INTRODUCTION TO VISUALIZATION
Alan B. Craig, Ph.D.
Materials from: Dr. Alan Shih, Dave Bock, and Alan Craig, plus all the
researchers who provided examples
National Center for Supercomputing Applications
University of Illinois at Urbana-Champaign
June 3, 2010
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What Is Visualization?!
•Visualization existed before the invention of computers
•Representation of information allowing us to perceive such
information visually
Dr. Alan M. Shih
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Early Representation
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The Cave of Lascaux, France
~15,000 years old
- Tells a story
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Planetary Orbits
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Tenth century
Inclinations of the planetary orbits as a function of time.
Oldest known attempt to show changing values graphically.
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1987 NSF Panel Initiative - Formal Definition
• "Visualization is a method of computing. It
transforms the symbolic into the geometric,
enabling researchers to observe their
simulations and computations. Visualization
offers a method for seeing the unseen. It
enriches the process of scientific discovery and
fosters profound and unexpected insights.”
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Richard Hamming: "The purpose of
computing is insight, not numbers."
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Goal of visualization - leverage existing
scientific methods by providing new scientific
insight through visual methods.
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Why do we do it?
• Because we NEED to...
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Purpose of Visualization
• Self study / analysis
– interactive exploration
– gain understanding
• Between Peers
– Probing inside the problem domain
– Analyzing data
– Communicating with peers
• Presentation to General Public
– Overall visualization
– Presentation of data
– Communicating with general audience
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Data Sources
• Computational Sciences
– Computational Fluid Dynamics
– Computational Structured Mechanics
– Computational Chemistry
– Computational …..
• Observed Data
– Wind Tunnels
– Field Observations
– Space Probes
Dr. Alan M. Shih
National Computational Science Alliance
What is Visualization
• Choice of appropriate representation
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Computational Sciences
• We can realize, without physical prototypes
– the performance of a design
– the possible outcome of a scenario
– the physical details that we did not know or notice
• Benefits
– Reduces development cost
– Reduces development time
– Reduces development risk
Dr. Alan M. Shih
National Computational Science Alliance
Computational Sciences
• Computers brought about the ability to
collect, create, and store more information
• Is a process of simulating a relevant subset
of the laws of nature through a set of
equations
• Yields a set of numeric solutions -Numbers, LOTS of them
• May not be able to see, much less interpret,
all of the results.
Dr. Alan M. Shih
National Computational Science Alliance
Visualization of Data
• Try to envision the domain in your mind
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Visualization of Data
• But, with some modifications to the images...
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Visualization of Data
• Interpolated vs. Non-interpolated
Dr. Alan M. Shih
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Interactive or Batch?
• Interactive Visualization
– Allows the Ability to Control in Real-Time
– Limits the Amount of Data to Be Visualized.
– Useful for Analysis and Exploration
• Batch Visualization
– High-Quality, Complex Representation
– No Control in Real Time.
– Useful for Presentation, Communication,
high complexity
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The Visualization Pipeline
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Data (simulated or observed)
Filter
Map to geometry
Viewing Attributes
Object Attributes
Render
Display
Record
Loop to appropriate step...
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Computer Art and Scientific Visualization
Cox, Donna; Patterson, Robert; Bargar, Robin; Daab, Fred; Moore,
Michael; Moorman, Jan; Waegner, Chris; Erickson, Christian;
Swing, Chris; Conrad, Renee; Knocke, Joel; Jordan, Robert;
Brandys, Mike; Fossum, Barbara; Colby, Don; McNeil, Mike; Bajuk,
Mark; Arrott, Matthew; Swanson, Amy
Researchers
Cerco, Carl; Noel, Mark; CEWES
Visualizaiton
Stein, Robert; Shih, Alan; NCSA
Dr. Alan M. Shih
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Qualitative vs. Quantitative
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A Wonderful Example
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Static vs. Time Varying
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Static vs. Time-Varying Data
• Static
– At an particular instance of time
– Particular Point of View, etc.
• Time-Varying Animation
– Evolving along the time line
– Dynamic Data or Point of View
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Representational Techniques
• Realistic
• Abstract
Researchers
Visualization
Cooper, David; Caterpillar Inc.
Bajuk, Mark; NCSA , 1991
Researchers
Cohen, Josef
Visualization
Cox, Donna, NCSA; Ellson, Rich; Olano, Marc, Eastman Kodak:
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Representation Techniques
Texture Mapping
Visualization
Stein, Robert, Baker, Polly, NCSA, ongoing
Sponsored by ARL
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Representation Techniques
• Ball & Stick
• Contour
Researchers
Herron, David, Eli Lilly & Co.
Visualization
Thingvold, Jeffrey A.; Sherman,
William; NCSA , 1991
Researchers
Treutlein, Herbert; Schulten,
Klaus; Physics Department Technical
University of Munich
Visualization
Arrott, Matthew; NCSA, 1987
Researcher
Taylor, Lafe
Visualization
Shih, Alan, MSU, 1993
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Representation Techniques
• False Color
• Height/Deformation
Researchers
Kovacic, David A., Romme, William H., Despain, Don G.
Visualization
Craig, Alan; NCSA, 1990
Researchers and visualization
Haber, Bob; Lee, Hae-Sung; Koh, Hyun; NCSA, 1989
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Representation Techniques
• Particulate/ Trace
• Iso-surfaces
Researchers
Wilhelmson, Robert; Brooks, Harold; Jewett, Brian; Shaw, Crystal;
Wicker, Louis; Department of
Atmospheric Science and NCSA
Visualization
Arrott, Matthew; Bajuk, Mark; Thingvold, Jeffrey; Yost, Jeffery;
Bushell, Colleen; Brady, Dan;
Patterson, Bob
Produced by the Visualization Services and Development Group,
NCSA
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Representation Techniques
Data from Aerodynamics and Acoustics of Rotorcraft, W. J. McCroskey, Principal Investigator
Animation: FAST Particle Traces: UFAT
Dr. Alan M. Shih
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Scientific Visualization
Damage Structure
Researcher
Namburu, Raju, CEWES
Visualization
Boch, David; Heiland, Randy; Baker, Polly; NCSA
Stephens, Mike; CEWES
Dr. Alan M. Shih
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Scientific Visualization
Damage Structure -- Animation
Researcher
Namburu, Raju, CEWES
Visualization
Boch, David; Heiland, Randy; Baker, Polly; NCSA
Stephens, Mike; CEWES
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Beyond Visual
• Virtual Reality Environment
– ImmersaDesk
– Cave
– Fully immersive sphere
• Haptic Devices
• Senses of hearing and smelling
Dr. Alan M. Shih
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Challenging Issues in SciVis
• Visualization of Large Data Sets
– How to deal with exabytes of data?
• Remote Visualization
– What is the best way to visualize large data sets on
remote mainframe?
• Interactive Computation
– How to monitor and steer ongoing simulations?
• Representation Techniques
– How to represent the data that shows more
information and shows it more clearly and accurately?
• Immersive Technologies
Dr. Alan M. Shih
National Computational Science Alliance
Summary
• The advent of computer capacity and power push
the envelope of computational sciences and
scientific visualization (SciVis)
• SciVis has revolutionized the way we do sciences
• SciVis provides scientists a process to probe into
enormously large data sets, perceive incredible
details of the domain, and discover unexpected
insights.
• Challenging issues in SciVis evolve, but we will
continue to face them, solve the problems, and
face future challenges.
Dr. Alan M. Shih
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Visualization Tools
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Visualization Tools
Dr. Alan M. Shih
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Layers of Information
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Contour Surface & Volume Visualization
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Composite Representation
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Stereo Visualization
• Red-Blue Glasses
– Lost color
• Shutter Glasses
– 60 Hz
– Synchronized with projected images
• Polarized Glasses
– Linear (Horizontal/Vertical)
– Circular (CW/CCW)
– Synchronized with projected images
Dr. Alan M. Shih
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Red-Blue Stereo Visualization
Dr. Alan M. Shih
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Animations
Dr. Alan M. Shih
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