Publication details

BTF Compound Texture Model with Fast Iterative Non-Parametric Control Field Synthesis

Conference Paper (international conference)

Haindl Michal, Havlíček Vojtěch


serial: SITIS 2018. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems, p. 98-105 , Eds: Sanniti di Baja G., Gallo L., Yetongnon K., Dipanda A., Castrillón-Santana M., Chbeir R.

action: SITIS 2018. International Conference on Signal Image Technology & Internet Based Systems /14./, (Las Palmas de Gran Canaria, ES, 20181126)

keywords: BTF texture model, compound Markov random field, BTF texture synthesis

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abstract (eng):

We propose a substantial speed up a modification to our recently published novel multidimensional statistical model for realistic modeling, enlargement, editing, and compression of the recent state-of-the-art Bidirectional Texture Function (BTF) textural representation. The multispectral compound Markov random field model (CMRF) efficiently fuses a non-parametric random field model with several parametric Markovian random fields models. The principal application of our model is physically correct and realistic synthetic imitation of material texture, its enlargement, and huge compression. So that ideally, both natural and synthetic texture of a given measured natural or artificial texture will be visually indiscernible for any observation or illumination directions. The presented model can be easily applied also for BTF material texture editing to model non-measured or unmeasurable but still realistic material textures. The CMRF model consists of several parametric sub-models each having different characteristics along with an underlying switching structure model which controls transitions between these submodels. The proposed model uses the non-parametric random field for distributing local texture models in the form of analytically solvable wide-sense BTF Markov representation for single regions among the fields of a mosaic approximated by the random field structure model. The non-parametric control field of the BTF-CMRF is iteratively generated to guarantee identical region-size histograms for all material sub-classes present in the target example texture. The present iterative algorithm significantly cuts the number of iterations to converge in comparison with our previous iterative method and even sometimes skip all iteration due to its ingenious initialization. The local texture regions (not necessarily continuous) are represented by analytical BTF models modeled by the adaptive 3D causal auto-regressive (3DCAR) random field model which can be analytically estimated as well as synthesized. The visual quality of the resulting complex synthetic textures generally surpasses the outputs of the previously published simpler non-compound BTF-MRF models and allows to reach tremendous compression ratio incomparable with any standard image compression method.

RIV: BD