional texture function simultaneous autoregressive model
MUSCLE'11 Proceedings of the 2011 international conference on Computational Intelligence for Multimedia Understanding
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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 monos-pectral 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.