|
|
MSAR BTF Model
Havlíček, Michal
The Bidirectional Texture Function (BTF) is the recent most advanced representation of material surface visual properties. BTF specifies the changes of visual appearance due to varying illumination and viewing conditions. Such a function might be represented by thousands of images of surface taken in given illumination and viewing conditions per sample of the material. Resulting BTF size, hundreds of gigabytes, excludes its direct rendering in graphical applications, accordingly some compression of these data is obviously necessary. This paper presents a novel probabilistic model based algorithm for realistic multispectral BTF texture modelling. This complex but efficient method combines several multispectral band limited spatial factors and corresponding range map to produce the required BTF texture. Proposed scheme enables very high BTF texture compression ratio and in addition may be used to reconstruct BTF space i.e. non-measured parts of the BTF space.
|
|
|
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.
|
|
|
Evaluation of Screening Mammograms by Local Structural Mixture Models
Grim, Jiří ; Lee, G. L.
We consider the recently proposed evaluation of screening mammograms by local statistical models. The model is defined as a joint probability density of inside grey levels of a suitably chosen search window. We approximate the model density by a mixture of Gaussian densities. Having estimated the mixture parameters we calculate at all window positions the corresponding log-likelihood values which can be displayed as grey levels at the respective window centers. The resulting log-likelihood image closely correlates with the original mammogram and emphasizes the structural details. In this paper we try to enhance the log-likelihood images by using structural mixture model capable of suppressing the influence of noisy variables.
|
|
|
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.
|
|
| |
|
|
Komprese dvousměrných texturních dat založaná na víceůrovňové vektorové kvantizaci - doplňkový materiál
Havran, V. ; Filip, Jiří ; Myszkowski, K.
The Bidirectional Texture Function (BTF) is becoming widely used for accurate representation of real-world material appearance. In this paper a novel BTF compression model is proposed. The model resamples input BTF data into a parametrization, allowing decomposition of individual view and illumination dependent texels into a set of multidimensional conditional probability density functions. These functions are compressed in turn using a novel multi-level vector quantization algorithm. The result of this algorithm is a set of index and scale code-books for individual dimensions. BTF reconstruction from the model is then based on fast chained indexing into the nested stored code-books. In the proposed model, luminance and chromaticity are treated separately to achieve further compression. The proposed model achieves low distortion and compression ratios 1:233-1:2040, depending on BTF sample variability.
|
|
|
Vyhodnocení stability jednotlivých metod i skupin metod výběru příznaků, který optimalizují kardinalitu podmnožiny příznaků
Somol, Petr ; Novovičová, Jana
Stability (robustness) of feature selection methods is a topic of recent interest yet often neglected importance with direct impact on the reliability of machine learning systems. We investigate the problem of evaluating the stability of feature selection processes yielding subsets of varying size. We introduce several novel feature selection stability measures and adjust some existing measures in a unifying framework that offers broad insight into the stability problem. We study in detail the properties of considered measures and demonstrate on various examples what information about the feature selection process can be gained. We also introduce an alternative approach to feature selection evaluation in form of measures that enable comparing the similarity of two feature selection processes. These measures enable comparing, e.g., the output of two feature selection methods or two runs of one method with different parameters. The information obtained using the considered stability and similarity measures is shown usable for assessing feature selection methods (or criteria) as such
|
|
|
Zobecněný váleček
Haindl, Michal ; Hatka, Martin
This paper describes a generalization of our previously published simple roller method for seamless enlargement of colour textures such as natural bidirectional texture functions (BTF) that realistically represent appearance of given material surfaces. The generalized roller allows automatic detection of major texture periodicity directions which do not need to be aligned with coordinate axes. The roller texture synthesis method is based on the overlapping tiling and subsequent minimum error boundary cut. One or several optimal double toroidal BTF patches are seamlessly repeated during the synthesis step. While the method allows only moderate texture compression it is extremely fast due to complete separation of the analytical step of the algorithm from the texture synthesis part. The method is universal and easily implementable in a graphical hardware for purpose of real-time rendering of any type of static or dynamic textures.
|
guest ::
