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	Extended Bidirectional Texture Function Moving Average Model Havlíček, Michal The bidirectional texture function (BTF) is the recent most advanced representation of visual properties of material surface. It specifies its appearance due to varying spatial, illumination, and viewing conditions. Corresponding enormous BTF measurements require compact mathematical representation for visual fidelity preserving compression. We present a novel BTF model based on a set of underlying three dimensional moving average random field (3D MA RF) models. 3D MA assumes the texture considered as a product of a convolution of an uncorrelated three dimensional random field with a three dimensional filter which completely characterizes the texture. The BTF model combines several spatial factors, subsequently factorized into a set of 3D MA representations, and range map to produce the required BTF texture. This enables high BTF space compression ratio, unrestricted texture enlargement, and reconstruction of unmeasured parts of the BTF space. We also compare proposed model with its simpler two dimensional variant in terms of colour distribution fidelity. Úplný záznam
	Bidirectional Texture Function Three Dimensional Pseudo Gaussian Markov Random Field Model Havlíček, Michal The Bidirectional Texture Function (BTF) is the recent most advanced representation of material surface visual properties. BTF specifies the changes of its visual appearance due to varying illumination and viewing angles. Such a function might be represented by thousands of images of given material surface. Original data cannot be used due to its size and some compression is necessary. This paper presents a novel probabilistic model for BTF textures. The method combines synthesized smooth texture and corresponding range map to produce the required BTF texture. Proposed scheme enables very high BTF texture compression ratio and may be used to reconstruct BTF space as well. Úplný záznam
	Towards Effective Measurement and Interpolation of Bidirectional Texture Functions Filip, Jiří Bidirectional texture function (BTF) is acquired by taking thousands of material surface images for different illumination and viewing directions. This function, provided it is measured accurately, is typically exploited for visualization of material appearance in visual accuracy demanding applications. However, accurate measurement of the BTF is time and resources demanding task. While the sampling of illumination and viewing directions is in all known measurement systems done uniformly, we believe that to be more effective the sampling should be tailored specifically to reflectance properties of materials to be measured. Hence, we introduce a novel method of sparse BTF sampling. The method starts with collecting information about material visual behavior by means of small initial subset of reflectance samples measurement and analysis. This information is fed into our heuristic algorithm producing sparse material dependent sampling that is consequently used for BTF measurement and interpolation. Úplný záznam

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