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Outline UFE Workshop for Scientific Visualization

by WebSysAdmin last modified 2006-09-08 16:02

The Scientific Visualization workshops will be conducted in a similar fashion to the previous Computer Graphics workshops in that they will be a mixture of lecture, demonstration, and laboratory, which proved to be a successful format. The faculty will use both high end visualization systems, such as IRIS Explorer for UNIX Workstations, and lower end systems, such as Mathematica, MathCad, or MATlab, that run on PC's.

Participants who complete this workshop will have enough background to teach a course in Scientific Visualization or a section on Scientific Visualization incorporated in some other course, such as computer graphics or computational science.

The objective of the Scientific Visualization Workshop is to educate the teachers in the principles and applications of Scientific Visualization. A second objective is courseware development. The faculty participants will produce images and examples of Scientific Visualization techniques that they can use in their own courses. The workshops will extend for five days, Monday through Friday. Some of the formal lecture time will be reserved for curriculum and pedagogical discussions and these will be integrated into all of the lectures. Our previous experience with the Computer Graphics Workshops was that the pedagogical issues were very important, i.e., participants wanted to know not only the actual content but also the best way to teach a subject. For a new, somewhat unsettled area such as Scientific Visualization, it will be very useful to also discuss why something should be included.

Detailed workshop outline

1 Definitions and Goals of Visualization

For any visualization course it is important to discuss background, definitions, and goals in order to provide a common understanding of visualization.

We will discuss the following subtopics in the tutorial:

  1. History of (scientific) visualization

  2. Definitions of visualization

  3. Goals of visualization

2 Abstract Visualization Concepts

It is necessary to establish a framework for the use of visualization. Participants should learn how to make use of concepts and paradigms, specifically of the ones they are not yet familiar with (e.g., paradigms from Fine Arts for Computer Science students). We will discuss the following subtopics in the tutorial:

  1. General visualization models and taxonomy

  2. Examples of specific visualization models and paradigms

3 Human Perception Concepts

This section will enhance the understanding of how to use graphics tools to support human perception in order to gain insight into phenomena that we seek to interpret. We will discuss the following subtopics in the tutorial:

  1. The human visual system (biological, psychophysical and cognitive issues, visual phenomena, texture and color perception)

  2. Perception theories

  3. Presenting complex information to the human visual system (e.g. data exploration, natural computing, integrated displays, using senses additional to vision)

  4. Practical considerations (e.g. expressiveness, effectiveness, interactivity, annotations, avoiding pitfalls)

  5. Evaluation methods

4 Scientific Methods and Concepts

This theme explains the relationship between the 'real world' and the 'models' we have available in order to understand the real world and the 'empirical (data) measurements' we have of the real world. Non-science students have usually little approach to models, data concepts and reality. We will discuss the following subtopics in the tutorial:

  1. Scientific concepts: what is a model; model vs. acquiring; going from macro-to micro worlds

  2. Modeling concepts: mathematical methods to represent reality; mathematical concepts; computational models

  3. Data concepts: how to represent reality; data collections; errors

5 Aspects of Data

Various aspects of data, such as acquisition, classification, storage and retrieval of data, will be discussed. Appropriate subtopics are

  1. Acquisition of data (Simulation vs. measuring devices)

  2. Discipline-independent classification of information sources

  3. Data base issues

  4. Query languages

  5. Reliability of data

6 Visualization Techniques

This section provides tutorial participants with a wealth of ideas for visual representations and teaches them how to apply appropriate tools. We will discuss 2-d, 3-d and multi-dimensional visualization techniques, such as color transformations, glyphs for high dimensional data sets, visualization of gaseous and fluid information, volume rendering, isolines and isosurfaces, coloring, particle tracing, animation, techniques in virtual environments, and interactive steering.

7 Interaction Issues

Interaction techniques are fundamental to the design and use of visualization systems. We will discuss interaction from the view point of ergonometry, HCI and hardware techniques.

8 Existing Visualization Systems/Tools

Available visualization systems will be discussed.

9 Aesthetics in Visualization

The following subtopics will be discussed:

  1. Aspects of successful visualizations

  2. Comparison of good and bad visual representations

10 Related Topics

A visualization course might include fundamental aspects of mathematics and computer science. The presentation of appropriate subtopics depends on the objectives of the course and the background of the students. Appropriate subtopics may be:

  1. Mathematical techniques (e.g. vectors, matrices, interpolation approximation, transformations for 2- and 3-d, parametric versus implicit versus explicit representations, curves, surfaces, fractals);

  2. Computer graphics (e.g. 2-d drawing, clipping, filling; 3-d modeling, rendering, lighting; transparency, translucency; raytracing, radiosity, volume rendering; graphics standards and libraries)

  3. General computer science (e.g. user interface design; computational geometry; computer hardware architectures, input/output technologies; data structures, data models, data formats, data transfer; programming languages)


Last changed 1 October, 1996, Valerie A. Miller, miller@siggraph.org

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