Lecture: Scientific Visualization


  • Lecturer(s):
  • Start: April 26, 2017
  • Dates: Wed. 10:30-12:00, B-IT 2.1; Fri. 10:30-12:00, B-IT Rheinsaal
  • Curriculum: B-IT Master Media Informatics , Master
  • Exams: TBA



To avoid overlap with the Media Informatics block course "Technical Writing", the SciVis lecture will only start on April 26.

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In science, but also in business, journalism, and many other fields, ever-increasing amounts of measured, simulated, collected and stored data have turned the ability to make sense of large and complex data into a key qualification. The goal of visualization is to produce interactive visual representations of data. It provides an intuitive, effective, and thus very popular means of quite literally gaining insight into complex phenomena.

This lecture introduces the main concepts and techniques of data visualization. It focuses on visualizing volumetric data from science and medicine, but also covers more abstract data such as networks or graphs, and conveys fundamental knowledge about color, human perception, and graphics programming. Its mixture of theoretical and practical programming exercises will train the ability to find effective solutions for real-world problems based on a clear theoretical foundation.

The topics of this lecture include: Color and perception, visualization design, rendering 3D volume data (such as medical CT or MRI data), extracting surfaces from volumetric data, visualizing and identifying meaningful structures in vector fields (such as flow fields from computational fluid dynamics) and tensor fields (as they arise in engineering and modern neuroimaging), visualizing high-dimensional data, dimensionality reduction, clustering, and uses of machine learning in visualization.

The course takes place in the B-IT building, Dahlmannstr. 2 (externHow to get there), in room 2.1 (on Wed) and Rheinsaal (on Fri).


Assignment Sheets

Exercise 1: Installation
Assignment sheet  (PDF document, 605 KB)


  • Alexandru C. Telea: Data Visualization - Principles and Practice, CRC Press, Second Edition, 2015
  • C.D. Hansen, C. Johnson: Visualization Handbook, Academic Press, 2004
  • B. Preim, C. Botha: Visual Computing for Medicine: Theory, Algorithms, and Applications, Morgan Kaufmann, 2014 (externofficially available online within University of Bonn)
  • K. Engel et al.: Real-Time Volume Graphics, A K Peters, 2006
  • C. Ware: Information Visualization. Perception for Design, Morgan Kaufmann, 3rd edition, 2013
  • M. Ward et al.: Interactive Data Visualization: Foundations, Techniques, and Applications. CRC Press, 2010