TNM067 - Scientific Visualization

Prel. scheduled hours: 48
Rec. self-study hours: 112

Area of Education: Technology

Main field of studies: Media Technology, Computer Engineering, Information Technology

Advancement level (G1, G2, A): A

The goal for this course is to provide the student with deep insights into methods for visualization of scientific data from experiments and simulations. The applicability of the various methods is shown through practical programming exercises. The knowledge gained is applicable in several existing and emerging applications in industry and the public sector, but can also form the foundation of research and development in scientific visualization both within academia and specialized companies. Upon completion of the course the student should be able to:
For a given data set choose an appropriate visualization method.
Design and implement a visualization tool using the chosen. method and available software toolkits.
Read and present the content in scientific papers in the field.

Prerequisites: (valid for students admitted to programmes within which the course is offered)
Computer Graphics, Physical modeling

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshhold requirements for progression within the programme, or corresponding.

The course is composed of lectures and laboratory assignments. Scientific papers will also be included as self-study material.

Course contents:
Introduction to visualization: visualization as a research field, applications, tasks
Visualization pipeline
Data representation and interpolation:
Basic data types: Scalar, vector and tensor data
Structured and unstructured data
Basic visualization algorithms
for scalar fields, e.g. color mapping, contour lines and surfaces
for vector fields, e.g. flow lines and surfaces and time animation of these
for tensor fields, e.g. glyphs, tensor lines
Overview of techniques for volume rendering
Introduction to concepts for more advanced visualizations data analysis
data exploration
feature extraction
topological methods
Examples of some application specific visualization techniques

Course literature:
Telea, A. C., Data Visualization: Principles and Practice, AK Peters, Ltd., 2008
Selected scientific papers
Additional related literature:
Munzner, T., Visualization Analysis and Design, Taylor & Francis, 2014
The Visualization Toolkit, An Object-Oriented Approach To 3D Graphics, 3rd edition, ISBN 1-930934-12-2

Oral examination
Laboratory work