Lecture 14 scientific visualization information visualization cpsc 533c fall 2006
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Lecture 14: Scientific Visualization Information Visualization CPSC 533C, Fall 2006 Tamara Munzner UBC Computer Science 26 Oct 2006 Credits almost unchanged from lecture by Melanie Tory (University of Victoria) who in turn used resources from Torsten Möller (Simon Fraser University)

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Lecture 14 scientific visualization information visualization cpsc 533c fall 2006 l.jpg

Lecture 14: Scientific VisualizationInformation VisualizationCPSC 533C, Fall 2006

Tamara Munzner

UBC Computer Science

26 Oct 2006


Credits l.jpg
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)


Slide3 l.jpg
News

  • Reminder: no class next week

    • I'm at InfoVis/Vis in Baltimore


Overview l.jpg
Overview

  • What is SciVis?

  • Data & Applications

  • Iso-surfaces

  • Direct Volume Rendering

  • Vector Visualization

  • Challenges


Difference between scivis and infovis l.jpg

SciVis

InfoVis

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]


Difference between scivis and infovis6 l.jpg
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 -- ?


Overview7 l.jpg
Overview

  • What is SciVis?

  • Data & Applications

  • Iso-surfaces

  • Direct Volume Rendering

  • Vector Visualization

  • Challenges


Medical scanning l.jpg
Medical Scanning

  • MRI, CT, SPECT, PET, ultrasound


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Medical Scanning - Applications

  • Medical education for anatomy, surgery, etc.

  • Illustration of medical procedures to the patient


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Medical Scanning - Applications

  • Surgical simulation for treatment planning

  • Tele-medicine

  • Inter-operative visualization in brain surgery, biopsies, etc.


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Biological Scanning

  • Scanners: Biological scanners, electronic microscopes, confocal microscopes

  • Apps – physiology, paleontology, microscopic analysis…


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Industrial Scanning

  • Planning (e.g., log scanning)

  • Quality control

  • Security (e.g. airport scanners)


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Scientific Computation - Domain

  • Mathematical analysis

  • ODE/PDE (ordinary and partialdifferential equations)

  • Finite element analysis (FE)

  • Supercomputer simulations


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Scientific Computation - Apps

  • Flow Visualization


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Overview

  • What is SciVis?

  • Data & Applications

  • Iso-surfaces

  • Direct Volume Rendering

  • Vector Visualization

  • Challenges


Isosurfaces examples l.jpg
Isosurfaces - Examples

Isolines

Isosurfaces


Isosurface extraction l.jpg
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


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MC 1: Create a Cube

  • Consider a Cube defined by eight data values:

(i,j+1,k+1)

(i+1,j+1,k+1)

(i,j,k+1)

(i+1,j,k+1)

(i,j+1,k)

(i+1,j+1,k)

(i,j,k)

(i+1,j,k)


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MC 2: Classify Each Voxel

  • Classify each voxel according to whether it liesoutside the surface (value > iso-surface value)inside the surface (value <= iso-surface value)

10

10

Iso=9

5

5

10

8

Iso=7

8

8

=inside

=outside


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MC 3: Build An Index

  • Use the binary labeling of each voxel to create an index

v8

v7

11110100

inside =1

v4

outside=0

v3

v5

v6

00110000

v1

v2

Index:

v3

v5

v6

v1

v2

v4

v7

v8


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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


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MC 4: Example

  • Index = 00000001

  • triangle 1 = a, b, c

c

a

b


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MC 5: Interp. Triangle Vertex

  • For each triangle edge, find the vertex location along the edge using linear interpolation of the voxel values

i+1

i

x

=10

=0

T=8

T=5


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MC 6: Compute Normals

  • Calculate the normal at each cube vertex

  • Use linear interpolation to compute the polygon vertex normal



Overview26 l.jpg
Overview

  • What is SciVis?

  • Data & Applications

  • Iso-surfaces

  • Direct Volume Rendering

  • Vector Visualization

  • Challenges




Classification l.jpg
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


Transfer functions tf s l.jpg

a(f)

RGB(f)

Shading,

Compositing…

Human Tooth CT

Transfer Functions (TF’s)

RGB

a

  • Simple (usual) case: Map data value f to color and opacity

f

Gordon Kindlmann


Slide31 l.jpg
TF’s

  • Setting transfer functions is difficult, unintuitive, and slow

a

a

f

f

a

a

f

f

Gordon Kindlmann


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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


Rendering pipeline rp33 l.jpg
Rendering Pipeline (RP)

Classify

Shade


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Light Effects

  • Usually only considering reflected part

Light

reflected

specular

Light

absorbed

ambient

diffuse

transmitted

Light=refl.+absorbed+trans.

Light=ambient+diffuse+specular


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Rendering Pipeline (RP)

Classify

Shade

Interpolate


Interpolation l.jpg

1D

  • Given:

  • Needed:

  • Needed:

Interpolation

  • Given:

2D


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Interpolation

  • Very important; regardless of algorithm

  • Expensive => done very often for one image

  • Requirements for good reconstruction

    • performance

    • stability of the numerical algorithm

    • accuracy

Linear

Nearest

neighbor


Rendering pipeline rp38 l.jpg
Rendering Pipeline (RP)

Classify

Shade

Interpolate

Composite


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Ray Traversal Schemes

Intensity

Max

Average

Accumulate

First

Depth


Ray traversal first l.jpg
Ray Traversal - First

  • First: extracts iso-surfaces (again!)done by Tuy&Tuy ’84

Intensity

First

Depth


Ray traversal average l.jpg
Ray Traversal - Average

  • Average: produces basically an X-ray picture

Intensity

Average

Depth


Ray traversal mip l.jpg
Ray Traversal - MIP

  • Max: Maximum Intensity Projectionused for Magnetic Resonance Angiogram

Intensity

Max

Depth


Ray traversal accumulate l.jpg
Ray Traversal - Accumulate

  • Accumulate: make transparent layers visible!Levoy ‘88

Intensity

Accumulate

Depth


Volumetric ray integration l.jpg

1.0

Volumetric Ray Integration

color

opacity

object (color, opacity)


Overview45 l.jpg
Overview

  • What is SciVis?

  • Data & Applications

  • Iso-surfaces

  • Direct Volume Rendering

  • Vector Visualization

  • Challenges


Flow visualization l.jpg
Flow Visualization

  • Traditionally – Experimental Flow Vis

  • Now – Computational Simulation

  • Typical Applications:

    • Study physics of fluid flow

    • Design aerodynamic objects



Techniques l.jpg
Techniques

Glyphs (arrows)

Contours

Jean M. Favre

Streamlines



Techniques stream ribbon l.jpg
Techniques - Stream-ribbon

  • Trace one streamline and a constant size vector with it

  • Allows you to see places where flow twists


Techniques stream tube l.jpg
Techniques - Stream-tube

  • Generate a stream-line and widen it to a tube

  • Width can encode another variable


Mappings flow volumes l.jpg
Mappings - Flow Volumes

  • Instead of tracing a line - trace a small polyhedron


Lic line integral convolution l.jpg
LIC (Line Integral Convolution)

  • Integrate noise texture along a streamline

H.W. Shen


Overview54 l.jpg
Overview

  • What is SciVis?

  • Data & Applications

  • Iso-surfaces

  • Direct Volume Rendering

  • Vector Visualization

  • Challenges


Challenges accuracy l.jpg
Challenges - Accuracy

  • Need metrics -> perceptual metric


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Challenges - Accuracy

  • Deal with unreliable data (noise, ultrasound)


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Challenges - Accuracy

  • Irregular data sets

Structured Grids:

regular

uniform

rectilinear

curvilinear

Unstructured Grids:

regular

irregular

hybrid

curved


Challenges speed size l.jpg
Challenges - Speed/Size

  • Efficient algorithms

  • Hardware developments (VolumePro)

  • Utilize current hardware (nVidia, ATI)

  • Compression schemes

  • Terabyte data sets


Challenges hci l.jpg
Challenges - HCI

  • Need better interfaces

  • Which method is best?


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Challenges - HCI

  • “Augmented” reality

  • Explore novel I/O devices


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