lecture 14 scientific visualization information visualization cpsc 533c fall 2006 l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Lecture 14: Scientific Visualization Information Visualization CPSC 533C, Fall 2006 PowerPoint Presentation
Download Presentation
Lecture 14: Scientific Visualization Information Visualization CPSC 533C, Fall 2006

Loading in 2 Seconds...

play fullscreen
1 / 60

Lecture 14: Scientific Visualization Information Visualization CPSC 533C, Fall 2006 - PowerPoint PPT Presentation


  • 637 Views
  • Uploaded on

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)

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Lecture 14: Scientific Visualization Information Visualization CPSC 533C, Fall 2006' - emily


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
lecture 14 scientific visualization information visualization cpsc 533c fall 2006

Lecture 14: Scientific VisualizationInformation VisualizationCPSC 533C, Fall 2006

Tamara Munzner

UBC Computer Science

26 Oct 2006

credits
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
News
  • Reminder: no class next week
    • I'm at InfoVis/Vis in Baltimore
overview
Overview
  • What is SciVis?
  • Data & Applications
  • Iso-surfaces
  • Direct Volume Rendering
  • Vector Visualization
  • Challenges
difference between scivis and infovis

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
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
Overview
  • What is SciVis?
  • Data & Applications
  • Iso-surfaces
  • Direct Volume Rendering
  • Vector Visualization
  • Challenges
medical scanning
Medical Scanning
  • MRI, CT, SPECT, PET, ultrasound
medical scanning applications
Medical Scanning - Applications
  • Medical education for anatomy, surgery, etc.
  • Illustration of medical procedures to the patient
medical scanning applications10
Medical Scanning - Applications
  • Surgical simulation for treatment planning
  • Tele-medicine
  • Inter-operative visualization in brain surgery, biopsies, etc.
biological scanning
Biological Scanning
  • Scanners: Biological scanners, electronic microscopes, confocal microscopes
  • Apps – physiology, paleontology, microscopic analysis…
industrial scanning
Industrial Scanning
  • Planning (e.g., log scanning)
  • Quality control
  • Security (e.g. airport scanners)
scientific computation domain
Scientific Computation - Domain
  • Mathematical analysis
  • ODE/PDE (ordinary and partialdifferential equations)
  • Finite element analysis (FE)
  • Supercomputer simulations
overview15
Overview
  • What is SciVis?
  • Data & Applications
  • Iso-surfaces
  • Direct Volume Rendering
  • Vector Visualization
  • Challenges
isosurfaces examples
Isosurfaces - Examples

Isolines

Isosurfaces

isosurface extraction
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

mc 1 create a cube
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)

mc 2 classify each voxel
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

mc 3 build an index
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

mc 4 lookup edge list
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
mc 4 example
MC 4: Example
  • Index = 00000001
  • triangle 1 = a, b, c

c

a

b

mc 5 interp triangle vertex
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

mc 6 compute normals
MC 6: Compute Normals
  • Calculate the normal at each cube vertex
  • Use linear interpolation to compute the polygon vertex normal
overview26
Overview
  • What is SciVis?
  • Data & Applications
  • Iso-surfaces
  • Direct Volume Rendering
  • Vector Visualization
  • Challenges
classification
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

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
TF’s
  • Setting transfer functions is difficult, unintuitive, and slow

a

a

f

f

a

a

f

f

Gordon Kindlmann

transfer function challenges
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

light effects
Light Effects
  • Usually only considering reflected part

Light

reflected

specular

Light

absorbed

ambient

diffuse

transmitted

Light=refl.+absorbed+trans.

Light=ambient+diffuse+specular

rendering pipeline rp35
Rendering Pipeline (RP)

Classify

Shade

Interpolate

interpolation

1D

  • Given:
  • Needed:
  • Needed:
Interpolation
  • Given:

2D

interpolation37
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
Rendering Pipeline (RP)

Classify

Shade

Interpolate

Composite

ray traversal schemes
Ray Traversal Schemes

Intensity

Max

Average

Accumulate

First

Depth

ray traversal first
Ray Traversal - First
  • First: extracts iso-surfaces (again!)done by Tuy&Tuy ’84

Intensity

First

Depth

ray traversal average
Ray Traversal - Average
  • Average: produces basically an X-ray picture

Intensity

Average

Depth

ray traversal mip
Ray Traversal - MIP
  • Max: Maximum Intensity Projectionused for Magnetic Resonance Angiogram

Intensity

Max

Depth

ray traversal accumulate
Ray Traversal - Accumulate
  • Accumulate: make transparent layers visible!Levoy ‘88

Intensity

Accumulate

Depth

volumetric ray integration

1.0

Volumetric Ray Integration

color

opacity

object (color, opacity)

overview45
Overview
  • What is SciVis?
  • Data & Applications
  • Iso-surfaces
  • Direct Volume Rendering
  • Vector Visualization
  • Challenges
flow visualization
Flow Visualization
  • Traditionally – Experimental Flow Vis
  • Now – Computational Simulation
  • Typical Applications:
    • Study physics of fluid flow
    • Design aerodynamic objects
techniques
Techniques

Glyphs (arrows)

Contours

Jean M. Favre

Streamlines

techniques stream ribbon
Techniques - Stream-ribbon
  • Trace one streamline and a constant size vector with it
  • Allows you to see places where flow twists
techniques stream tube
Techniques - Stream-tube
  • Generate a stream-line and widen it to a tube
  • Width can encode another variable
mappings flow volumes
Mappings - Flow Volumes
  • Instead of tracing a line - trace a small polyhedron
lic line integral convolution
LIC (Line Integral Convolution)
  • Integrate noise texture along a streamline

H.W. Shen

overview54
Overview
  • What is SciVis?
  • Data & Applications
  • Iso-surfaces
  • Direct Volume Rendering
  • Vector Visualization
  • Challenges
challenges accuracy
Challenges - Accuracy
  • Need metrics -> perceptual metric
challenges accuracy56
Challenges - Accuracy
  • Deal with unreliable data (noise, ultrasound)
challenges accuracy57
Challenges - Accuracy
  • Irregular data sets

Structured Grids:

regular

uniform

rectilinear

curvilinear

Unstructured Grids:

regular

irregular

hybrid

curved

challenges speed size
Challenges - Speed/Size
  • Efficient algorithms
  • Hardware developments (VolumePro)
  • Utilize current hardware (nVidia, ATI)
  • Compression schemes
  • Terabyte data sets
challenges hci
Challenges - HCI
  • Need better interfaces
  • Which method is best?
challenges hci60
Challenges - HCI
  • “Augmented” reality
  • Explore novel I/O devices