Syllabus for Scientific Visualisation
A revised version of the syllabus is available.
- 5 credits
- Course code: 1TD389
- Education cycle: Second cycle
Main field(s) of study and in-depth level:
Computer Science A1N,
Computational Science A1N
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
- Established: 2008-03-18
- Established by: The Faculty Board of Science and Technology
- Revised: 2015-11-11
- Revised by: The Faculty Board of Science and Technology
- Applies from: Spring 2016
120 credits including Computer Programming I and Scientific Computing II. Scientific Computing II may be replaced by Numerical Methods and Simulation, 5 credits, Scientific Computing, Bridging Course, 5 credits, or Scientific Computing and Calculus, 10 credits.
- Responsible department: Department of Information Technology
To pass, the student should be able to
- describe the data flow in a visualisation system;
- outline the methods that transform the data and information to visual representations;
- use and program advanced software for various visualisation techniques.
Scientific Visualisation is an area concerned with the visualisation of large and complex data sets, where the data might come from experiments or computations. Visualisation is a way, in many cases the only possible way, to achieve insight and knowledge.
Discrete models. Volume rendering: ray-tracing, splatting, texture based. Isosurface reconstruction. Transformation of discrete volume data to polygonal representations. Mesh topologies and mesh simplification. Visualisation techniques. Visual aspects based on perception. Particle rendering. Algorithms for programmable graphics hardware. Applied visualisation. The course includes projects such as programming in VTK (the Visualisation Toolkit).
Lectures, laboratory work and compulsory assignments.
Written examination at the end of the course. Passed laboratory course and approved compulsory assignments are also required.
- Latest syllabus (applies from Autumn 2022)
- Previous syllabus (applies from Spring 2019)
- Previous syllabus (applies from Spring 2018)
- Previous syllabus (applies from Spring 2016)
- Previous syllabus (applies from Autumn 2010)
- Previous syllabus (applies from Autumn 2009)
- Previous syllabus (applies from Autumn 2008)
Applies from: Spring 2018
Some titles may be available electronically through the University library.
Reading list revisions
- Latest reading list (applies from Spring 2018)
- Previous reading list (applies from Spring 2016)