\documentclass[a4paper, 11pt]{article}

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\title{Visualisierung WS2011, Eduard Gröller}
\author{Onur Dogangönül}

\begin{document}

\maketitle

\tableofcontents

\part{Intro}
\section{Introduction}
	\subsection{Definition}
		\begin{itemize}
		\item Purpose is insight not numbers\footnote{Hamming, 1962}
		\item Tool to enable a user insight into data.
		\item From mental vision, image, or picture of something more abstract; to make visible to the mind or imagination\footnote{Oxford Engl. Dict. 1989}
		\end{itemize}

	\subsection{Background}
		\begin{itemize}
		\item Old method: Often intuitive graphical illustration 
		\item Scientific discipline since 20 years (1. dedi conf 1990)
		\item Examples
			\begin{itemize}
			\item 1137 China: Geographical map w cartesian coords (grid with longitudinal/latitudinal lines)
			\item Cartography
				\begin{itemize}
				\item Isolines visualize compass deviations
				\item Wind flow visualization
				\end{itemize} 
			\item Military Campaign of Napoleon
			\item Cholera Epidemic Londong: Corr between water supply/disease incidents
			\item Weather maps in meteorology
				\begin{itemize}
				\item Iso-pressure lines
				\item Weather fronts
				\item Maps for pilots
				\end{itemize}
			\item Visualization in medicine: 1895 X-rays, 1896 stereo x-ray image, x-ray tomography
			\item Experimental flow investigation
				\begin{itemize}
				\item Aircraft in wind tunnels
				\item Ship hull in fluid tanks
				\item Smoke particles in wind tunnel
				\item Dye in fluids 
				\end{itemize}
			\item 1770/1782 USA-England Business Graphics: Imports/Expors  
			\item 1750-1785 Sweden Population Development (as function of year and age group)
			\item 1973 Chernoff:  encodes data charac in geometric face features.
			\end{itemize}
		\end{itemize}

		\subsection{Sub Topics}
			\begin{itemize}
				\item Computational sciences comprises
					\begin{itemize}
					\item Scientific Computing: Data acquisition, Data enhancement
					\item Visual Computing: Vis mapping, Quantitative Analysis
						\begin{itemize}
						\item Scientific vis
						\item Computer vision
						\item Human computer interaction
						\end{itemize}
					\end{itemize}
				\item Sub Topics
					\begin{itemize}
						\item Scientific Visualization: Inherent spatial ref
							\begin{itemize}
							\item VolVis: Medical data
							\item FlowVis: Flow data
							\end{itemize}
						\item InfoVis (usually no spatial ref): Abstract data
						\item Gis data
						\item Hist data
						\item Micro(molecular physics)/Macro (astronomy)
						\item Huge data sets
					\end{itemize}
			\end{itemize}

		\subsection{Goals}
			\begin{itemize}
			\item Explore: Nothing known, Vis for data exploration
			\item Analyze
				\begin{itemize}
				\item There are hypotheses (maybe after exploration)
				\item Vis used for verification/falsification
				\end{itemize}
			\item Present
				\begin{itemize}
				\item Everything known about data
				\item Vis used for communication of results
				\end{itemize}
			\end{itemize}

		\subsection{Examples}
			\begin{itemize}
			\item VolVis
				\begin{itemize}
				\item Medical Vis in Surgery Planning
				\item Liver with blood vessels and tumors highlighted
				\end{itemize}
			\item FlowVis: Diesel Particle Filter (DPF) Analysis.
			\item InfoVis: Visualization of search results. 
			\end{itemize}

\section{Visualization Pipeline}
	\begin{itemize}
	\item Data acquisition: data are given
		\begin{itemize}
		\item Measurements, e.g. CT/MRI
		\item Simulation, e.g. flow sim
		\item Modelling, e.g. game theory
		\end{itemize}
	\item Data enhancement: data processed
		\begin{itemize}
		\item Filtering, e.g. smoothing (noise suppr)
		\item Resamplng, e.g. diff-resolution grid
		\item Data derivation, e.g. gradients, curvature
		\item Data interpolation, e.g. linear, cubic
		\end{itemize}
	\item Visualization mapping: map data to e.g. geometry
		\begin{itemize}
		\item Data renderable now
		\item Iso-surface calc
		\item Glyphs, Icons determination
		\item Graph-Layout calc
		\item Voxel attr: color, transparency, ..
		\end{itemize}
	\item Rendering: $3D \rightarrow 2D$ 
		\begin{itemize}
		\item Visibility calc
		\item Illumination
		\item Compositing (combine trans objects, ...)
		\item Animation
		\end{itemize}
	\end{itemize}
		\subsection{Example}
			\begin{itemize}
			\item Simulation data
				\begin{itemize}
				\item Surface splines
				\item x,y,z sampling points (temp pressure)
				\item Irregular in space, time
				\end{itemize}
			\item Derived data
				\begin{itemize}
				\item Polygonal patches (verts at x,y,z)
				\item Data at verts: Temp, pressure
				\item Regular in time
				\end{itemize}
			\end{itemize}

\section{Visualization Scenarios}
	\begin{itemize}
	\item Data, Visualization, Interaction
		\begin{itemize}
		\item Data generation (data acquisition): Measuring, Sim, Modelling
			\begin{itemize}
			\item Can take long
			\item Can be costly
			\end{itemize}
		\item Visualization (rest of pipeline): Data enhancement, vis mapping, rendering
			\begin{itemize}
			\item Depending on hardware
			\item Implementation: fast, slow
			\end{itemize}
		\item Interaction (user feedback):
			\begin{itemize}
			\item Intervening possible?
			\item Parameters?
			\end{itemize}
		\end{itemize} 
	\item Scenarios
		\begin{itemize}
		\item Passive vis (All 3 steps sep)
			\begin{itemize}
				\item Prev gen data visualized (Off-line Vis)
				\item Viewing of vis results 
				\end{itemize} 
		\item Interactive vis (Data gen sep)
			\begin{itemize}
			\item Param of vis tech
			\item Variation
			\item Nowadays widespread
			\end{itemize}
		\item Interactive steering (3 steps coupled)
			\begin{itemize}
			\item On the fly data gen
			\item Interactive vis
			\item User may change sim or vis (high costs, lots of effort)
			\end{itemize}
		\end{itemize}
	\end{itemize}

\section{On Data}
	\begin{itemize}
	\item Focus on data
	\item Driving factor of vis tech
	\item Questions
		\begin{itemize}
		\item Where do the data live? (data space)
		\item Type of data
		\item Which rep makes sense
		\end{itemize}
	\item Which data space?
		\begin{itemize}
		\item Spatial domain (SciVis): medical data, flow sim data, gis data
		\item No spatial ref (InfoVis): abstract data, e.g. from data bases
		\item Aspects: dim, coords, region of influence (local, global)
		\end{itemize}
	\item Which data types (data charac)?
		\begin{itemize}
		\item Scalar, non numerical (nominal, ordinal values)
		\item Multidim values (vectors, tensors, ...)
		\item Multimodal values (arrays of var type)
		\item Aspects: dimensionality, co-domain (range)
		\end{itemize}
	\item Which data rep fits best?
		\begin{itemize}
		\item Inherent spatial domain? Recycle data space or find new one
		\item Which dimension is used for what?
			\begin{itemize}
			\item Relationship data space and data characteristics
			\item Abstract or save dim
			\item Available display space 2d, 3d 
			\item Where is focus
			\end{itemize}
		\end{itemize}
	\item Visualization Examples
		\begin{itemize}
		\item $\mathbb{N} \rightarrow \mathbb{R}$: value series, e.g. bar chart, pie chart, etc.
		\item $\mathbb{R} \rightarrow \mathbb{R}$: function, e.g. (line) graph
		\item $\mathbb{R}^2 \rightarrow \mathbb{R}$: function over $\mathbb{R}^2$, e.g. 2d-height map in 3d, contour lines in 2d, false color map
		\item $\mathbb{N}^2 \rightarrow \mathbb{R}^2$: 2d-vector field, e.g. hedgehog plot, lic, streamlets, etc.
		\item $\mathbb{R}^3 \rightarrow \mathbb{R}$: 3d-densities, e.g. iso-surfaces in 3d, volume rendering
		\item $\mathbb{N} \rightarrow \mathbb{R}^n$ set of tuples, e.g. parallel coords, glyphs, icons, etc.
		\end{itemize}
	\end{itemize}

\section{On Grids}
	\begin{itemize}
	\item Questions
		\begin{itemize}
		\item Which data org optimal?
		\item Where does the data come from?
		\item Neighborhood rel? If yes, how is it stored
		\item Navi within the data possible
		\item Calc with data possible?
		\item Data structured?
		\end{itemize}
	\item Grid types
		\begin{itemize}
		\item Cartesian grid
			\begin{itemize}
			\item Ortho equidist grid
			\item Uniform dists in all dims (dx = dy)
			\item Implicit neighborhood rel
			\end{itemize}
		\item Regular grid
			\begin{itemize}
			\item Ortho equidist grid
			\item Dists not equal over dims
			\item Implicit neighborhood rel
			\end{itemize}
		\item Rectilinear grid
			\begin{itemize}
			\item Orthogonal grid
			\item Var sample dists
			\item Imp neighborhood rel
			\end{itemize}
		\item Curvilinear grid
			\begin{itemize}
			\item Non-ortho grid
			\item Grid-points explicitely given
			\item Imp neighborhood rel
			\end{itemize}
		\item Block-structured gird
			\begin{itemize}
			\item Comb of struct grids
			\item Each block spec sep
			\item Consider interface between blocks
			\item Imp neighborhood rel
			\end{itemize}
		\item Unstructured grid
			\begin{itemize}
			\item Arbitrary points and connec
			\item Points and neighborhood rel explicitly given
			\item Cells
				\begin{itemize}
				\item Tetrahedron (plural tetrahedra, 4 triangular faces)
				\item Hexahedron (plural hexahedra, polyhedron with 6 faces)
				\end{itemize}
			\end{itemize}
		\item Hybrid grid
			\begin{itemize}
			\item Comb of struct and unstruct grids
			\item Subgrids spec sep
			\item Interface between subgrids
			\end{itemize}
		\item Other
			\begin{itemize}
			\item Non-cartcoords
			\item Hierarchical grids
			\item Time-varying grids
			\item Maybe imp but alternative neighborhood rel
			\end{itemize}
		\item Scattered data
			\begin{itemize}
			\item Grid-free data
			\item Withouth neighborhood rel (given by spatial proximty)
			\end{itemize}
		\end{itemize}

	\item Grid transformation
		\begin{itemize}
		\item Conversion
			\begin{itemize}
			\item Physical domain (simulation)
			\item Computational domain (visualization mapping)
			\item Image domain (rendering
			\end{itemize}
		\item Questions
			\begin{itemize}
			\item Accuracy of re-sampling
			\item Design of algorithms
			\item Conversion of Values or Vectors with Jacobi-matrix (Mat of part derivs)
			\end{itemize}
		\end{itemize}

	\end{itemize}

\section{Visualization and Color}
	\begin{itemize}
	\item Color may emphasize info
	\item 5-9 colors, 50-300 shades distinguishable
	\item Color perception depends on context
	\item Consider color associations (warm, cold, etc..)
	\item Mach band effect: eye emphasizes edges
	\item Different color gamuts on different devices (colour correction)
	\item Guidelines
		\begin{itemize}
		\item Desaturated lines as border of colored areas
		\item No saturated blue for details, animations
		\item Not mixing saturated blue and red
		\item Avoid high colour freqs
		\item Colors to compare should be close
		\item Observe context / associations
		\end{itemize}
	\end{itemize}

\part{Volume Visualization}

\section{Introduction}
	\begin{itemize}
	\item Volume data comes from
		\begin{itemize}
		\item Medical Application: Computed Tomographie (CT), Magnetic Resonance Imaging (MR)
		\item Material Testing: Industrial-CT
		\item Simulation: Finite element methods (FEM), Computational fluid dynamics (CFD)
		\end{itemize}
	\item Volume data organization (3d data space)
		\begin{itemize}
		\item Cartesian or regular grid
			\begin{itemize}
			\item CT/MR: often dx =dy $<$ dz, e.g. 135 slices (z) of 512x512 values.
			\item Data enhancement: Iso-stack-calc (int of add slices so dx = dy = dz e.g. 512x512x512)
			\item Data: Cells (Cuboid)
			\item Corner: Voxel
			\end{itemize}
		\item Curvi-linear grid or unstructured
			\begin{itemize}
			\item Data org as tetrahedra or hexahedrea.
			\item Often conv to tetrahedra.
			\end{itemize}
		\end{itemize}
	\item Challenges
		\begin{itemize}
		\item Rendering projection: huge info and few pixels
		\item Large data sizes: 512x512x1024 at 16bit: 512Mbytes
		\item Speed $>$ 10fps for interaction
		\end{itemize}
	\item Voxel vs Cells (Data interpretation)
		\begin{itemize}
		\item Set of Voxel (volume elements): Voxel - point sample in 3d, not necessarily interpolated
		\item Set of cells: 3d cube primitive, 8 corner voxels, interpolation is used for values in cells
		\end{itemize}
	\item Interpolation: NN or Trilinear Interpolation
	\item Gradients as normal vector replacement
		\begin{itemize}
		\item Gradient in $x_0$ is normal vector to iso-surface in $f(x_0)$
		\item Approximate deriv (forward diff, backwards diff, intermediate diff).
		\item Then trilinear interpolate within a cell.
		\end{itemize}
	\item Concepts and Terms
		\begin{itemize}
		\item Sampled data (measurment), analytical data (modelling).
		\item voxel space (discrete) - geometric surfaces (analytic) by iso-surfacing.
		\item geometric surfaces - voxel space by voxelization.
		\item geometric surfaces - pixel space (discrete) by surface rendering.
		\item voxel space (discrete) - pixel space (discrete) by direct volume rendering.
		\end{itemize}
	\item Examples
		\begin{itemize}
		\item CT measurement, iso-stack-conv, iso-surface-calc (marching cubes), surface rendering (OpenGL)
		\item MR measurement, iso-stack-conv, MIP (maximum intensity proj), image: blood-vessels in hand).
		\item Potential function, iso-surface, surface rendering (ray tracing)
		\item X-ray modelling, surface definition, sampling (voxelization), direct volume rendering
		\end{itemize}
	\item Surface vs volume rendering
		\begin{itemize}
		\item Surface rendering
			\begin{itemize}
			\item Indirect volume vis
			\item Intermediate rep: iso-surface
			\item Shading - Shape, hw rendering
			\end{itemize}
		\item Volume rendering
			\begin{itemize}
			\item Direct volume vis
			\item Usage of transfer functions
			\item Pros: illustrate the interior, semi-transparency
			\end{itemize}
		\item Hybrid rendering: surfaces and volumes
		\end{itemize}
	\end{itemize}

\section{Techniques}
	\begin{itemize}
	\item Overview
		\begin{itemize}
		\item Simple methods:
			\begin{itemize}
			\item Slicing
			\item MPR (multi-planar recon)
			\end{itemize} 
		\item Direct volume visualization
			\begin{itemize}
			\item Ray casting
			\item Shear-warp fac
			\item Splatting
			\item 3d tex mapping
			\item Fourier volume rendering
			\end{itemize}
		\item Surface-fitting methods: Marchning cubes (marching tetrahedra)
		\end{itemize}
	\item Image-Order vs Object order
		\begin{itemize}
			\item Image-order
				\begin{itemize}
				\item For every pixel do (sample volume)
				\item Cost/complexity depends on image size
				\item Example: ray casting (trace viewing rays)
				\end{itemize}
			\item Object-order
				\begin{itemize}
				\item For every object do
				\item Traverse the volume and project to the image
				\item Cost/complexity depends on object size (num voxels)
				\item Example: Splatting (throwing snow balls), Texture Mapping (plane by plane)
				\item Splatting either cell-by-cell or plane-by-plane
				\end{itemize}
		\end{itemize}
	\end{itemize}

	\subsection{Simple Methods}
		\begin{itemize}
		\item Slicing
			\begin{itemize}
			\item Simple for regular grids
			\item Without transfer func no color
			\item Not simple for general grid
			\item Interpolation needed
			\item E.g. MPR: Multi-planar reformation (arbit axes, 3d)
			\end{itemize}
		\end{itemize}

	\subsection{Direct Volumne Visualization}
		\begin{itemize}
		\item Overview
			\begin{itemize}
			\item No intermediate rep ("real 3d")
			\item Integration of so huge info difficult
			\item Techniques
				\begin{itemize}
				\item Ray casting
				\item Splatting
				\item Shear-wrap
				\item Texture mapping
				\item Fourier vol rendering
				\end{itemize}
			\item Various types of combinations (Compositing, MIP, first-hit, average, etc.)
			\end{itemize}
		\item Types of combinations
			\begin{itemize}
			\item First hit: Extracts iso-surfaces
			\item Average: Produces basically an X-ray picture
			\item MIP (max intensity): Used for Magnetic Resonance Angiograms (Blood vessels)
			\item Compositing (accumulate): Make transparent layers visible
			\end{itemize}
		\item Classification
			\begin{itemize}
			\item Assign semantics to data (bone, skin, muscle, etc )
			\item Usage of data values, gradient, curvature
			\item Goal is segmentation
			\item Often: semi-automatic or manuel
			\item Automatic approxwith transfer functions (TF)
			\end{itemize}
		\item TFs
			\begin{itemize}
			\item Mapps data to renderable quantities
			\item Map data to color and opacity
			\item Results depend on transfer functions
				\begin{itemize}
				\item Often non-trivial spec
				\item Limited seg possi
				\end{itemize} 
			\item E.g. Lobster with three objects (media, shell, flesh)
			\end{itemize}
		\item Gradient
			\begin{itemize}
			\item Emphasis of changes
			\item Special interest often in transitional areas
			\item Measure degree of change, like surface normal
			\item Larger gradient magnitude - larger opacity
			\end{itemize}
		\item Gradient-Based TFs
			\begin{itemize}
			\item Levoy 1988: 2d tranfer function by 
			\item Specific opacity at certain threshold
			\item Close-by variation according to gradient magnitude
			\item Highlights transitions (large gradients)
			\item Dampens homogeneos areas
			\end{itemize}
		\item Multi-Dimensional Transfer Function
			\begin{itemize}
			\item Histograms of $f, f' and f''$ to epict material boundaries
			\item Showing materials and boundaries
			\end{itemize}
		\item Slices vs Iso Surfaces vs Volume Rendering
			\begin{itemize}
			\item Slices: scalar selection, 2d, color coding
			\item Iso-surface, selection of iso surface, covers 3d
			\item Volume rendering: transfer function dependent, too sparse, too dense.
			\end{itemize}
		\end{itemize}

\section{Volume Rendering}
	\subsection{Optical Models for Volume Rendering}
		\begin{itemize}
		\item Modelling of Natural Phenomena
			\begin{itemize}
			\item Examples
				\begin{itemize}
				\item Emission only (light particles)
				\item Absorption only (dark fog)
				\item Emission and absorption (clouds)
				\item Single scattering, without shadows
				\item Multiple scattering
				\end{itemize}
			\item Two approaches
				\begin{itemize}
				\item Analytical model (via differentials)
				\item Numerical approximation (via differences) 
				\end{itemize}
			\end{itemize}
		\item Emission
			\begin{itemize}
			\item Continuous emission model
				\begin{itemize}
				\item Question: How much light is added along an ifinitely short ray segment in volume
				\item Differential dI/dt = g(t) ... I: intensity, g(t): glow factor
				\item Integration results in: $I(s) = I_0 + \int_{t \in [0,s]} g(t) dt$ 
				\item Overall emission contrib: $G(0,s) = \int_{t \in [0,s]} g(t) dt$
				\item Volume emits light corresponding to thickness.
				\item Unrealistic - because no absorption.
				\end{itemize}
			\item Discrete emission model
				\begin{itemize}
				\item Question: how much light is added within a small, but finite volume extent (C like color)
				\item Accumulation of several extents
				\item $C_i$: contrib ob vol ext i (thickness 1) $$Out_i = In_i + C_i \equiv Out_i = Out_{i-1} + C_i$$ 
				\item $Out_i = In_j + C_j + ... + C_{i-1} + C_i$
				\item Example: pixel val = bg + $\sum_{k\in \mathbb{N}} C(ray(k))$
				\end{itemize}
			\end{itemize}
		\item Absorption
			\begin{itemize}
			\item Absorption, Differential Model
				\begin{itemize}
				\item Question: How much light (in \% of init intensity) remains after traversal of ray seg through the vol
				\item Differential: $dI/dt = -\tau(t)I(t)$
				\item Light I is partially absorbed by $\tau$ e.h. $\tau(t) = 30\%$
				\item $I(s) = I_0 \cdot exp(-\int_{x \in [0,s]} \tau(t) dt$
				\end{itemize}
			\item Absorption, Numerical Approximation: $Out_s = In_0 \cdot \prod_{s \geq k\in \mathbb{N}} (1 - \alpha_k)$
			\end{itemize}
		\item Emission and Absorption
			\begin{itemize}
			\item Continuous model (no scattering)
				\begin{itemize}
				\item At each point emission $g(t)$ and extinction coefficient $\tau(t)$ is given
				\item Emission $g(t)$ is attenuated
				\item Differential $dI/dt = g(t) - \tau(t) I(t)$
				\end{itemize}
			\item Numerical Approximation
				\begin{itemize}
				\item Discrete model (compositing)
				\item For each volume extent we have contribution c, opacity ($\alpha$), transparency ($1 - \alpha$)
				\item $Out_i = In_i (1 - \alpha_i) + C_i \cdot \alpha_i$ (std-compositing)
				\end{itemize}
			\end{itemize}
		\item Scattering
			\begin{itemize}
			\item Particles deviate light at a position
			\item BRDF (bidirectional reflectance distribution function)
			\item Single scattering: too little light in the interior
			\item Single scattering with shadows
			\item Multiple Scattering: Radiosity techniques, very realistic, very costly
			\end{itemize}
		\end{itemize}

	\subsection{Ray Casting/Compositing}
		\begin{itemize}
		\item Ray Tracing vs Ray Casting
			\begin{itemize}
			\item Ray Tracing: ray-object intersection
			\item Ray Casting: no objects, denisty values in 3d
			\item Theory: take all density values into account
			\item Practice: traverse volume step by step
			\item Interpolation necessary for each step
			\end{itemize}
		\item Ray Traversal through Volume Data
			\begin{itemize}
			\item Volume data: $f(x) \in \mathbb{R}, x \in \mathbb{R}^3$
			\item Ray defined as half-line: $r(t) \in \mathbb{R}^3, t \in \mathbb{R} > 0$
			\item Values along ray: $f(r(t)) \in \mathbb{R}, t \in \mathbb{R} > 0$ (intensity profile)
			\end{itemize}
		\item Standard Ray Casting (Levoy 88)
			\begin{itemize}
			\item Overview
				\begin{itemize}
				\item $C(i)$ and $\alpha(i)$ from TF
				\item Ray casting, interpolation
				\item Compositing
				\end{itemize}
			\item Detail
				\begin{itemize}
				\item Shading, Classification
					\begin{itemize}
					\item Shading $f(i) \rightarrow C(i)$: Apply transfer function, diffuse illumnation (Phong), gradient $\approx$ normals
					\item Classification $f(i) \rightarrow \alpha(i)$: Levoy gradient enhanced, Emphasizes transitions
					\end{itemize}
				\item Ray Traversal
					\begin{itemize}
					\item Three approaches: Regular sampling (equidistant samples), Scan conversion (All voxels used one), Voxel intersections (Samples weighted by lengths of ray segments)
					\item Interpolation: Voxel based vs cell based traversal, Tri-linear (interpolation within a cell) vs bilinear (interpolation within a cell face), Unit sampling vs variable sample distances - compositing different
					\end{itemize}
				\item Compositing: F2B vs B2F
					\begin{itemize}
					\item B2F: Use earlier $Out_i$ in formula to create $Out_{i+1}$
					\item F2B: Accumulate 
					\end{itemize}
				\item Acceleration Progressive Refinement: Render pixels inbetween afterwards
				\end{itemize}
			\end{itemize}
		\end{itemize}

	\subsection{Hardware-Volume Visualization}
		\begin{itemize}
		\item Two Approaches
			\begin{itemize}
			\item 3d textures
				\begin{itemize}
				\item Volume data stored in 3d texture
				\item Slices parallel to image plane are interpolated trilinearly
				\item Back to front compositing
				\end{itemize}
			\item 2d textures
				\begin{itemize}
				\item 3 stacks of slices (x, y, z axis)
				\item Select stack (most parallel to image plane)
				\item Back to front compositing
				\end{itemize}
			\end{itemize}
		\item Special Hardware
			\begin{itemize}
			\item Hardware volume raycasting: in vertex and fragment operations of modern graphics cards
			\item VolumePro board: special card for pc, calculates shear-warp factorization, incl compositing, warp-step with regular graphics card (OpenGL)
			\end{itemize}
		\end{itemize}

\section{Surface rendering}
	\subsection{Marching Cubes (MC) - Iso surface display}
		\begin{itemize}
		\item Iso-Surfaces
			\begin{itemize}
			\item Intermediate representation
			\item Aspects
				\begin{itemize}
				\item Preconditions
					\begin{itemize}
					\item Expressive iso-value, iso-value separates materials
					\item Interest: In transitions 
					\end{itemize}
				\item Very selective (binary selection, omission)
				\item Uses traditional hardware
				\item Shading - 3d impression
				\end{itemize}
			\end{itemize}
		\item Volume Data - Iso-Surfaces
			\begin{itemize}
			\item Iso-value $f_0$ separates values $> f_0$ from values $\leqslant f_0$
			\item Often not known - can only be approximated from samples
			\item Shape and position dependet on type of reconstruction
			\end{itemize}
		\item Approximation of Iso-Surface
			\begin{itemize}
			\item Approach: Iso-Surface intersects data volume.. set of all cells
			\item Idea
				\begin{itemize}
				\item Parts of iso-surface represented on cell basis (iso-surface intersects cell)
				\item Usage of triangles - as simple as possible
				\end{itemize}
			\end{itemize}
		\item Marching Cubes
			\begin{itemize}
			\item Cell consists of 8 voxel .. and 4 pixel on screen
				\begin{enumerate}
				\item Classify each vertex as inside or outside 
					\begin{itemize}
					\item Each vertex has a value
					\item Depending on chosen iso value - vertices are outside or inside
					\end{itemize}
				\item Build an index 
					\begin{itemize}
					\item Binary labeling
					\item Vertices are ordered from v1 to v8
					\item e.g. 00110000
					\end{itemize}
				\item Get edge list from table(index)
					\begin{itemize}
					\item For a given index, access an array storing a list of edges 
					\item All 256 cases cab ve derived from 15 base cases
					\end{itemize}
				\item Interpolate the edge location
					\begin{itemize}
					\item For each triangle edge, find the vertex locatation using linear interpolation of the voxel values.
					\item Calculate the normal at each cube vertex
					\item Linear interpolation to compute the polygon vertex normal
					\end{itemize}
				\item Go to next cell
				\end{enumerate}
		\item Ambiguous Cases
			\begin{itemize}
			\item Adjacent vertices different states, diagonal vertices same state
			\item Decide for one case
			\item Danger: holes
			\item Asymptotic Decider
				\begin{itemize}
				\item Assume bilinear interpolation within a face
				\item Hence iso-surface is a hyperbola
				\item Compute point p where the asymptotes of the hyperbola meet
				\item Sign of S(p) decides the connectedness
				\end{itemize}
			\end{itemize}
		\item Summary
			\begin{itemize}
			\item Up to 4 triangles per cube
			\item Can result in several million triangles
			\item Iso-surface does not represent an object
			\item No depth info
			\item Semi-transparent representation - sorting
			\item Optimization
				\begin{itemize}
				\item Reuse intermediate results
				\item Prevent vertex replication
				\item Mesh simplification
				\end{itemize}
			\end{itemize}
		\end{itemize}
	\end{itemize}

\part{Flow Visualization}
\section{Introduction}
	\begin{itemize}
	\item Visualization of change information
	\item Typically more than 3 data dimensions
	\item General overview: even more difficult
	\item User goals: Overview vs details (with context)
	\item Flow data sources
		\begin{itemize}
		\item Flow Simulation
			\begin{itemize}
			\item Airplane-, ship-, car-design
			\item Weather simulation (air-, sea-flows)
			\item Medicine (blood flows)
			\end{itemize}
		\item Flow measurements
			\begin{itemize}
			\item Wind tunnel, fluid tunnel
			\item Schlieren-, shadow-technique
			\end{itemize}
		\item Flow Models: Differential equation systems (ODE, dynamical systems)
		\end{itemize}
	\item Flow Data Specification
		\begin{itemize}
		\item Simulation
			\begin{itemize}
			\item Flow: set of samples (n-dim of data), e.g. on curvilinear grid
			\item Most important primitive: tetrahedron (cell)
			\end{itemize}
		\item Measurement of flow-vectors: reconstruction out of correlations, often calculated on regular grids
		\item Modelling Flow: Analytic formula, can be evaluated everywhere
		\end{itemize}
	\item Simulation vs Measurement vs Modelling
		\begin{itemize}
		\item Simulation
			\begin{itemize}
			\item Flow space modelled with grid
			\item FEM (finite elements method), CfD (computational fluid dynamics)
			\end{itemize}
		\item Measurements: Optical methods and pattern recognition, e.g. PIV (Particle image velocimetry)
		\item Models: Differential equation systems (dx/dt)
		\end{itemize}
	\item 2d vs surfaces vs 3d
		\begin{itemize}
		\item 2d-flow vis
			\begin{itemize}
			\item 2d x 2d flows
			\item Models, slice flows (2d out of 3d)
			\end{itemize}
		\item Surface flows
			\begin{itemize}
			\item 3d-flows around obstacles
			\item Boundary flows on surfaces (2d)
			\end{itemize}
		\item 3d-flow visualization
			\begin{itemize}
			\item 3d x 3d flows
			\item Simulations, 3d-models
			\end{itemize}
		\end{itemize}
	\item Steady vs Time-Dependent flows
		\begin{itemize}
		\item Steady (time-independent) flows
			\begin{itemize}
			\item $v(x): \mathbb{R}^n \rightarrow \mathbb{R}^n$
			\item Flow static over time, e.g. laminar flows (Bewegung von Flüssigkeiten und Gasen)
			\item Simpler interrelationship
			\end{itemize} 
		\item Time-dependent (unsteady) flows
			\begin{itemize}
			\item $v(x,t): \mathbb{R}^n \times \mathbb{R} \rightarrow \mathbb{R}^n$
			\item Flow itself changes over time
			\item More complex interrelationship
			\end{itemize}
		\end{itemize}
	\item Direct vs Indirect Flow Visualization
		\begin{itemize}
		\item Direct flow visualization
			\begin{itemize}
			\item Overview on current flow state
			\item Visualization of vectors
			\item Arrow plots, smearing techniques
			\end{itemize}
		\item Indiret flow visualization
			\begin{itemize}
			\item Usage of intermediate representation: Vector-field integration over time 
			\item Visualization of temporal evolution
			\item Streamlines, streamsurfaces
			\end{itemize}
		\end{itemize}
	\end{itemize}

\section{Experimental Flow Visualization}
	\subsection{Optical Methods}
		\begin{itemize}
		\item White Smoke or Color Injection
			\begin{itemize}
			\item Injection of color or smoke particles
			\item Optical methods: Schlieren, Shadows
			\end{itemize}
		\item PIV (Particle Image Velocimetry)
			\begin{itemize}
			\item Laser and correlation analysis
			\item Real flow, e.g. in wind tunnel
			\item Injection of particles (as uniform as possible)
			\item At interesting locations: 2 times fast illumination with laser-slice
			\item Image capture (high-speed camera), then correlation analysis of particles
			\item Vector calculation, reconstruction, typically only 2d-vectors
			\end{itemize}
		\end{itemize}

	\subsection{Visualization of Models}
		\begin{itemize}
		\item Analytical flow def: $dx / dt = v(x)$
		\item Navier-Stokes equations, e.g. Lorenz-system
		\item Larger variety in data (2d, 3d, nd)
		\item Often sketchy, hand-drawn visualization
		\end{itemize}

	\subsection{Flow Vis with Arrows}
		\begin{itemize}
		\item Aspects
			\begin{itemize}
			\item Direct flowvis
			\item Normalized arrows vs scaling with velocity (also colored)
			\item 2d quite useable,3d often problematic
			\item Sometimes limited expressivity (temporal component missing) but Often used
			\end{itemize}
		\item Arrows in 3d
			\begin{itemize}
			\item Ambiguity
			\item Perspective shortening
			\item Improvement by 3d arrows
			\end{itemize}
		\end{itemize}

\section{Streamlines}
	\subsection{Streamlines Integration}
		\begin{itemize}
		\item Streamlines Theory
			\begin{itemize}
			\item Flow data v: derivative information
			\item $dx/dt = v(x)$
			\item Spatial points $x \in \mathbb{R}^n$, time $t \in \mathbb{R}$, flow vectors $v \in \mathbb{R}^n$
			\item Streamline s: integration over time
			\item Seed point $s_0$, integration var u $$s(t) = s_0 + \int_{0 \leq u \leq t} v(s(u)) du$$
			\item Result s also in the integral: analytical solution usually impossible
			\end{itemize}
		\item Streamlines - Practice
			\begin{itemize}
			\item Numerical integration Idea: Very locally, the solution is linearly approximated
			\item Euler integration: Follow the current flow vector $v(s_i)$ from the current streamline point $s_i$ for a very small time (dt)
			\item $s_{i+1} = s_i + dt \cdot v(s_i)$, integration of small steps
			\item Euler is getting better proportionally to dt, good result for very small dt
			\end{itemize}
		\item Runge-Kutta Approach
			\begin{itemize}
			\item Better than euler integration
			\item Idea: Evaluate flow vector inbetween
			\item Do half a euler step, evaluate flow vector there, use it in the origin 
			\item Second order runge-kutta: $s_{i+1} = s_i + v(s_i + v(s_i) \cdot dt / 2) \cdot dt$
			\item Forth order even better: pays off only with complex flows
			\end{itemize}
		\end{itemize}

	\subsection{Visualization with Streamlines}
		\begin{itemize}
		\item Streamlines in 2d: Adequate for overview
		\item Visualization with particles
			\begin{itemize}
			\item Particle paths = streamlines (steady flows)
			\item Variants (time-dependent data): streak lines (steadily new particles), path lines (long-term path of one particle) 
			\end{itemize}
		\item Streamlines in 3d
			\begin{itemize}
			\item Color coding speed
			\item Selective Placement
			\item Sweeps: better spatial 3d perception
			\end{itemize}
		\end{itemize}
	\subsection{Streamline Placement in 2d}
		\begin{itemize}
		\item Problems: Choice of Seed Points
			\begin{itemize}
			\item If regular grid: Very irregular result
			\item Idea: streamlines should not get too near to each other
			\item Approach: Choose a seed point with distance $d_{sep}$ from an already existing streamline
			\item Forward and backward integration until distance $d_{test}$ is reached
			\item Two parameters: $d_{sep}$ ... start distance, $d_{test}$ ... minimum distance
			\end{itemize}
		\item Algorithm:
			\begin{itemize}
			\item Compute initial streamline, put into queue
			\item Try get new seed point to compute streamline
			\item If succesful it is put into queue etc
			\item Stop streamline integration
				\begin{itemize}
				\item When dist to neighboring streamline gets to close
				\item When streamline leaves flow domain
				\item Runs into fixed point
				\item Gets to near to itself
				\item After a certain amount of maximal steps
				\end{itemize}
			\end{itemize}
		\item Tapering and Glyphs
			\begin{itemize}
			\item Thickness in rel to dist
			\item Directional glyphs
			\end{itemize}
		\end{itemize}

\section{Integral Objects}
	\begin{itemize}
	\item Integral Objects in 3d
		\begin{itemize}
		\item Streamribbon (Bänder) Generation
			\begin{itemize}
			\item Start with two points in 3d with particular distance $$(x_i - y_i)^2 = d^2$$
			\item Loop: Do an integration step from $x_i$ to yield $x_{i+1}$ 
			\item Do an integration step from $y_i$ to yield z, renormalize the distance between $x_{i+1}$ and z to d.
			\item $y_{i+1} = x_{i+1} + d \cdot (z - x_{i+1})/ |z - x_{i+1}|$
			\item End streamribbon integration if wanted
			\end{itemize}
		\item Streamsurfaces - split, merge
		\item Stream Arrows
		\item Flow volumes vs Streamtubes (similar to streamribbon)
		\end{itemize}
	\end{itemize}

\section{Line Integral Convolution (LIC)}
	\begin{itemize}
	\item Flow Vis in 2d or on surfaces
	\item Aspects
		\begin{itemize}
		\item Goal: general overview of flow
		\item Approach: Usage of textures
		\item Idea: flow - visual correlation
		\end{itemize}
	\item Approach
		\begin{itemize}
		\item For every texel: let the texture value either correlate with neighboring texture values in flow direction or not correlate normal to flow direction.
		\item Result: Along streamlines the texture values are correlated - visually coherent
		\item Approach: smudge white noise along flow
		\end{itemize}
	\item Steps
		\begin{itemize}
		\item Look at streamline through point
		\item Filter white noise along streamline
		\item Is a convolution of white noise and a smoothing filter
		\end{itemize}
	\item Arrows vs Streamlines vs Textures: Streamlines and Arrows are selective, Textures 2d filling
	\item LIC in 3d
		\begin{itemize}
		\item Problem of rendering: Destruction of correlational information
		\item Hence: Selective use
		\end{itemize}
	\item LIC-Variants
		\begin{itemize}
		\item OLIC - Oriented Line Integral Convolution (Vis of directional info)
		\item Texture Advection (Steady flows, Unsteady flows)
		\end{itemize}
	\item Similar Alternatives to LIC
		\begin{itemize}
		\item Spot noise
		\item Vector kernel
		\item Line bundles, splats
		\item Textured splats
		\item Particle systems
		\item Flow volumes
		\end{itemize}
	\end{itemize}

\section{Flow Vis dependent on Local properties}
	\begin{itemize}
	\item Glyphs and Icons
		\begin{itemize}
		\item Local and topological properties
		\item Icons in 2d
		\item Icons and Glyphs in 3d
		\end{itemize}
	\item Flow topology
		\begin{itemize}
		\item Topology: abstract structure of a flow
		\item Different elements, e.g. Checkpoints (v(x) = 0), cycles (s(t + T) = s(t)), connecting structures (separatrices)
		\end{itemize}
	\item Timesurfaces
		\begin{itemize}
		\item Start surface, e.g. part of a plane
		\item Move whole surface along flow over time
		\item Time surface: surface at one point in time
		\end{itemize}
	\end{itemize}

\part{Outro}
\section{Knowledge Assisted Visualization}
	\begin{itemize}
	\item Limits of my language mean the limits of my world\footnote{Wittgenstein}
	\item Is visualization using the right language
	\end{itemize}

	\subsection{Illustrative Visualization}
		\begin{itemize}
		\item Illustration
			\begin{itemize}
			\item Illustration: Picture with a communicative intend
			\item Presents complex structures or procedures in an easily understandable way
			\item Uses abstraction to prevent visual overload
			\item Abstraction is visualized through distinct stylistic choices
			\end{itemize}
		\item Abstraction
			\begin{itemize}
			\item Fundamental for creating an expressive illustration
			\item Introduces a distortion between visualization and underlying model
			\item Diff degrees of abstraction at difft levels
			\item Task of an illustrator: Find necessary abstractions for the intend of the illustration
			\end{itemize}
		\item Challenges
			\begin{itemize}
			\item Smart visibility
			\item Smart interaction
			\item Smart transformation, deformation
			\item Automatic transfer of styles
			\item Novel application areas
			\end{itemize}
		\item Examples
			\begin{itemize}
			\item Importance-driven rendering
			\item Exploded Views
			\item Style Transfer functions
			\item Illustrative rendering of seismic data
			\end{itemize}
		\end{itemize}
	
	\subsection{Knowledge Assisted Visualization (KAV)}
		\begin{itemize}
		\item About
			\begin{itemize}
			\item Utilize knowledge and information derived from process of scientific vis or from abstract data analysis
			\item Computational space and the perceptual/cognitive space.
			\item When knowledge-based system added - knowledge-assisted vis
			\end{itemize}
		\item Challenges
			\begin{itemize}
			\item Metadata vis
			\item Vis enabled by
				\begin{itemize}
				\item Topological information of data
				\item Statistical information of data
				\item Geometric information of data
				\item Semantic information of data
				\end{itemize}
			\item Visualization via learning
			\item Visualization via shared knowledge in collaborative setting
			\item Knowledge representation for vis
			\end{itemize}
		\item Examples
			\begin{itemize}
			\item Viewpoint mutual information
			\item Pre-determined ranking of vis designs
			\item Workflow management (VisTrails)
			\end{itemize}
		\end{itemize}
	
	\subsection{KAV Examples}
		\begin{itemize}
		\item Importance-Driven Focus of Attention: Guided navigation between characteristic views
		\item Visualization with Style
			\begin{itemize}
			\item Volume and transfer function = volume rendering
			\item Semantic layers and style transfer functions (STF) = visualization with style
			\item STF Motivation
				\begin{itemize}
				\item Generate scientific illustrations directly from volume data
				\item Provide illustrator with the possibility to quickly modify rendering styles
				\item Ability to extract styles from existing works of art
				\end{itemize}
			\item Lit Sphere Maps
				\begin{itemize}
				\item Sphere map indexed by the eye-space normal to determine the color of a point
				\item Easy to obtain: Lighting studies are frequently performed using spheres
				\item Sloan et al describe simple extraction process from existing works of art
				\item Intuitive representation, can be directly displayed to the user as a preview
				\end{itemize}
			\item Style Transfer Functions
				\begin{itemize}
				\item Use lit sphere maps to allow data-dependent illustrative shading for volume rendering
				\item One lit sphere map represents one specific rendering style
				\item Transfer function is defined over styles instead of colors
				\item Combines the power of data-dependent lighting with the flexibility of lit sphere maps
				\item Replace color nodes in transfer function by 2d lit sphere maps
				\item Essentially a 3d transfer function of data value and eye-space normal: $stf(s, n_x, n_y)$ 
				\item Prohibitive storage requirements: Split up into two functions $sf(tf(s))(n_x, n_y)$
				\item Linear blending between styles: Complex transitions possible through intermediate styles
				\item Style Contours
					\begin{itemize}
					\item Contours are a frequent stylistic element in illustrations
					\item Contour appearance should be derived from lit sphere map
					\item Apparent contour thickness varies based on curvature
					\item Kindlmann et al: Use normal curvature along the view direction to modulate contour threshold
					\end{itemize}
				\item Illustrative Transparency
					\begin{itemize}
					\item Transparency in illustrations puts emphasis on edges
					\item Use of contourness to simulate this technique
					\end{itemize}
				\item Implementation and Results
					\begin{itemize}
					\item Easy integration into existing GPU-based ray casting algorithms
					\item Performance between 80 and 100 percent of normal transfer function and phong shading
					\item Style transfer function lookups require three textures but additional memory requirement small
					\item Additional texture fetches incur an overhead, but shading computations are simplified
					\item Can completely replace conventional lighting computations
					\end{itemize}
				\item Style Transfer Functions and Semantic Layers
					\begin{itemize}
					\item Different styles: Tissue texture, Specular highlights, Contours 
					\item Complex rules for illustrations
					\item Mapping from expert domain to visual appearance (TF maps densities to colors and opacities, Semantic layers map features and attribs to styles with parameters)
					\end{itemize}
				\item Semantic Layers for Illustrative Volume Rendering
					\begin{itemize}
					\item Mapping volumetric attributes to visual styles
					\item Use natural language of domain expert (rules)
					\item Rules evaluated with fuzzy logic arithmetics
					\item Gibt bestimmte semantics: density, curvature etc..
					\item Wenn curvature negative und density hoch und gradient hoch und distance zum user focus niedrig dann contour style is red
					\end{itemize}
				\end{itemize}
			\item LiveSync: Knowledge-Based Navigation
				\begin{itemize}
				\item Slice view - 3d view
				\item Underconstrained problem: viewpoint, clipping plane, zoom
				\item Viewing Sphere
					\begin{itemize}
					\item Concept: Surrounds object, viewpoints on surface of viewing sphere, viewing direction to sphere center
					\item Encoding of viewpoint quali: Deformation of viewing sphere  (high radial distance indicates good viewpoint)
					\end{itemize}
				\item Interaction with 2d slices
				\item Automatic generation of expressive 3d views
				\end{itemize}
			\end{itemize}
		\end{itemize}
	
\section{Visualisierung - Aktuelle Themen und Trends}
	\subsection{Challenges}
		\begin{itemize}
		\item Scientific Visualization - Information Visualization
			\begin{itemize}
			\item Data resulting from Computational fluid dynamics simulation (CFD):
				\begin{itemize}
				\item Grid-based geometry
				\item Scalar and vector data per grid element (cell or vertex)
				\item Time-dependent results
				\item Time-varying grid geometries
				\end{itemize}
			\item Data characteristics
				\begin{itemize}
				\item Multi-dimensional data
				\item Large data sets (num cells * num timesteps * num dim)
				\item Data ranges differ by many magnitudes
				\end{itemize}
			\item Motivation
				\begin{itemize}
				\item Large data sets from simulation
				\item Goal: support exploration and analysis of results
					\begin{itemize}
					\item Analyze ndim data interactively
					\item Use 3d visualization
					\item Overview, zoom and filter, detail on demand
					\end{itemize}
				\item Challenge: Occlusion, Interactive data handling (very big data)
				\end{itemize}
			\item SimVis (Interactive Visual Analysis of Large and Complex Simulation Data)
				\begin{itemize}
				\item VRVis solution for these challenges
				\item Feature-based visualization framework
				\item Key-features
					\begin{itemize}
					\item Multiple, linked views (Scatterplots, Hists, 3d(4d) view, etc..
					\item Interactive feature specification
					\item Focus and context visualization
					\item Smooth feature boundaries
					\item Explicit feature representation
					\item On-the-fly attribute derivation
					\end{itemize}
				\end{itemize}
			\end{itemize}
		\item New Data Sources - Novel Imaging Modalities
			\begin{itemize}
			\item Challenges
				\begin{itemize}
				\item Very large abstract data sets
				\item High-dimensional, multi-valued, multi-modal, heterogeneous
				\item Time varying
					\begin{itemize}
					\item Spatially sparse or dense, temporally sparse or dense
					\item Need for registration
					\item Need for feature extraction
					\end{itemize}
				\end{itemize}
			\item Examples: Web 2.0, Dual energy CT
			\item New Data Sources - Web 2.0
				\begin{itemize}
				\item MySpace
				\item LinkedIn
				\item Flickr
				\item Youtube
				\end{itemize}
			\item Novel Imaging Modalities - Dual energy CT
				\begin{itemize}
				\item Micro CT - Industrial CT
				\item Two X-ray sources
				\item Metrology and dimensional measurement
				\item Multi-materials
				\item Resolution: 508 x 523 x 61
				\item Voxelsize ($\mu m$) 200
				\item Data have complimentary strenghts and weaknesses
				\end{itemize}
			\end{itemize}
		\item Challenge Visual Analytics - Visual Computing - Knowledge Assisted Visualization
			\begin{itemize}
			\item Visual Analytics
				\begin{itemize}
				\item Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces
				\item What do we have: Automatic knowledge disconvery and information mining. Interactive visual data-exploration
				\item What do we need: Tight integration of visual and automatic data analysis methods with database technology for a scalable interactive decision support
				\item Event tunnel: complex event stream visualization for business process pattern analysis
				\item Event tunnel several patterns...
				\end{itemize}
			\item Visual Computing (SciVis, Computer vision, Human computer interaction)
				\begin{itemize}
				\item Sonar explorer, 4d sonar data, Cones with resolution: 25 x 20 x 1319
				\item Ping rate 1Hz, 2 GB/ping
				\item Time steps overlapping
				\item Highly anisotropic, Noisy
				\item Singal strength redued with spreading an absorption
				\item Data reduction in pipeline, visual analysis and selection, survey map
				\end{itemize}
			\item KAV
				\begin{itemize}
				\item Utilize knowledge and information derived from the process of scientific visualization or from abstract data analysis 
				\item Challenges
					\begin{itemize}
					\item Metadata visualization 
					\item Visualization enabled by (topological info, statistical info, geometric info and semantic info of the data)
					\item Visualization via learning
					\item Visualization via shared knowledge in a collaborative setting
					\item Knowledge representation for visualization
					\end{itemize}
				\item Example: Automatic view point selection, Automatic reporting
				\end{itemize}
			\end{itemize}
			\item Challenge Scalability
				\begin{itemize}
				\item Challenges
					\begin{itemize}
					\item Amount of data and dimensionality
					\item Numbers of data sources and heterogeneity
					\item Data quality and data resolution
					\item Dynamicity and novelty
					\item Data representation and visual resolution
					\end{itemize}
				\item Examples: Focus and Context, Aggregation, Abstraction and Illustration
				\item Scalability - Focus and Context Principle
					\begin{itemize}
					\item Important regions in great detail (focus)
					\item Global view with reduced detail (context)
					\item Dynamic integration
					\item Human vision (fovea and retina)
					\end{itemize}
				\item Scalability - Process Visualization
					\begin{itemize}
					\item Improving singular instruments (History encoding, Multi-instruments, LOD)
					\item Improving the monitoring system (Focus and Context rendering, Collision avoidance)
					\item Various instruments can be used to construct LODs
					\end{itemize}
				\item Scalability - Illustration
					\begin{itemize}
					\item Illustration - picutre with communicative intent
					\item Complex structures or procedures in easily understandable way
					\item Uses abstraction to prevent visual overload - allows to focus on essential parts
					\item Abstraction through distinct stylistic choices
					\item As detailed as necessary - as simple as possible
					\item Different degrees of abstraction introduced at different levels
					\item Task of the illustrator: find necessary abstraction for the intent of the illustration
					\item Examples (Illustration): Hierarchical Edge Bundles, Illustrative Parallel Coordinates
					\item Examples (Smart visibility): Importance-driven feature enhancement (Importance spec, Importance compo, Level of sparseness)
					\item Do not fight complexity with complexity
					\end{itemize}
				\end{itemize}
			\item Challenges Visualization Yes, Interaction No?
				\begin{itemize}
				\item Problems
					\begin{itemize}
					\item Interaction is very time-consuming
					\item Prevents comparisons
					\item Hampers reporting
					\end{itemize}
				\item Challenges
					\begin{itemize}
					\item Provide standardized views
					\item Algorithms highly parameterized - provide sensible default settings
					\item Support automatic parameter tuning
					\item Provide navigational aids
					\end{itemize}
				\item Examples
					\begin{itemize}
					\item Automatic view point selection
					\item Focus of attention
					\item Automatic light placement (inconsistent lighting)
					\item Automatic reporting
					\item Dynamic poster - automatic storytelling
					\end{itemize}
				\item Provide the user with the flexibility he or she can cope with
				\item Interaction Yes - but user centric
					\begin{itemize}
					\item Problems: doctors dont know transfer functions. 3d interaction is complex
					\item Challenges: user model (novice, experienced, expert), motifs, user preferences, 2d and navigation
					\item Examples: Semantic layers for illustrative volume rendering. Knowledge based navigation
					\end{itemize}
				\end{itemize}
			\end{itemize}

\end{document}
