Computer Graphics and Image Processing

Unit code: COMP27112
Credit Rating: 10
Unit level: Level 2
Teaching period(s): Semester 2
Offered by School of Computer Science
Available as a free choice unit?: Y



Additional Requirements

Students who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.


The importance of visual interfaces has never been greater. Graphical interfaces have become ubiquitous, from desk-top interaction, to games and three-dimensional virtual environments. In parallel, there has been an explosion in digital image processing and analysis. We take for granted digital photography and video, while our health services rely on digital X-ray systems, CT and MRI scanners for seeing inside our bodies. Meanwhile, the visualization of computer simulations is an essential aspect of product design and testing, genome exploration, drug design, and climate modelling. The demand for computer scientists with advanced knowledge of such areas has never been greater.

The theme will enhance your knowledge and understanding, answering such questions as:

  • How are three-dimensional environments represented in a computer, and how are interactive 3D worlds created?
  • How are 2D and 3D representations combined ? for example, how can we recover 3D geometry from 2D images?
  • How are the basic mathematical techniques and algorithms used to build useful applications?
  • How are images stored, processed and manipulated?
  • How can computers interpret images captured by cameras and other recording devices?


This course covers the fundamental principles of Computer Graphics, and Image Processing. These two separate, but related, disciplines play increasingly important roles in modern applied Computer Science. The course is lecture-based and is supported by a set of laboratory exercises and self-study coursework projects. We teach Graphics using OpenGL from C; for Image Processing we use MATLAB. This is a very popular course taken by most 2nd Year students in the School of Computer Science.

Teaching and learning methods


24 hours spread over 12 weeks

Examples classes

5 hours of assessed, self-study Coursework Assignments.


10 hours in total, 5 2-hour sessions.

Learning outcomes

Learning outcomes are detailed on the COMP27112 course unit syllabus page on the School of Computer Science's website for current students.

Employability skills

  • Analytical skills
  • Innovation/creativity
  • Project management
  • Problem solving

Assessment methods

  • Written exam - 75%
  • Written assignment (inc essay) - 5%
  • Practical skills assessment - 20%


Fundamentals (1 week)

2 and 3 D Coordinate systems. Vectors, matrices and basic vector/matrix operations.2 and 3 D geometric transformations (translation, rotation, scaling, affine).

3D Modelling and Illumination (5 weeks)


The camera model. Viewing and projection. Points, lines, B?zier curves. Polygons. Local illumination: ambient, diffuse and specular components. Interpolation: intensity (Gouraud) and normal vector (Phong). Surface detail: textures, bump mapping. Model structuring using scene graphs. Masters and instances. Inheritance of transformations and attributes.

Practical core

OpenGL (in C) laboratory exercises. Self-paced Coursework Assignments using example programs and software tools (with a small amount of experiment-driven programming).

Image Transformations (2 weeks)


Pixels, pixel values, grey level, spatial resolution, colour representations, image transformations: point transformations (windowing, histogram equalisation, colour transformations ? colour spaces).

Practical core

MATLAB programming, MATLAB exercises in image manipulation. Manipulating greyscale images. Matrix manipulation in MATLAB, image translation, rotation and scaling (bilinear interpolation), affine transformations.

Image Enhancement (3 weeks)


Local processes, convolution, image smoothing (local averaging, weighted averaging), size of support, Gaussian mask. Edge enhancement (unsharp masking). Edge detection (Prewitt, Sobel, Canny), Thresholding, blob detection, simple measurement (geometric features). Rank order filters (median, max-min).

Practical core

MATLAB exercises in image processing. Noise reduction. General image convolution code. Using the code to make an edge detector (Sobel, Prewitt), Combined smoothing and edge detection ? Scale.

Recommended reading

COMP27112 reading list can be found on the School of Computer Science website for current students.

Feedback methods

Face to face feedback and marking in programming laboratories.

Study hours

  • Assessment written exam - 2 hours
  • Lectures - 24 hours
  • Practical classes & workshops - 10 hours
  • Independent study hours - 64 hours

Teaching staff

Toby Howard - Unit coordinator

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