Visualization
FACULTY
* Bill Ribarsky
* John Stasko
* Chris Shaw
* Greg Turk
SCOPE
Visualization deals with the automatic construction and display of visual
interpretations of data.
SUGGESTED READINGS
1. W Schroeder, K Martin, and W Lorenson. The Visualization
Toolkit, 2nd E dition. Prentice Hall PTR, Upper Saddle River, NJ (1998).
2. SK Card, J Mackinlay, and B Schneiderman. Readings in
Information Visualization. (Morgan Kaufmann, San Francisco, 1998).
3. C. Upson, T Faulhaber, D Kamins, D Laidlaw, D Schlegel,
J Vroom, R Gurwitz, and A van Dam. The Application Visualization System:
A Computational Environment for Scientific Visualization. IEEE Computer Graphics
&Applications, pp. 30-42 (July, 1989).
4. T Delmarcelle and L Hesselink. Visualizing Second-Order
Tensor Fields with Hyperstreamlines. IEEE Computer Graphics & Applications,
pp. 25-33 (July, 1993).
5. M. Cox, and D. Ellsworth. Application-controlled Demand
Paging for Out -of-core Visualization. Proceedings of the IEEE Visualization
Conference, pp 2 35-244 (1997).
6. S. Bryson and S. Johan. Time Management, Simultaneity
and Time-critical Computation in Interactive Unsteady Visualization Environments.
Proc. Visualization 1996, pp. 255-261 (1996).
7. R.M. Kirby, H. Marmanis, and D.H. Laidlaw. Visualizing
Multivalued Data from 2D Incompressible Flows Using Concepts from Painting.
Proceedings IEEE Visualization '99, pp. 333-340.
8. C.A.H Baker, M.S.T Carpendale, P. Prusinkiewicz, and
M.G. Surette. GeneVis: Visualization Tools for Genetic Regulatory Network
Dynamics. Proceedings IEEE Visualization '02, pp. 243-250.
9. Nickolas Faust, William Ribarsky, T.Y. Jiang, and Tony
Wasilewski, “Real-Time Global Data Model for the Digital Earth,”d Proceedings
of the INTERNATIONAL CONFERENCE ON DISCRETE GLOBAL GRIDS (2000). pdf
10. H. Hoppe. Smooth View-Dependent Level-of-Detail Control
and its Application to Terrain Rendering. Proceedings Visualization '98,
18-23 Oct 1998, pp.35 -42, 516.
11. L. Hong, S. Muraki, A. Kaufman, D. Bartz, and T. He.
Virtual Voyage: Interactive Navigation in the Human Colon. Proceedings of
Computer Graphics and Interactive Techniques '97, pp. 27-34.
12. S. Rusinkiewicz and M. Levoy.. QSplat: A Multiresolution
Point Rendering System for Large Meshes. Proceedings of Computer Graphics
and Interactive Techniques 2000, pp. 343-352.
13. S. Eick. Visual Discovery and Analysis. IEEE Transactions
on Visualization and Computer Graphics, Volume: 6 Issue: 1 , Jan-Mar 2000,
pp. 44 -58.
14. W. de Leeuw, J. van Wijk. Enhanced Spot Noise for Vector
Field Visualization. Proceedings IEEE Visualization ’95, pp. 233-239 (1995).
15. M. Zwicker, H. Pfister, J. van Baar, and M. Gross.
EWA Volume Splatting. IEEE Transactions on Visualization and Computer Graphics,
Volume: 8 Issue: 3 , Jul-Sep 2002, pp. 223 -238.
OPTIONAL READINGS
1. Edward R. Tufte. The Visual Display of Quantitive Information.
Graphics Press, 1983.
2. Peter R. Keller and Mary M. Keller. Visual Cues: Practical
Data Visualization. IEEE Computer Society Press, 1993.
SUGGESTED COURSES
* CS 6480 Computer Visualization Techniques
* CS 7450 Information Visualization
* CS 6780 Medical Image Processing
SAMPLE QUESTIONS
1. Describe in detail the Marching Cubes algorithm, and
the topological errors that need to be accounted for relative to the original
(1987) formulation.
2. Describe in detail Levoy's volume rendering algorithm.
3. Describe in detail the different approaches to volume
visualization represented by surface rendering, volume rendering, and texture
memory.
4. Describe in detail how a synthetic object (i.e., a graphically
generated structure) can be imbedded in a discrete volume (i.e., an acquired
3D dataset).
5. How can an octree data representation be used to guide
and/or expedite the volume rendering process?
6. Describe in detail a principal-component or eigenvalue
approach to representing tensor fields.
7. Describe how a large dataset that can include widely
varying scales (from very large resolution data to very small resolution
samples) can be efficiently visualized by varying the level of detail in
the interactive visualization process. Describe in detail how the transition
from one scale to another can be smoothed.
8. Describe in detail a technique for finding isosurfaces
in a volume of data that would be scalable with respect to size of the dataset.
9. Suppose you were confronted with a time-dependent dataset
that you wanted to depict with a particular technique (isosurfaces, volumetric,
clusters, etc.) How would you modify the technique to make it efficiently
handle time dependence.
10. Consider a multidimensional, time dependent collection
of spatial data. Describe two separate methods to visualize this multivariate,
temporally evolving dataset.
11. Dataflow visualization architectures are quite widely
used. These are based on the notion of directed acyclic graphs. Discuss the
advantages and disadvantages of these architectures. In particular, what
are their limitations for handling large data, and are they intrinsic?
12. Discuss what factors affect performance in a visually-dominant
virtual environment. Are there certain tasks affected more by some factors
than others? Describe how you would set up an experiment to measure the effects
of these factors and how you would analyze the results
13. Compare and contrast scientific visualization and information
visualization. Are different approaches or techniques needed in the two fields,
or do they use variations of the same methods?
14. Visualization involves more than methods of representation
for data. It is also concerned with the process of visual analysis. Describe
some general approaches and specific examples for visual analysis.
15. For data organizations it is necessary to know the
topology, geometry, and attributes of the data. Describe these and give examples
of each. In particular, describe in detail the the topological classifications
of 3D data and show how these can be used in visual representations.
16. Describe in detail a technique for finding isosurfaces
in a volume of data that would be scalable with respect to size of the dataset.