Visualization

CSE 694L- Visualization

Raghu Machiraju Dreese Laboraories, 779 [email protected] www.cse.ohio-state.edu/~raghu Outline

 Introduction

 Visualization pipeline

 Data acquisition and data structures

 Basic visual mapping approaches

 Scalar ﬁelds (isosurfaces + volume rendering)

 Vector and ﬁeld visualization

 Perception + Interaction Issues

 Graphs/Trees + HighD Data

22 Syllabus

33 Sources

Selective contributions from

- Hanspeter Pfister, Harvard University

- Torsten Moeller, Simon Fraser U, Canada

- Tamara Munzner, University of British Columbia

- Melanie Tory, U of Victoria, Canada

- Daniel Weiskopf, TU Stuttgart, Germany

44 1. Introduction

 What is visualization?

 Definitions and goals

55 1.1. 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.

 Picture

- Color, texture, patterns, objects

- Spatial resolution, stereo, temporal resolution

 Here: visualization in scientific and technical environments

- Not in education, marketing, …. 6 1.1. 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 21(6), November 1987

8 1.1. 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 13(8), pp. 46-, August 1990

9 1.1. 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.

10 1.1. 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. 1010 1.1. 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.

1111 1.1. 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. 1212 1.1. Definitions and Goals

 Insight and analysis

- Extract the information content

- Make things/coherences visible that are not apparent

- Analyze the data by means of the visual representation

 Communication

- Allow the non–expert to understand • Present specific information in a way that all of us understand

- Guide the expert into the right direction 1313 • Steering (Exploration)

- Interactively control and drive your application 1.1. Definitions and Goals

Educational goals

 Visualization specialist

 Theory

- Classification

- Algorithms

- Visual design

 Application

- Methods

- Visualization packages

• Experience

- How to visualize something in the best way

1414 1.2. History

Techniques for finding visual representations for abstract data are not new!

1515 1.2. History

Map of Catal Hyük 6200 BC

Excavation

Reconstruction

1616 1.2. History

Map on clay of Ga-Sur

2500 BC

• Map on clay • Reconstruction

1717 1.2. History

Sung Dynasty, China (1137 BC)

1818 1.2. History

Ptolemaic map of the world by Johan Scotus (1505 AD)

20 1.2. History

Time series: inclination of planets (10th century)

2020 1.2. History

Map of tracks of Yü the Great

(11th century)

2121 1.2. History

Vector visualization (1686) Height ﬁeld (1879) Streamlines, arrow plots by Halley Census data by Perozzo 1.2. History

Napoleon‘s campaign against Russia (1812/13) Cartography (1861) 1.2. History

 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

 Direct implications

- The data flood from supercomputer simulations can only be dealt with visually

- This task needs a visualization specialist and an interdisciplinary team

- New developments in hardware, software, nets, standards are necessary

 Advantages in the long term will be

- Faster insight

- Faster product–development cycles

- Stronger position in global competition

 Suggestion: spend lots of money to support scientific visualization 1.3. Examples

2525 1.3. Examples

Deutsches Klimarechenzentrum

DKRZ

Simulation of climate phenomena (volcanoes, atmosphere) 1.3. Examples

Deutsches Klimarechenzentrum

DKRZ

Simulation of global warming. Worst case (left), with drastic political measures (right)

2727 1.3. Examples

AVS Express Visualization of traffic in Dallas, Texas 1.3. Examples

AVS Express Medical imaging: therapy planning

2929 1.3. Examples

AVS Express Medical imaging: therapy evaluation

3030 1.3. Examples

Varying the transfer function – the visible woman 1.3. Examples

Aneurysms: vessel deformations in CTA 1.3. Examples

7T MRI Image 3D/4D Fetal Imaging CT Small Animal Imaging

Coronary Artery Ulcerated Plaque Heart - CT Visualization - MRI 1.3. Examples

CT scan

3434 1.3. Examples Blue: nuclei indicator

Purple: neuron indicator

Green: blood vessel wall indicator

Red: astrocyte indicator (GFAP)

Yellow: microglia indicator (Iba-1)

50 µm

Prof. Badri Roysam, RPI 1.3. Examples

Prof. Sean Megason, Harvard Medical School

800 Timesteps, Movie 1.3. Examples

Jens Rittscher, GE Global Researcher

More ZebraFish Body Parts - Eye 1.3. ExamplesDrs. Knopp, Sammett, Radiology, OSU Drs. Peter Basser, Carlo Pierpaoli, NIH

DWI Tensors Fibers 1.3. Examples

Dr. Morocz, Radiology, Harvard Medical 1.3. Examples

AChE-FAS complex, showing that the snake venom blocks the entrance to the gorge. The active residues are shown as ball and stick. 1.3. Examples

The secondary structure (alpha helices and beta sheets) of the protein Acetylcholinesterase. The aromatic residues (bonds) and the neurotransmitter Acetylcholine (molecular surface) lodged in the active site are also shown. [J. Sussman] 1.3. Examples

Relation of secondary structure (alpha-helix) to primary structure formed by the polypeptide chain of amino acids. The helix is stabilized by Hydrogen bonding. 1.3. Examples

Aromatic residues in active site (ball and stick) of AChE enzyme with molecular surface of ACh molecule. 1.3. Examples

Active site in AChE showing Tachrine molecule used in the experimental drug Cognix to treat Alzheimer's disease. By inhibiting the enzyme's activity, Tachrine prevents the breakdown of the neurotransmitter ACh. 4444 1.3. Examples

Visualization and Animation Laboratory at Center of Scientiﬁc Computing Finland

Simulation of the docking of the CBH1 tail on a cellulose surface. 1.3. Examples

Simulated events in the Relativistic Heavy Ion Collider Experiment PHENIX. Hits at the detector appear as yellow glyphs. Blue lines trace the probable tracks of products from the collision. [J. Mitchell] 1.3. Examples

Generation of a ring of dust around a star

4747 1.3. Examples

Simulation of a ﬂow around a wheel (3D Line Integral Convolution) 1.3. Examples

Flow visualization 1.3. Examples

Flow visualization

5050 1.3. Examples

Computed flow through a cerebral aneurysm.

5151 1.3. Examples

Pseudo color image of the spray pattern generated by a pressure swirl nozzle 5252 1.3. Examples

Measurement and simulation of flow in a water tunnel

5353 1.3. Examples

Flow visualization: Tornado dataset

5454 1.3. Examples

5555 1.3. Examples Flow Feature Analysis

 1. Vortex detection  2. Topology Identification 1  3. Core line extraction  4. Extent computation  5. TBD: Outlier Detection and Robustness 2

5 1.3. Examples

Non-spatial Data Vis: Web hyperlinks (quasi-hierarchical graphs)

5858 1.3. Examples

Cone Trees Tree Maps

5959 1.4. Related Fields

• Image synthesis (CSE 681/781/782)

- Photorealistic rendering

- The synthesis of an image of a scene as it would look like in reality

6060 1.4. Related Fields

Geometric modeling (CSE 784)

- The effective representation and efficient modification of geometric shape on a computer 1.4. Related Fields

Image processing and computer vision

- The manipulation of images and the extraction of object speciﬁc information from images

6262 1.4. Related Fields

Rendering/ Signal/Image Computer Processing Graphics

Vision Computational Geometry

Visualization Psychology Applied Cognition Mathematics

Hardware User Interfaces 1.5. Visualization Flavors

 Spatial Data Vis (aka: Scientiﬁc visualization)

- User Interfaces

- Data representation/processing

- Algorithms

- Visual representations

- Mainly: Continuous models

 Non-Spatial Data Vis (aka: Information visualization)

- Abstract data

- WWW documents

- File structures

- Arbitrary relationships

- …

- Mainly: Discrete models 1.6. Further Reading

 Primary book on perception and visual design:

- C. Ware: Information Visualization: Perception for Design, Elsevier/Morgan Kaufmann, (1st ed. 2000, 2nd ed. 2004) First edition available online: http://ollie.lib.sfu.ca/cgi-bin/validate/books24x7.cgi?isbn=1558605118

 Primary book(s) on Spatial Data / Volume Graphics

- C.D. Hansen, C.R. Johnson (eds.): The Visualization Handbook, Elsevier, 2005

- K. Engel, M. Hadwiger, J.M. Kniss, C. Rezk-Salama, D. Weiskopf, Real-time volume graphics, AK Peters, 2006

 Primary book(s) on Non-Spatial Data

- Ch. Chen, Information Visualization: Beyond the Horizon, Springer Verlag, 2004

- Card, Mackinlay, and Shneiderman, (eds.), Readings in Information Visualization: Using Vision To Think; Morgan Kaufmann 1999 1.6. Further Reading

• References:

- G.M. Nielson, H.Hagen, H. Müller: Scientiﬁc Visualization, IEEE Computer Society Press, Los Alamitos, 1997

- W.J. Schroeder, K.W. Martin, B. Lorenson: The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, Kitware, Clifton Park

- The Visualization Toolkit User's Guide, Version 4.2, Kitware, 2003

- E.R. Tufte, The Visual Display of Quantitative Information, Graphics Press 1983

- E.R. Tufte, Envisioning Information, Graphics Press 1990

- E.R. Tufte, Visual Explanations, Edward R. Tufte, Graphics Press 1997 1.6. Further Reading

• Further books on visualization (somewhat older):

- 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 1.6. Further Reading

• Proceedings of annual conferences:

- IEEE Visualization (USA) [vis.computer.org]

- Eurovis (EG / IEEE TCVG Symposium on Visualization)

- ACM SIGGRAPH (USA) [www.siggraph.org]

- Eurographics [www.eg.org]

- …

• Journals:

- IEEE Transactions on Visualization and Computer Graphics

- IEEE Computer Graphics & Applications

- ACM Transactions on Graphics

- The Visual Computer (Springer)

- Computers & Graphics (Pergamon) 1.7. (Spatial) Visualization Tools

• Great / free:

- VTK (The Visualization Toolkit) (http://www.vtk.org)

• Commercial tools:

- Amira http://www.amiravis.com

- AVS/Express http://www.avs.com

- IDL http://www.creaso.com

- IRIS Explorer http://www.nag.co.uk/ Welcome_IEC.asp

- OpenDX (now open software): http:// www.opendx.org 1.7. (Non-Spatial) Vis. Tools

• Tamara’s resources page! http://www.cs.ubc.ca/~tmm/courses/cpsc533c-06-fall/ resources.html

• Free:

- Prefuse (java) http://prefuse.sourceforge.net/

- Xgobi http://www.research.att.com/areas/stat/xgobi/

• Commercial tools:

- Tableau http://www.tableausoftware.com/ What Is It I Expect?

• Good programming background

- C/C++

• Good Unix exposure

- Make files, etc.

• Basic computer science

- Data structures, algorithms

• Basic math

- Numerical integration

- Linear algebra, systems of linear equations

- Vectors, matrices

• Basic computer graphics

- CMPT 361 or equivalent

- Preferably some previous OpenGL exposure

• Keeping up with the text(s) is very important I Am Not Going To ...

• Teach C/C++

• Teach data structures

• Teach linear algebra and basic numerical methods

• Questions about C/C++ are low priority

• Lab procedures are your responsibility