|Time, Place:||Thursdays, 13:00-16:00, Gustav-Niemann-Hörsaal|
|Begin:||October 19., 2017|
|Prerequisites||Einführung in die Informatik 1, Analysis, Linear Algebra|
This course is intended for students in Informatics (Diploma/Master), Computational Science and Engineering, Computational Mechanics, Computational Methods in Applied Science and Engineering. It is given in English. The course consists of 3 lecture hours and 1 hour of free exercise, giving 5 ECTS.
The lecture gives an introduction to the fundamentals of data visualization. It discusses the different stages of the visualization pipeline and exemplifies application areas where visualization is paramount such as medicine or engineering. Furthermore, it gives an overview of the many different sources the data can result from, and addresses techniques to bring the initial data into a form that can be visualized. Particular aspects are data interpolation, triangulation, and filtering techniques. We will then outline different strategies to map the data onto a visual representation via graphical primitives. Finally, specific visualization fields are addressed such as volume visualization, flow visualization, and interactive visual analysis.
The students understand the basic algorithms used by modern visualization software. They learn for which data types to use these algorithms, and become aware of frequently used software systems supporting these algorithms. In the practical exercise, students are introduced to available visualization software systems, and are supposed to work with these systems on their own initiative.
- Basics (visualization pipeline, data sources, data types)
- Key application (medical imaging, computational fluid dynamics)
- Data reconstruction, interpolation, triangulation
- Filtering techniques
- Basic data mapping techniques (color mapping, diagrams, glyphs, etc.)
- Volume visualization (iso-surface rendering, direct volume rendering, etc.)
- Vector field visualization (arrows, streamlines, vector field topology, etc.)
- Visual analysis of scientific data
- Munzner: Visualization Analysis & Design, CRC Press
- Hansen & Johnson: The Visualization Handbook, Elsevier
- Schumann & Müller: Visualisierung - Grundlagen und allgemeine Methoden, Springer
- Nielson, Hagen, Müller: Scientific Visualization, IEEE CS Press
- Gallagher: Computer Visualization: Graphics Techniques for Scientific and Engineering Analysis, CRC Press
- Brodlie: Scientific Visualization - Techniques and Applications, Springer
- Earnshaw & Wiseman: An Introductory Guide to Scientific Visualization, Springer