Course info

Authors: Holger Theisel

Level: intermediate

Prerequisites: basic knowledge of Computer Graphics

Description:

Nowadays huge amounts of data are constantly created in various kinds of applications like medicine, engineering, science and industry. Due to the increasing size and complexity of the data, an effective data analysis is still a challenge. One approach to do so is to create a visualization in such a way that important properties and correlations of the data become visible in an intuitive way. The lecture gives an overview of the most important concepts and algorithms in data visualization. Advances and limitations of data visualization are discussed. In particular the lecture focuses on four data classes: multivariate data (spatial and temporal), volume data, flow data, and tensor data.

Literature:

• H. Schumann; W.Müller: Visualisierung – Grundlagen und allgemeine Konzepte, Springer-Verlag, Heidelberg, 2000 (in German)
• Proceedings of IEEE Visualization Conferences 1990-2005
• Proceedings of EuroVis/VisSym 1999-2005
• K. W. Brodlie et al. (eds.): Scientific Visualization - Techniques and Applications, Springer 1992
• R. A. Earnshaw, N. Wiseman (Eds): An Introductory Guide to Scientific Visualization, Springer, 1992
• P. Keller, M. Keller: Visual Cues, IEEE Computer Society Press, 1993
• L. Rosenblum, u.a. (ed): Scientific Visualization, Academic Press, London, 1994
• H. Hearnshaw, D. Unwin: Visualization in Geographical Information Systems, John Wiley & Sons, Chichester, 1994
• G.M. Nielson; H.Hagen; H.Müller: Scientific Visualization, IEEE Computer Society Press, Los Alamitos, 1997

  Format: Slides in pdf-format.

  Course material

  1. slides