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E-grāmata: Visual Texture: Accurate Material Appearance Measurement, Representation and Modeling

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Visual Texture: Accurate Material Appearance Measurement, Representation and ModelingPalielināt
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This book surveys the state of the art in multidimensional, physically-correct visual texture modeling. The authors review the entire process of texture synthesis, visualization, measurement and analysis, as well as applications in medicine and industry.

This book surveys the state of the art in multidimensional, physically-correct visual texture modeling. Features: reviews the entire process of texture synthesis, including material appearance representation, measurement, analysis, compression, modeling, editing, visualization, and perceptual evaluation; explains the derivation of the most common representations of visual texture, discussing their properties, advantages, and limitations; describes a range of techniques for the measurement of visual texture, including BRDF, SVBRDF, BTF and BSSRDF; investigates the visualization of textural information, from texture mapping and mip-mapping to illumination- and view-dependent data interpolation; examines techniques for perceptual validation and analysis, covering both standard pixel-wise similarity measures and also methods of visual psychophysics; reviews the applications of visual textures, from visual scene analysis in medical applications, to high-quality visualizations in the automotive industry.
1 Motivation
1(8)
1.1 Visual Texture Definition
1(5)
1.2 Contents Overview
6(3)
References
7(2)
2 Representation
9(14)
2.1 General Reflectance Function
9(3)
2.2 Textured Model Representation Taxonomy
12(5)
2.2.1 Bidirectional Surface Scattering Reflectance Distribution Function
12(1)
2.2.2 Bidirectional Reflectance and Transmittance Texture Function
13(1)
2.2.3 Bidirectional Texture Function
14(1)
2.2.4 Spatially Varying BRDF
14(1)
2.2.5 Surface Light Field
15(1)
2.2.6 Surface Reflectance Field
16(1)
2.2.7 Multispectral Texture
16(1)
2.3 Representation Taxonomy of Homogeneous Models
17(4)
2.3.1 Bidirectional Scattering Distribution Function
18(1)
2.3.2 Bidirectional Reflectance Distribution Function
18(1)
2.3.3 Bidirectional Transmittance Distribution Function
19(1)
2.3.4 Isotropic Bidirectional Reflectance Distribution Function
20(1)
2.4 Attributes of Taxonomical Classes
21(2)
2.4.1 Taxonomical Class Advantages
21(1)
2.4.2 Taxonomical Class Drawbacks
22(1)
References
22(1)
3 Texture Acquisition
23(40)
3.1 High Dynamic Range Texture Acquisition
23(1)
3.2 Static Textures Acquisition
24(1)
3.3 Dynamic Textures Acquisition
25(1)
3.4 BRDF Acquisition
26(9)
3.4.1 Gonioreflectometers-Based BRDF Setups
28(2)
3.4.2 Mirror-Based BRDF Setups
30(2)
3.4.3 Image-Based BRDF Acquisition
32(2)
3.4.4 Portable BRDF Acquisition Systems
34(1)
3.5 Spatially Varying BRDF Acquisition
35(2)
3.6 BTF Acquisition
37(10)
3.6.1 Gonioreflectometers-Based BTF Setups
38(4)
3.6.2 Mirror-Based BTF Setups
42(1)
3.6.3 Other BTF Setups
42(1)
3.6.4 Sparse Sampling of BTF
43(1)
3.6.5 BTF Setups Overview
44(1)
3.6.6 A BTF Setup Design
44(3)
3.7 Measurement of Time-Varying Surfaces
47(1)
3.8 BSSRDF Measurement
48(5)
3.8.1 Diffuse-Specular Separation of Reflectance Measurements
48(2)
3.8.2 Homogeneous Subsurface Scattering Measurement
50(1)
3.8.3 Spatially Varying Subsurface Scattering Measurement
51(2)
3.9 Surface Light and Reflectance Fields Measurements
53(10)
References
55(8)
4 Static Multispectral Textures
63(34)
4.1 Texture Modeling Approaches
63(1)
4.2 Model-Based Representations
64(24)
4.2.1 Spectral Factorization
65(1)
4.2.2 Spatial Factorization
66(1)
4.2.3 Fractal Models
67(1)
4.2.4 Random Mosaics
68(2)
4.2.5 Markovian Models
70(10)
4.2.6 Mixture Models
80(3)
4.2.7 Probabilistic Discrete-Mixture 2D Model
83(1)
4.2.8 Bernoulli Distribution Mixture Model
84(1)
4.2.9 Gaussian-Mixture 2D Model
85(1)
4.2.10 Mixture Model-Based Texture Synthesis
86(1)
4.2.11 Probabilistic Mixture Models Properties
87(1)
4.3 Texture Sampling
88(2)
4.3.1 Texture Sampling Methods
88(1)
4.3.2 Roller
89(1)
4.3.3 Sampling Methods Summary
89(1)
4.4 Hybrid Modeling
90(7)
References
92(5)
5 Dynamic Textures
97(22)
5.1 Introduction
97(1)
5.2 Modeling Approaches
98(3)
5.2.1 Sampling Methods
98(2)
5.2.2 Mathematical Models
100(1)
5.3 Adaptive Models
101(6)
5.3.1 Learning
101(1)
5.3.2 Synthesis
102(1)
5.3.3 Spatio-Temporal Autoregressive Model
102(1)
5.3.4 Multiscale Autoregressive Model
102(1)
5.3.5 Autoregressive Eigen Model
103(2)
5.3.6 Linear Dynamical System
105(2)
53.7 Time-Varying LDS
107(4)
5.3.8 Switching Linear Dynamical System
108(1)
5.3.9 Mixture of LDSs
109(1)
5.3.10 Region-Based LDS
109(1)
5.3.11 Non-parametric Dynamic Model
109(1)
5.3.12 Nonlinear Dynamical System
110(1)
5.4 DT Test Data
111(1)
5.5 Quality Validation
112(1)
5.6 Other Applications
113(1)
5.7 Summary
113(6)
References
114(5)
6 Spatially Varying Bidirectional Reflectance Distribution Functions
119(28)
6.1 BRDF Principle and Properties
119(2)
6.2 BRDF Representations
121(3)
6.3 BRDF Compression
124(2)
6.4 BRDF Models
126(12)
6.4.1 Ideal Mirror and Diffuse Reflection
127(1)
6.4.2 Empirically Derived Reflectance Models
128(4)
6.4.3 Physically Motivated BRDF Models
132(4)
6.4.4 Probabilistic BRDF Models
136(1)
6.4.5 Multilayer BRDF Models
136(1)
6.4.6 BRDF Models Comparison
137(1)
6.5 BRDF Extension to Spatially Varying BRDF
138(3)
6.5.1 Approximative SVBRDF Measurement
139(2)
6.6 BRDF and SVBRDF Editing Methods
141(6)
References
142(5)
7 Bidirectional Texture Functions
147(64)
7.1 BTF Representations
147(1)
7.2 BTF Methods Taxonomy
148(1)
7.3 BTF Dimensionality Analysis
148(4)
7.3.1 Statistical Methods
149(3)
7.3.2 Psychophysical Methods
152(1)
7.4 Compression Methods
152(9)
7.4.1 Pixel-Wise Compression
154(2)
7.4.2 Linear Factorization Approaches
156(3)
7.4.3 Clustering Approaches
159(1)
7.4.4 Approaches Combining Surface Geometry and Reflectance
160(1)
7.4.5 Other Approaches
160(1)
7.5 Modeling Methods
161(25)
7.5.1 Sampling Methods
162(4)
7.5.2 Spatial Enlargement of BTF Reflectance Models
166(8)
7.5.3 Statistical Models
174(9)
7.5.4 Hybrid Methods
183(2)
7.5.5 Compound Methods
185(1)
7.6 BTF Editing
186(1)
7.7 Comparison of Selected Methods
187(16)
7.7.1 Tested Methods Description
187(5)
7.7.2 A Psychophysical Comparison
192(3)
7.7.3 Computational and Visual Quality Comparison
195(2)
7.7.4 Parametric Representation Size and Compression
197(4)
7.7.5 Speed Comparison
201(1)
7.7.6 Discussion
202(1)
7.8 Summary
203(8)
References
205(6)
8 Visualization
211(20)
8.1 Introduction
211(1)
8.2 Texture Mapping
212(1)
8.3 World vs. Local Coordinate Systems
213(1)
8.4 Local Coordinate System
214(3)
8.4.1 Barycentric Coordinates
216(1)
8.5 Surface Height Simulation
217(3)
8.5.1 Bump Mapping
217(3)
8.5.2 Displacement Mapping
220(1)
8.6 Measured Direction Interpolations
220(2)
8.7 Directional Appearance Rendering
222(1)
8.8 Illumination Environment
223(2)
8.9 Texture Anti-aliasing
225(1)
8.10 Rendering Using Graphics Hardware
226(5)
References
228(3)
9 Perceptual Validation and Analysis
231(24)
9.1 Motivation
231(1)
9.2 Texture Similarity Computational Measures
232(6)
9.2.1 Local Similarity Measures
232(4)
9.2.2 Statistical Similarity Measures
236(2)
9.3 Visual Psychophysics
238(17)
9.3.1 Stimuli Preparation
239(1)
9.3.2 Data Analysis
240(1)
9.3.3 Perceptual Texture Space
241(2)
9.3.4 BRDF Visual Perception
243(2)
9.3.5 Perceptually Driven BTF Analysis and Compression
245(5)
References
250(5)
10 Applications
255(22)
10.1 Applied Visual Textures
255(1)
10.2 Editing
256(3)
10.3 Visual Scene Interpretation
259(8)
10.3.1 Segmentation
259(2)
10.3.2 Visual Invariants
261(3)
10.3.3 Medical Applications
264(1)
10.3.4 Security
265(2)
10.4 Human Perception
267(1)
10.5 Correct Visualization of Visual Scenes
267(10)
10.5.1 Movie and Game Industry
268(1)
10.5.2 Car Industry
268(1)
10.5.3 Cultural Heritage Preservation
269(4)
References
273(4)
11 Conclusions and Open Problems
277(4)
11.1 Visual Texture
277(1)
11.2 Measurement
278(1)
11.3 Mathematical Models
278(1)
11.4 Validation
279(1)
11.5 Real-Time Visualization
279(2)
Index 281
Dr. Michal Haindl is a Professor and Head of the Department of Pattern Recognition at the Institute of Information Theory and Automation within the Academy of Sciences of the Czech Republic. Dr. Jiķ Filip is a Research Associate at the same institution.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
Permanent link: https://www.kriso.lv/db/97814471490262e.html
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