Introduction
Scientific Visualization (Part 1)
PD Dr.-Ing. Peter Hastreiter
What is Visualization?
2
Applied Visualization, SS10
Definitions and goals
• Oxford English Dictionary
– to visualize
• Form a mental vision, image, or picture of
(something not visible or present to sight, or of an abstraction)
• To make visible to the mind or imagination
– e.g. air pressure
• Transformation of an abstraction to a picture
– e.g. molecular structure
3
Applied Visualization, SS10
Definitions and goals
• “A picture says more than a thousand words”
– Color, texture, patterns, objects
– Spatial resolution, stereo, temporal resolution
• Johann Wolfgang von Goethe
– "Thinking is more interesting than knowing,
but not as interesting as watching“
(Denken ist interessanter als Wissen,
aber nicht so interessant wie Anschauen)
4
Applied Visualization, SS10
Definitions and goals
• Importance of the human visual system
– Example: repetition easy to recognize
5
Applied Visualization, SS10
Definitions and goals
• In this lecture:
Scientific Visualization
– Visualization in scientific and technical environments
– Computer aided extraction and display of information
(from measured or simulated data)
– Not in education, marketing, ….
6
Applied Visualization, SS10
Definitions and goals
• B. McCormick, T. DeFanti, and M. Brown
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. In many fields it is already
revolutionizing the way scientists do science.
– McCormick, B.H., T.A. DeFanti, M.D. Brown, Visualization in Scientific
Computing, Computer Graphics Vol. 21.6, Nov 1987
7
Applied Visualization, SS10
Definitions and goals
• R. Friedhoff and T. Kiley
The standard argument to promote scientific visualization is
that today's researchers must consume ever higher
volumes of numbers that gush, as if from a fire hose, out of
supercomputer simulations or high-powered scientific
instruments. If researchers try to read the data, usually
presented as vast numeric matrices, they will take in the
information at snail's pace. If the information is rendered
graphically, however, they can assimilate it at a much
faster rate.
– R.M. Friedhoff and T. Kiely, The Eye of the Beholder, Computer
Graphics World, Vol. 13.8, pp. 46, August 1990
8
Applied Visualization, SS10
Definitions and goals
• R.B. Haber and D. A. McNabb
The use of computer imaging technology as a tool for
comprehending data obtained by simulation or physical
measurement by integration of older technologies,
including computer graphics, image processing, computer
vision, computer-aided design, geometric modeling,
approximation theory, perceptual psychology, and user
interface studies.
– R.B. Haber and D. A. McNabb, Visualization Idioms: A Conceptual
Model for Scientific Visualization Systems, in Visualization in
Scientific Computing, IEEE Computer Society Press 1990
9
Applied Visualization, SS10
Definitions and goals
• R.A. Earnshaw
Scientific Visualization is concerned with exploring data
and information in such a way as to gain understanding
and insight into the data. The goal of scientific visualization
is to promote a deeper level of understanding of the data
under investigation and to foster new insight into the
underlying processes, relying on the humans' powerful
ability to visualize. In a number of instances, the tools and
techniques of visualization have been used to analyze and
display large volumes of, often time-varying, multidimensional data in such a way as to allow the user to
extract significant features and results quickly and
easily.
– K.W. Brodlie, L.A. Carpenter, R.A. Earnshaw, J.R. Gallop, R.J. Hubbard,
A.M. Mumford, C.D. Osland, P. Quarendon, Scientific Visualization,
Techniques and Applications, Springer-Verlag, 1992
10
Applied Visualization, SS10
Definitions and goals
• J. Foley and B. Ribarsky
A useful definition of visualization might be the binding (or
mapping) of data to representations that can be
perceived. The types of bindings could be visual, auditory,
tactile, etc., or a combination of these.
– J. Foley and B. Ribarsky, Next-generation Data Visualization Tools, in
Scientific Visualization, 1994, Advances and Challenges, Academic Press
11
Applied Visualization, SS10
Definitions and goals
• H. Senay and E. Ignatius
Scientific data visualization supports scientists and
relations, prove or disprove hypotheses, and discover
new phenomena using graphical techniques.
The primary objective in data visualization is to gain
insight into an information space by mapping data onto
graphical primitives.
– H. Senay and E. Ignatius, A Knowledge-Based System for
Visualization Design, IEEE Computer Graphics and Applications, pp.
36-47, November 1994.
12
Applied Visualization, SS10
Definitions and goals
• Insight and analysis
– Extract the information content
– Make things/coherences visible that are not apparent
– Analyze data by means of the visual representation
• Communication
– Allow the non-expert to understand
• Present information in a way that all of us understand
– Guide the expert into the right direction
• Steering
– Interactively control and drive your application
– Accelerate the understanding of phenomena
13
Applied Visualization, SS10
Definitions and goals
Hamming 1962
“The purpose of computing is insight not numbers”
14
Applied Visualization, SS10
Historic Examples
Techniques for finding visual representations
for abstract data are not new !
15
Applied Visualization, SS10
History
Map of Catal Hyük
6200 BC
Excavation
Reconstruction
16
Applied Visualization, SS10
History
Map on clay of Ga-Sur
2500 BC
•
•
17
Map on clay
Reconstruction
Applied Visualization, SS10
History
Map on stone (Sung Dynasty, China, 1137 BC)
18
Applied Visualization, SS10
History
• Time series
– Inclination of planets (10th century)
19
Applied Visualization, SS10
History
Map of the Tracks
of Yü (the Great)
(11th century)
20
Applied Visualization, SS10
History
Ptolemaic map of the world by Johan Scotus (1505)
21
based on the writings of Claudius Ptolemy (87-150 A.D.)
Applied Visualization, SS10
History
22
Vector visualization (1686)
Height field (1879)
Stream lines, arrow plots by Halley
Census data by Perozzo
Applied Visualization, SS10
History
Dr. John Snows
map (1855) of death
from cholera in the
broad Street area in
September 1854.
23
Applied Visualization, SS10
History
Napoleon‘s campaign against Russia (1812/13)
Cartography (1861)
24
Applied Visualization, SS10
History
Main ocean currents
Provides better understanding of transoceanic travel
Courtesy of Albatros, Enciclopedia del mar, Barcelona 1974
25
Applied Visualization, SS10
Some historical remarks
26
Applied Visualization, SS10
Some historical remarks
• 1987 NSF Advisory Panel
on Graphics and Image Processing
– Computer experiments allow access to new worlds
• Real experiments are too expensive, too dangerous, etc.
• Arbitrary large and small time scales and spatial dimensions
– SIGGRAPH-Workshop
on Visualization in Science Computing
• Start of scientific visualization
– Direct implications
• Data flood from supercomputers can only be dealt visually
• Needs visualization specialist and an interdisciplinary team
• Needs developments in hardware, software, nets, standards
27
Applied Visualization, SS10
Some historical remarks
– Advantages in the long term will be
• Faster insight
• Faster product–development cycles
• Stronger position in global competition
– Suggestion
(addressed to economy and politics!)
• Greater financial support has to be provided
• Spend lots of money to support scientific visualization
28
Applied Visualization, SS10
Relation to other subjects
29
Applied Visualization, SS10
Relation to other subjects
• Measurements and experiments
–
–
–
–
Medical imaging (e.g. computed tomography, …)
Geology (e.g. oil exploration)
Meteorology and environment (e.g. satellites)
Astronomy (e.g. Hubble Space Telescope 100MB/day)
• Computer simulations
–
–
–
–
30
Computational fluid mechanics (CFD)
Structural mechanics
Chemistry (molecular modeling)
Physics (computational physics)
Applied Visualization, SS10
Relation to other subjects
• Links with the following areas
–
–
–
–
–
–
–
–
31
Engineering
Numerical mathematics
Image processing
Computer vision
Physics
Chemistry
Medicine
Psychology
Applied Visualization, SS10
Visualization topics
–
Lecture content
Educational goals
32
Applied Visualization, SS10
Visualization topics
• Lecture overview
– Basics
• Visualization pipeline, data types, coordinate systems
• Data structures, differentiation, interpolation
–
–
–
–
1D and 2D scalar fields
2D and 3D vector fields: flow visualization
Tensor fields
3D scalar fields: volume visualization
– Optional topics (depending on remaining time)
• Information Visualization
• Visual perception
33
Applied Visualization, SS10
Visualization topics
• Educational goals
– Theory
• Classification
• Algorithms
– Application
• Methods
• Visualization packages
– Experience and preparation
• How to visualize something in the best way
• Diploma / master thesis
• Further work in industry and research
– Become a visualization specialist
34
Applied Visualization, SS10
Applications
in geosciences
35
Applied Visualization, SS10
Visualization in geosciences
• Weather forecast
36
Applied Visualization, SS10
Visualization in geosciences
37
Applied Visualization, SS10
Visualization in geosciences
• Simulation of climate phenomena
(volcanoes, atmosphere)
Deutsches
Klimarechenzentrum
DKRZ
38
Applied Visualization, SS10
Visualization in geosciences
• Simulation of global warming
– Worst case (left), with drastic political measures (right)
Deutsches
Klimarechenzentrum
DKRZ
39
Applied Visualization, SS10
Visualization in geosciences
• Exploration of oil and gas reservoirs
40
Applied Visualization, SS10
Visualization in geosciences
41
Applied Visualization, SS10
Visualization in geosciences
• Visualization of traffic in Dallas, Texas
AVS
Express
42
Applied Visualization, SS10
Visualization in geosciences
• Analysis of tornado phenomena
43
Applied Visualization, SS10
Applications
in material sciences
44
Applied Visualization, SS10
Visualization in material sciences
• Interior of structural components and
composite materials based on tomography
45
Applied Visualization, SS10
Visualization in material sciences
46
Applied Visualization, SS10
Visualization in material sciences
47
Applied Visualization, SS10
Visualization
in medicine
48
Applied Visualization, SS10
Visualization in medicine
• CT (Computed tomography) data
49
Applied Visualization, SS10
Visualization in medicine
• MRI (Magnetic Resonance Imaging) data
50
Applied Visualization, SS10
Visualization in medicine
• Visible human project
51
Applied Visualization, SS10
Visualization in medicine
• Diagnosis
AVS
Express
52
Applied Visualization, SS10
Visualization in medicine
• Therapy planning
AVS
Express
53
Applied Visualization, SS10
Visualization in medicine
• Therapy evaluation
AVS
Express
54
Applied Visualization, SS10
Visualization in medicine
• Malformation of blood vessel (aneurysm)
– Based on computed tomography angiography (CTA)
55
Applied Visualization, SS10
Examples
• Computed flow through a cerebral aneurysm
56
Applied Visualization, SS10
Examples
• Blood flow in carotid artery bifurcation
57
Applied Visualization, SS10
Visualization in medicine
• Functional time dependent information
– Example with PET (Positron Emission Tomography) data
58
Applied Visualization, SS10
Examples
• Visualization of neuronal pathways
Glyph representation
59
Fiber tracking
Applied Visualization, SS10
Example
60
Applied Visualization, SS10
Visualization
in archeology
61
Applied Visualization, SS10
Visualization in archeology
62
Applied Visualization, SS10
Visualization
in Chemistry and Biology
63
Applied Visualization, SS10
Visualization in chemistry and biology
•
64
AChE-FAS complex, showing that the snake venom blocks the
entrance to the gorge. The active residues are shown as ball and stick
Applied Visualization, SS10
Visualization in chemistry and biology
•
65
Aromatic residues in active site (ball and stick) of AChE enzyme with
molecular surface of ACh molecule
Applied Visualization, SS10
Visualization in chemistry and biology
• Simulation of the docking of the CBH1 tail on
a cellulose surface
Visualization and
Animation Laboratory at
Center of Scientific
Computing Finland
66
Applied Visualization, SS10
Visualization
in Physics
67
Applied Visualization, SS10
Visualization in Physics
• Simulated events
– In relativistic Heavy Ion Collider Experiment PHENIX
– Hits at detector appear as yellow glyphs. Blue lines
trace probable tracks of products from collision. [J. Mitchell]
68
Applied Visualization, SS10
Visualization in Physics
• Generation of a ring of dust around a star
69
Applied Visualization, SS10
Visualization
in Engineering
70
Applied Visualization, SS10
Visualization in Engineering
• Cavity Flow Field
71
Applied Visualization, SS10
Visualization in Engineering
• Turbulent Flow
72
Applied Visualization, SS10
Visualization in Engineering
• Flow visualization
73
Applied Visualization, SS10
Visualization in Engineering
• Simulation of a flow around a wheel
(3D Line Integral Convolution)
74
Applied Visualization, SS10
Visualization in Engineering
• 3D-LIC + interactive slicing
75
Applied Visualization, SS10
Visualization in Engineering
• Arrow Plot on Clipping Plane
76
Applied Visualization, SS10
Visualization in Engineering
• Pseudo color image
– Spray pattern generated by a pressure swirl nozzle
77
Applied Visualization, SS10
Visualization in Engineering
Visualization of swirl motion
using 3D streamlines
78
Visualization of flow at the
surface of two intake ports
Applied Visualization, SS10
Visualization in Engineering
• Measurement and simulation
– Flow in a water tunnel
measurement
simulation
79
Applied Visualization, SS10
Visualization in Engineering
• Different flow visualization techniques
80
Applied Visualization, SS10
Visualization in Engineering
81
Applied Visualization, SS10
Visualization in Engineering
• Analysis of structural deformation
82
Applied Visualization, SS10
Information Visualization
InfoVis
83
Applied Visualization, SS10
InfoVis (Information Visualization)
Web
hyperlinks
84
Applied Visualization, SS10
Examples
• InfoVis (Information Visualization)
Cone trees
85
Tree maps
Applied Visualization, SS10
Visualization of algorithms
86
Applied Visualization, SS10
Visualization of algorithms
• Decompression of a triangle mesh
87
Applied Visualization, SS10
Related Fields
88
Applied Visualization, SS10
Related Fields
• Image synthesis
– Photorealistic rendering:
– Synthesis of an image of a scene as it would look like
in reality
89
Applied Visualization, SS10
Related Fields
• Geometric modeling
– The effective representation and efficient modification
of geometric shape on a computer
90
Applied Visualization, SS10
Related Fields
• Image processing and computer vision
– The manipulation of images and the extraction of
object specific information from images
91
Applied Visualization, SS10
Further Reading
92
Applied Visualization, SS10
Further Reading
• Books
– G.M. Nielson, H.Hagen, H.Müller, Scientific
Visualization, IEEE Computer Society Press, Los
Alamitos, 1997
– Richard S. Gallagher (Ed.), Computer Visualization:
Graphics Techniques for Scientific and
Engineering Analysis, CRC Press, 1995
– R. A. Earnshaw, N. Wiseman (Eds.), An Introductory
Guide to Scientific Visualization, Springer, 1992
– K.W. Brodlie u.a. (Eds.), Scientific Visualization Techniques and Applications, Springer 1992
– H. Schumann, W. Müller, Visualisierung Grundlagen und allgemeine Methoden, SpringerVerlag, Heidelberg 2000
93
Applied Visualization, SS10
Further Reading
• Books
– W. Schroeder, K. Martin, The Visualization Toolkit:
An Object-Oriented Approach to 3-D Graphics,
Pearson 1997
– C. Rezk-Salama, K. Engel, M. Hadwiger, J. Kniss,
D. Weiskopf, Real-Time Volume Graphics,
AK Peters, 2006
– B. Preim, D. Bartz, Visualization in Medicine. Theory,
Algorithms, and Applications: Theory, Algorithms,
and Applications, Morgan Kaufmann, 2007
– AC. Telea, Data Visualization: Principles and
Practice, A K Peters, 2008
94
Applied Visualization, SS10
Further Reading
• Advanced reading:
–
–
–
–
–
P. Keller und M. Keller, Visual Cues … (Image book)
Wolff, Yaeger: Visualization of Natural Phenomena
Rosenblum et al.: Scientific Visualisation
Gallagher (ed): Computer Visualization
Brown, Earnshow, et al.: Visualization, Using CG to
Explore...
95
Applied Visualization, SS10
Further Reading
• Proceedings of annual conferences:
– IEEE Visualization
[vis.computer.org]
– EuroVis EG/IEEE TCVG Symp. on Visualization
[eurovis2010.labri.fr]
– ACM SIGGRAPH (USA)
[www.siggraph.org]
– Eurographics
[www.eg.org]
–…
96
Applied Visualization, SS10
Further Reading
• Journals:
– IEEE Transactions on Visualization and Computer
Graphics
– IEEE Computer Graphics & Applications
– The Visual Computer (Springer)
– Computers & Graphics (Pergamon)
– Visualization and Computer Animation (Wiley)
• IEEE digital library
– Access from computers within the university
97
Applied Visualization, SS10