Fakultät Informatik

Scientific Visualization

 Dr. Johannes Kehrer

Wissenschaftliche Visualisierung - Algorithms for Data Visualization

Time, Place:

Thursdays, 13:45-16:00,  Interims Hörsaal 2

Begin:

October 15., 2015

Prerequisites

Einführung in die Informatik 1, Analysis, Linear Algebra

This course is intended for students in Informatics (Diploma/Bachelor/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.

Content

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.

Goal

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.

Course Topics

  • Introduction
  • 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

Lecture Slides and Programming

 Slides

 Webapplets

 Datasets

 

Visualization examples

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