Module Identifier CS34210  
Academic Year 2007/2008  
Co-ordinator Dr Frederic Labrosse  
Semester Semester 1  
Other staff Dr Frederic Labrosse, Mr David J Smith, Dr Yonghuai Liu  
Pre-Requisite CS20410  
Course delivery Practical   2 x 2 hours  
  Lecture   22 Hours.  
Assessment TypeAssessment Length/DetailsProportion
Semester Assessment 1 assignment (report on practical work) (approx 20 hours)  30%
Semester Exam2 Hours  70%
Supplementary Assessment Will take the same form, under the terms of the Department's policy.  100%
Further details  

Learning outcomes

On successful completion of this module students should be able to:
express a consolidated and extended understanding and knowledge of visualisation techniques.

compare, critically discuss and use different methods to visualise data and extract relevant information from the data set.

design and implement experiments in a scientific manner to visualise and extract relevant information from a data set.


Brief description

Many processes produce and/or use data, the interpretation of which is often difficult. This includes medical applications (e.g. radiographs and MRI scans), weather analysis and prediction, planetary exploration and robotics. Virtual reality is a very trendy way of visualising some kind of data, but is not the only way.The module will introduce the subject of scientific visualisation: data acquisition, processing of the data and finally visualisation and how this can help interpreting and understanding the data. The module will concentrate on using algorithms rather than developing them, although some will be described in detail. An understanding of graphics and image manipulation will be beneficial but not required.The module will offer practical work using leading visualisation software packages.


1.Introduction (2 lectures)

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.

Module Skills

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