Efnisyfirlit

• Preliminaries
• Dedication
• Preface to Second Edition
• Chapter 1 Introduction
• 1.1 How Visualization Works
• Visualization and insight
• Concrete questions
• Quantitative vs. qualitative questions
• Exact vs. fuzzy questions
• Discover the unknown
• Examples.
• Subfields of data visualization
• Scientific visualization
• Information visualization
• Visual anaytics
• Interactive exploration
• 1.2 Positioning in the Field
• Interactive Data Visualization: Foundations, Techniques, and Applications
• The Visualization Toolkit
• The Visualization Handbook
• Information Visualization Literature
• 1.3 Book Structure
• Chapter 2.
• Chapter 3.
• Chapter 4.
• Chapter 5-7.
• Chapter 8.
• Chapter 9.
• Chapter 10.
• Chapter 11.
• Chapter 12.
• Appendix
• 1.4 Notation
• 1.5 Online Material
• Acknowledgments
• Figure 1.1
• Figure 1.2
• Figure 1.3
• Chapter 2 From Graphics to Visualization
• 2.1 A Simple Example
• 2.2 Graphics-Rendering Basics
• Rendering equation
• 2.3 Rendering the Height Plot
• Flat shading
• Smooth shading
• Computing vertex normals
• 2.4 Texture Mapping
• 2.5 Transparency and Blending
• 2.6 Viewing
• Virtual camera
• Projection
• Viewport
• 2.7 Putting It All Together
• Initialization
• Viewing
• Drawing
• Improvements
• 2.8 Conclusion
• Figure 2.1
• Figure 2.2
• Figure 2.3
• Figure 2.4
• Figure 2.5
• Figure 2.6
• Figure 2.7
• Figure 2.8
• Figure 2.9
• Figure 2.10
• Figure 2.11
• Figure 2.12
• Listings 2.1
• Listings 2.2
• Listings 2.3
• Listings 2.4
• Listings 2.5
• Listings 2.6
• Listings 2.7
• Chapter 3 Data Representation
• 3.1 Continuous Data
• 3.1.1 What Is Continuous Data?
• 3.1.2 Mathematical Continuity
• 3.1.3 Dimensions: Geometry, Topology, and Attributes
• 3.2 Sampled Data
• Interpolation
• Grids and cells
• Putting it all together
• 3.3 Discrete Datasets
• 3.4 Cell Types
• 3.4.1 Vertex
• 3.4.2 Line
• 3.4.3 Triangle
• 3.4.4 Quad
• 3.4.5 Tetrahedron
• 3.4.6 Hexahedron
• 3.4.7 Other Cell Types
• 3.5 Grid Types
• 3.5.1 Uniform Grids
• 3.5.2 Rectilinear Grids
• 3.5.3 Structured Grids
• 3.5.4 Unstructured Grids
• 3.6 Attributes
• 3.6.1 Scalar Attributes
• 3.6.2 Vector Attributes
• 3.6.3 Color Attributes
• RGB space
• HSV space
• Converting between RGB and HSV
• Color perception
• 3.6.4 Tensor Attributes
• Curvature as a tensor
• Tensors, vectors, and scalars
• 3.6.5 Non-Numerical Attributes
• 3.6.6 Properties of Attribute Data
• Completeness
• Multivariate data
• Node vs. cell attributes
• High-variate interpolation
• Normals
• Vectors
• Colors
• Tensors
• 3.7 Computing Derivatives of Sampled Data
• 3.8 Implementation
• 3.8.1 Grid Implementation
• Uniform grids
• Rectilinear grids
• Structured grids
• Unstructured grids
• 3.8.2 Attribute Data Implementation
• Scalar attributes
• Vector attributes
• Storing several attribute instances
• 3.9 Advanced Data Representation
• 3.9.1 Data Resampling
• Cell to vertex resampling
• Vertex to cell resampling
• Subsampling and supersampling
• 3.9.2 Scattered Point Interpolation
• Constructing a grid from scattered points
• Gridless interpolation
• Performance issues
• Shepard interpolation
• 3.10 Conclusion
• Figure 3.1
• Figure 3.2
• Figure 3.3
• Figure 3.4
• Figure 3.5
• Figure 3.6
• Figure 3.7
• Figure 3.8
• Figure 3.9
• Figure 3.10
• Figure 3.11
• Figure 3.12
• Figure 3.13
• Figure 3.14
• Figure 3.15
• Figure 3.16
• Figure 3.17
• Figure 3.18
• Figure 3.19
• Table 3.1
• Table 3.2
• Listings 3.1
• Listings 3.2
• Listings 3.3
• Listings 3.4
• Listings 3.5
• Listings 3.6
• Listings 3.7
• Listings 3.8
• Listings 3.9
• Listings 3.10
• Listings 3.11
• Listings 3.12
• Chapter 4 The Visualization Pipeline
• 4.1 Conceptual Perspective
• 4.1.1 Importing Data
• 4.1.2 Data Filtering and Enrichment
• See what is relevant
• Handle large data
• Ease of use
• 4.1.3 Mapping Data
• Mapping vs. rendering
• Desirable mapping properties
• Inverting the mapping
• Distance preservation
• Organization levels
• Further reading
• 4.1.4 Rendering Data
• 4.2 Implementation Perspective
• Dataflow design
• Dataflow implementation
• Visual dataflow programming
• Simplified visual programming
• 4.3 Algorithm Classification
• 4.4 Conclusion
• Figure 4.1
• Figure 4.2
• Figure 4.3
• Figure 4.4
• Figure 4.5
• Figure 4.6
• Figure 4.7
• Figure 4.8
• Figure 4.9
• Table 4.1
• Listings 4.1
• Chapter 5 Scalar Visualization
• 5.1 Color Mapping
• 5.2 Designing Effective Colormaps
• Color legends
• Rainbow colormap
• Other colormap designs
• Grayscale
• Two-hue
• Heat map
• Diverging
• Zebra colormap
• Interpolation issues
• Color banding
• Additional issues
• 5.3 Contouring
• Contour properties
• Computing contours
• 5.3.1 Marching Squares
• 5.3.2 Marching Cubes
• Marching algorithm variations
• Dividing cubes algorithm
• 5.4 Height Plots
• 5.4.1 Enridged Plots
• 5.5 Conclusion
• Figure 5.1
• Figure 5.2
• Figure 5.3
• Figure 5.4
• Figure 5.5
• Figure 5.6
• Figure 5.7
• Figure 5.8
• Figure 5.9
• Figure 5.10
• Figure 5.11
• Figure 5.12
• Figure 5.13
• Figure 5.14
• Figure 5.15
• Figure 5.16
• Figure 5.17
• Figure 5.18
• Figure 5.19
• Figure 5.20
• Figure 5.21
• Listings 5.1
• Listings 5.2
• Chapter 6 Vector Visualization
• 6.1 Divergence and Vorticity
• Divergence
• Vorticity
• Streamwise vorticity
• Helicity
• 6.2 Vector Glyphs
• Line glyphs
• Cone and arrow glyphs
• 6.2.1 Vector Glyph Discussion
• Vector glyphs in 2D
• Vector glyphs in 3D
• Vector glyphs on 3D surfaces
• 6.3 Vector Color Coding
• Color coding on 2D surfaces
• Color coding on 3D surfaces
• 6.4 Displacement Plots
• Parameter settings
• 6.5 Stream Objects
• 6.5.1 Streamlines and Their Variations
• Streamlines.
• Pathlines
• Streaklines
• Computing streamlines
• Parameter setting
• Accuracy
• Stop criterion
• Geometry
• Streamline seeding
• 6.5.2 Stream Tubes
• 6.5.3 Streamlines and Tubes in 3D Datasets
• 6.5.4 Stream Ribbons
• 6.5.5 Stream Surfaces
• 6.5.6 Streak Surfaces
• 6.6 Texture-Based Vector Visualization
• Line integral convolution
• 6.6.1 IBFV Method
• 6.6.2 IBFV Implementation
• Parameters
• Putting it all together
• 6.6.3 IBFV Examples
• IBFV on curved surfaces
• IBFV on 3D volumes
• 6.7 Simplified Representation of Vector Fields
• 6.7.1 Vector Field Topology
• Topology analysis
• Interpolation issues
• Excluding critical points
• Boundaries
• 6.7.2 Feature Detection Methods
• 6.7.3 Field Decomposition Methods
• Top-down decomposition
• Bottom-up decomposition
• Multiscale decomposition
• Multiscale IBFV
• 6.8 Illustrative Vector Field Rendering
• Depth-dependent halos
• 6.9 Conclusion
• Figure 6.1
• Figure 6.2
• Figure 6.3
• Figure 6.4
• Figure 6.5
• Figure 6.6
• Figure 6.7
• Figure 6.8
• Figure 6.9
• Figure 6.10
• Figure 6.11
• Figure 6.12
• Figure 6.13
• Figure 6.14
• Figure 6.15
• Figure 6.16
• Figure 6.17
• Figure 6.18
• Figure 6.19
• Figure 6.20
• Figure 6.21
• Figure 6.22
• Figure 6.23
• Figure 6.24
• Figure 6.25
• Figure 6.26
• Figure 6.27
• Figure 6.28
• Figure 6.29
• Figure 6.30
• Figure 6.31
• Figure 6.32
• Figure 6.33
• Figure 6.34
• Figure 6.35
• Figure 6.36
• Figure 6.37
• Listings 6.1
• Listings 6.2
• Listings 6.3
• Chapter 7 Tensor Visualization
• 7.1 Principal Component Analysis
• 7.2 Visualizing Components
• 7.3 Visualizing Scalar PCA Information
• Diffusivity
• Anisotropy
• 7.4 Visualizing Vector PCA Information
• 7.5 Tensor Glyphs
• 7.6 Fiber Tracking
• Focus and context
• Fiber clustering
• Tracking challenges
• 7.7 Illustrative Fiber Rendering
• Fiber generation
• Alpha blending
• Anisotropy simplification
• Illustrative rendering
• Fiber bundling
• Fibers in context
• 7.8 Hyperstreamlines
• 7.9 Conclusion
• Figure 7.1
• Figure 7.2
• Figure 7.3
• Figure 7.4
• Figure 7.5
• Figure 7.6
• Figure 7.7
• Figure 7.8
• Figure 7.9
• Figure 7.10
• Figure 7.11
• Figure 7.12
• Figure 7.13
• Figure 7.14
• Figure 7.15
• Figure 7.16
• Chapter 8 Domain-Modeling Techniques
• 8.1 Cutting
• 8.1.1 Extracting a Brick
• 8.1.2 Slicing in Structured Datasets
• 8.1.3 Implicit Function Cutting
• 8.1.4 Generalized Cutting
• 8.2 Selection
• Selecting cells
• Thresholding, segmentation, and contouring
• 8.3 Grid Construction from Scattered Points
• 8.3.1 Triangulation Methods
• Delaunay triangulations
• Voronoi diagrams
• Variation of the basic techniques
• Implementation
• 8.3.2 Surface Reconstruction and Rendering
• Using radial basis functions
• Using signed distance functions
• Local triangulations
• Multiple local triangulations
• Alpha shapes
• Ball pivoting
• Poisson reconstruction
• Surface splatting
• Sphere splatting
• 8.4 Grid-Processing Techniques
• 8.4.1 Geometric Transformations
• 8.4.2 Grid Simplification
• Triangle mesh decimation
• Vertex clustering
• Simplification envelopes
• Progressive meshes
• 8.4.3 Grid Refinement
• Loop subdivision
• Advanced subdivision tools
• 8.4.4 Grid Smoothing
• 8.5 Conclusion
• Figure 8.1
• Figure 8.2
• Figure 8.3
• Figure 8.4
• Figure 8.5
• Figure 8.6
• Figure 8.7
• Figure 8.8
• Figure 8.9
• Figure 8.10
• Figure 8.11
• Figure 8.12
• Figure 8.13
• Figure 8.14
• Figure 8.15
• Figure 8.16
• Figure 8.17
• Figure 8.18
• Figure 8.19
• Figure 8.20
• Figure 8.21
• Chapter 9 Image Visualization
• 9.1 Image Data Representation
• 2D Images
• Higher-dimension images
• 9.2 Image Processing and Visualization
• 9.3 Basic Imaging Algorithms
• 9.3.1 Basic Image Processing
• Transfer functions
• 9.3.2 Histogram Equalization
• 9.3.3 Gaussian Smoothing
• Fourier transform
• Convolution for filtering
• 9.3.4 Edge Detection
• Gradient-based edge detection
• Roberts operator
• Sobel operator
• Prewitt operator
• Laplacian-based edge detection
• 9.4 Shape Representation and Analysis
• 9.4.1 Basic Segmentation
• 9.4.2 Advanced Segmentation
• Snakes
• Normalized cuts
• Mean shift
• Image foresting transform
• Level sets
• Threshold sets
• 9.4.3 Connected Components
• 9.4.4 Morphological Operations
• Dilation and erosion
• 9.4.5 Distance Transforms
• Distance transform properties.
• Brute-force implementation.
• Distance transforms using OpenGL
• Fast Marching Method.
• Other distance transform algorithms
• 9.4.6 Skeletonization
• Centeredness.
• Structural and topological encoding.
• Geometrical encoding.
• Multiscale shape encoding.
• Applications.
• 9.4.7 Skeleton Computation in 2D
• Using distance field singularities.
• Using boundary collapse metric.
• Applications
• 9.4.8 Skeleton Computation in 3D
• Surface skeletons.
• Curve skeletons.
• Thinning methods.
• Distance field methods.
• Geodesic methods.
• Mesh contraction methods.
• Curve skeleton comparison.
• 9.5 Conclusion
• Figure 9.1
• Figure 9.2
• Figure 9.3
• Figure 9.4
• Figure 9.5
• Figure 9.6
• Figure 9.7
• Figure 9.8
• Figure 9.9
• Figure 9.10
• Figure 9.11
• Figure 9.12
• Figure 9.13
• Figure 9.14
• Figure 9.15
• Figure 9.16
• Figure 9.17
• Figure 9.18
• Figure 9.19
• Figure 9.20
• Figure 9.21
• Figure 9.22
• Figure 9.23
• Figure 9.24
• Figure 9.25
• Figure 9.26
• Figure 9.27
• Listings 9.1
• Listings 9.2
• Listings 9.3
• Listings 9.4
• Listings 9.5
• Listings 9.6
• Chapter 10 Volume Visualization
• 10.1 Motivation
• 10.2 Volume Visualization Basics
• 10.2.1 Classification
• 10.2.2 Maximum Intensity Projection Function
• 10.2.3 Average Intensity Function
• 10.2.4 Distance to Value Function
• 10.2.5 Isosurface Function
• 10.2.6 Compositing Function
• Transfer functions.
• Integration issues
• Examples
• 10.2.7 Volumetric Shading
• 10.3 Image Order Techniques
• 10.3.1 Sampling and Interpolation Issues
• 10.3.2 Classification and Interpolation Order
• 10.4 Object Order Techniques
• 2D texture methods.
• 3D texture methods.
• 10.5 Volume Rendering vs. Geometric Rendering
• Aims.
• Complexity.
• Mixed methods.
• 10.6 Conclusion
• Figure 10.1
• Figure 10.2
• Figure 10.3
• Figure 10.4
• Figure 10.5
• Figure 10.6
• Figure 10.7
• Figure 10.8
• Figure 10.9
• Figure 10.10
• Figure 10.11
• Figure 10.12
• Figure 10.13
• Figure 10.14
• Figure 10.15
• Figure 10.16
• Chapter 11 Information Visualization
• 11.1 What Is Infovis?
• 11.2 Infovis vs. Scivis: A Technical Comparison
• 11.2.1 Dataset
• 11.2.2 Data Domain
• 11.2.3 Data Attributes
• 11.2.4 Interpolation
• 11.3 Table Visualization
• Printing the contents
• Mapping values
• Sampling issues
• 11.4 Visualization of Relations
• 11.4.1 Tree Visualization
• Node-link visualization
• Rooted tree layout
• Radial tree layout
• Bubble tree layout
• Cone tree layout
• Treemaps
• Squarified treemaps
• Cushion treemaps
• 11.4.2 Graph Visualization
• Hierarchical graph visualization
• Orthogonal layouts
• Hierarchical edge bundling
• Image-based edge bundling
• Force-directed layouts
• Multiple views
• Graph splatting
• General graph-edge bundling
• FDEB
• GBEB
• WR
• SBEB
• KDEEB
• Comparing bundling algorithms
• Visualizing dynamic graphs
• Types of dynamic graphs
• Online vs. offline drawing
• Visualizing small numbers of keyframes
• Using animation
• 11.4.3 Diagram Visualization
• 11.5 Multivariate Data Visualization
• 11.5.1 Parallel Coordinate Plots
• 11.5.2 Dimensionality Reduction
• 11.5.3 Multidimensional Scaling
• 11.5.4 Projection-Based Dimensionality Reduction
• 11.5.5 Advanced Dimensionality Reduction Techniques
• 1. Least Square Projection (LSP)
• 2. Part-Linear Multidimensional Projection (PLMP)
• 3. Local Affine Multidimensional Projection (LAMP)
• Implementations
• 11.5.6 Explaining Projections
• Attribute axes
• Axis legends
• 11.5.7 Assessing Projection Quality
• Aggregate point-wise error
• False neighbors
• Missing neighbors
• Group members
• Comparing projections
• 11.6 Text Visualization
• 11.6.1 Content-Based Visualization
• 11.6.2 Visualizing Program Code
• 11.6.3 Visualizing Evolving Documents
• Analyzing the project structure
• Analyzing activity
• Analyzing growth
• Visualizing quality metrics
• 11.7 Conclusion
• Figure 11.1
• Figure 11.2
• Figure 11.3
• Figure 11.4
• Figure 11.5
• Figure 11.6
• Figure 11.7
• Figure 11.8
• Figure 11.9
• Figure 11.10
• Figure 11.11
• Figure 11.12
• Figure 11.13
• Figure 11.14
• Figure 11.15
• Figure 11.16
• Figure 11.17
• Figure 11.18
• Figure 11.19
• Figure 11.20
• Figure 11.21
• Figure 11.22
• Figure 11.23
• Figure 11.24
• Figure 11.25
• Figure 11.26
• Figure 11.27
• Figure 11.28
• Figure 11.29
• Figure 11.30
• Figure 11.31
• Figure 11.32
• Figure 11.33
• Figure 11.34
• Figure 11.35
• Figure 11.36
• Figure 11.37
• Figure 11.38
• Figure 11.39
• Figure 11.40
• Figure 11.41
• Figure 11.42
• Figure 11.43
• Table 11.1
• Table 11.2
• Listings 11.1
• Chapter 12 Conclusion
• Scientific visualization
• Information visualization
• Synergies and challenges
• The way forward
• Efficiency and effectiveness
• Measuring value
• Integration
• Explorers vs. practitioners
• Specialists vs. generalists
• Appendix Visualization Software
• A.1 Taxonomies of Visualization Systems
• A.2 Scientific Visualization Software
• The Visualization Toolkit (VTK)
• MeVisLab
• AVS/Express
• IRIS Explorer
• SCIRun
• ParaView
• MayaVi
• A.3 Imaging Software
• The Insight Toolkit (ITK)
• 3D Slicer
• Teem
• ImageJ
• Binvox
• OpenVDB
• A.4 Grid Processing Software
• MeshLab
• PCL
• CGAL
• A.5 Information Visualization Software
• The Infovis Toolkit (IVTK)
• Prefuse
• GraphViz
• Tulip
• Gephi
• ManyEyes
• Treemap
• XmdvTool
• Bibliography