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Lecture 14: Scientific Visualization Information Visualization CPSC 533C, Fall 2006

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News. Reminder: no class next week. I'm at InfoVis/Vis in Baltimore. Overview. What is SciVis? ... Jean M. Favre. Glyphs (arrows) Techniques. Techniques ... – PowerPoint PPT presentation

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Title: Lecture 14: Scientific Visualization Information Visualization CPSC 533C, Fall 2006


1
Lecture 14 Scientific VisualizationInformation
VisualizationCPSC 533C, Fall 2006
  • Tamara Munzner
  • UBC Computer Science
  • 26 Oct 2006

2
Credits
  • almost unchanged from lecture by Melanie Tory
    (University of Victoria)
  • who in turn used resources from
  • Torsten Möller (Simon Fraser University)
  • Raghu Machiraju (Ohio State University)
  • Klaus Mueller (SUNY Stony Brook)

3
News
  • Reminder no class next week
  • I'm at InfoVis/Vis in Baltimore

4
Overview
  • What is SciVis?
  • Data Applications
  • Iso-surfaces
  • Direct Volume Rendering
  • Vector Visualization
  • Challenges

5
Difference between SciVis and InfoVis
Parallel Coordinates
Direct Volume Rendering
Hauser et al.,Vis 2000
Fua et al., Vis 1999
Isosurfaces
Glyphs
Scatter Plots
Line Integral Convolution
http//www.axon.com/gn_Acuity.html
Node-link Diagrams
Cabral Leedom,SIGGRAPH 1993
Streamlines
Lamping et al., CHI 1995
Verma et al.,Vis 2000
6
Difference between SciVis and InfoVis
  • Card, Mackinlay, Shneiderman
  • SciVis Scientific, physically based
  • InfoVis Abstract
  • Munzner
  • SciVis Spatial layout given
  • InfoVis Spatial layout chosen
  • Tory Möller
  • SciVis Spatial layout given Continuous
  • InfoVis Spatial layout chosen Discrete
  • Everything else -- ?

7
Overview
  • What is SciVis?
  • Data Applications
  • Iso-surfaces
  • Direct Volume Rendering
  • Vector Visualization
  • Challenges

8
Medical Scanning
  • MRI, CT, SPECT, PET, ultrasound

9
Medical Scanning - Applications
  • Medical education for anatomy, surgery, etc.
  • Illustration of medical procedures to the patient

10
Medical Scanning - Applications
  • Surgical simulation for treatment planning
  • Tele-medicine
  • Inter-operative visualization in brain surgery,
    biopsies, etc.

11
Biological Scanning
  • Scanners Biological scanners, electronic
    microscopes, confocal microscopes
  • Apps physiology, paleontology, microscopic
    analysis

12
Industrial Scanning
  • Planning (e.g., log scanning)
  • Quality control
  • Security (e.g. airport scanners)

13
Scientific Computation - Domain
  • Mathematical analysis
  • ODE/PDE (ordinary and partialdifferential
    equations)
  • Finite element analysis (FE)
  • Supercomputer simulations

14
Scientific Computation - Apps
  • Flow Visualization

15
Overview
  • What is SciVis?
  • Data Applications
  • Iso-surfaces
  • Direct Volume Rendering
  • Vector Visualization
  • Challenges

16
Isosurfaces - Examples
Isolines
Isosurfaces
17
Isosurface Extraction
0
1
1
3
2
  • by contouring
  • closed contours
  • continuous
  • determined by iso-value
  • several methods
  • marching cubes is most common

1
3
6
6
3
3
7
9
7
3
2
7
8
6
2
1
2
3
4
3
Iso-value 5
18
MC 1 Create a Cube
  • Consider a Cube defined by eight data values

(i,j1,k1)
(i1,j1,k1)
(i,j,k1)
(i1,j,k1)
(i,j1,k)
(i1,j1,k)
(i,j,k)
(i1,j,k)
19
MC 2 Classify Each Voxel
  • Classify each voxel according to whether it
    liesoutside the surface (value gt iso-surface
    value)inside the surface (value lt iso-surface
    value)

10
10
Iso9
5
5
10
8
Iso7
8
8
inside
outside
20
MC 3 Build An Index
  • Use the binary labeling of each voxel to create
    an index

v8
v7
11110100
inside 1
v4
outside0
v3
v5
v6
00110000
v1
v2
Index
v1
v2
v3
v4
v5
v6
v7
v8
21
MC 4 Lookup Edge List
  • For a given index, access an array storing a list
    of edges
  • all 256 cases can be derived from 15 base cases

22
MC 4 Example
  • Index 00000001
  • triangle 1 a, b, c

c
a
b
23
MC 5 Interp. Triangle Vertex
  • For each triangle edge, find the vertex location
    along the edge using linear interpolation of the
    voxel values

i1
i
x
10
0
T8
T5
24
MC 6 Compute Normals
  • Calculate the normal at each cube vertex
  • Use linear interpolation to compute the polygon
    vertex normal

25
MC 7 Render!
26
Overview
  • What is SciVis?
  • Data Applications
  • Iso-surfaces
  • Direct Volume Rendering
  • Vector Visualization
  • Challenges

27
Direct Volume Rendering Examples
28
Rendering Pipeline (RP)
Classify
29
Classification
  • original data set has application specific values
    (temperature, velocity, proton density, etc.)
  • assign these to color/opacity values to make
    sense of data
  • achieved through transfer functions

30
Transfer Functions (TFs)
RGB
a
  • Simple (usual) case Map data value f to color
    and opacity

f
Gordon Kindlmann
31
TFs
  • Setting transfer functions is difficult,
    unintuitive, and slow

a
a
f
f
a
a
f
f
Gordon Kindlmann
32
Transfer Function Challenges
  • Better interfaces
  • Make space of TFs less confusing
  • Remove excess flexibility
  • Provide guidance
  • Automatic / semi-automatic transfer function
    generation
  • Typically highlight boundaries

Gordon Kindlmann
33
Rendering Pipeline (RP)
Classify
Shade
34
Light Effects
  • Usually only considering reflected part

Light
reflected
specular
Light
absorbed
ambient
diffuse
transmitted
Lightrefl.absorbedtrans.
Lightambientdiffusespecular
35
Rendering Pipeline (RP)
Classify
Shade
Interpolate
36
Interpolation
2D
  • Given

37
Interpolation
  • Very important regardless of algorithm
  • Expensive gt done very often for one image
  • Requirements for good reconstruction
  • performance
  • stability of the numerical algorithm
  • accuracy

Linear
Nearest neighbor
38
Rendering Pipeline (RP)
Classify
Shade
Interpolate
Composite
39
Ray Traversal Schemes
Intensity
Max
Average
Accumulate
First
Depth
40
Ray Traversal - First
Intensity
First
Depth
  • First extracts iso-surfaces (again!)done by
    TuyTuy 84

41
Ray Traversal - Average
Intensity
Average
Depth
  • Average produces basically an X-ray picture

42
Ray Traversal - MIP
Intensity
Max
Depth
  • Max Maximum Intensity Projectionused for
    Magnetic Resonance Angiogram

43
Ray Traversal - Accumulate
Intensity
Accumulate
Depth
  • Accumulate make transparent layers
    visible!Levoy 88

44
Volumetric Ray Integration
color
opacity
object (color, opacity)
45
Overview
  • What is SciVis?
  • Data Applications
  • Iso-surfaces
  • Direct Volume Rendering
  • Vector Visualization
  • Challenges

46
Flow Visualization
  • Traditionally Experimental Flow Vis
  • Now Computational Simulation
  • Typical Applications
  • Study physics of fluid flow
  • Design aerodynamic objects

47
Traditional Flow Experiments
48
Techniques
Glyphs (arrows)
Contours
Streamlines
Jean M. Favre
49
Techniques
50
Techniques - Stream-ribbon
  • Trace one streamline and a constant size vector
    with it
  • Allows you to see places where flow twists

51
Techniques - Stream-tube
  • Generate a stream-line and widen it to a tube
  • Width can encode another variable

52
Mappings - Flow Volumes
  • Instead of tracing a line - trace a small
    polyhedron

53
LIC (Line Integral Convolution)
  • Integrate noise texture along a streamline

H.W. Shen
54
Overview
  • What is SciVis?
  • Data Applications
  • Iso-surfaces
  • Direct Volume Rendering
  • Vector Visualization
  • Challenges

55
Challenges - Accuracy
  • Need metrics -gt perceptual metric

56
Challenges - Accuracy
  • Deal with unreliable data (noise, ultrasound)

57
Challenges - Accuracy
  • Irregular data sets

Structured Grids
regular
rectilinear
uniform
curvilinear
Unstructured Grids
regular
irregular
hybrid
curved
58
Challenges - Speed/Size
  • Efficient algorithms
  • Hardware developments (VolumePro)
  • Utilize current hardware (nVidia, ATI)
  • Compression schemes
  • Terabyte data sets

59
Challenges - HCI
  • Need better interfaces
  • Which method is best?

60
Challenges - HCI
  • Augmented reality
  • Explore novel I/O devices
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