|Co-ordinator||Dr Frederic Labrosse|
|Other staff||Dr Frederic Labrosse, Mr David J Smith, Dr Yonghuai Liu|
|Course delivery||Practical||2 x 2 hours|
What is visualisation and what is it for. Different types of data and different types of software packages.
2.Data acquisition (2 lecture)Where does the data come from.
3.The process of visualisation (2 lecture)
The visualisation pipe-line. Different types of data lead to different types of visualisation. Data enrichment.
4.One-dimensional scalar data (3 lecture)
Interpolation, extrapolation, visualisation.
5.Two-dimensional scalar data (3 lectures)
Regular data, scattered data, triangulation, interpolation, iso-contours, visualisation.
6.Three-dimensional scalar data (3 lectures)
Voxelisation, interpolation, slicing, iso-surfaces, visualisation.
7.3D rendering (1 lecture)
Volume rendering, shading, texture mapping.
8.Vector fields (3 lectures)
Different types of flow. Flow visualisation of 2 and more components vector fields, Different techniques: particle-based, image based, critical point.
9.Information visualisation (3 lectures)
Comparison, causality, context. Quantitative versus qualitative. Univariate, multivariate. Plots, graphs, glyphs.
|Problem solving||Thinking through and designing an experiment to visualise data from a set of available tools involves the application of problem solving skills.|
|Research skills||Students will have to research some algorithms not described at length during the lectures.|
|Communication||Written communication will be developed through the writing of a practical session report.|
|Improving own Learning and Performance||Written communication will be developed through the writing of a practical session report.|
|Information Technology||Students will be exposed to a new (with a rather unusual graphical interface) and fairly difficult to use software package.|
This module is at CQFW Level 6