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Module Descriptor School of Computer Science and Statistics

Module CodeCS4053
Module NameComputer Vision
Module Short TitleN/a
ECTS5
Semester TaughtFirst semester
Contact Hours

Lecture hours: 23 hours
Lab hours: 1 hour
Tutorial hours: 9 hours

Total hours: 33 hours

Module PersonnelDr. Kenneth Dawson-Howe
Learning Outcomes

When students have successfully completed this module they should be able to:

  • design solutions to real-world problems using computer vision.
  • develop working computer vision systems using C++.
  • critically appraise computer vision techniques.
  • explain, compare and contrast computer vision techniques.
Learning Aims

The aim of this module is to give students a firm understanding of the theory underlying the processing and interpretation of visual information and the ability to apply that understanding to ubiquitous computing and entertainment related problems. It provides them with an opportunity to apply their problem-solving skills to an area which, while it is firmly part of computer science/engineering, draws strongly from other disciplines (physics, optics, psychology). The module is based around problems so that the technology is always presented in context and during some tutorials students work in groups to design solutions to real world problems using the techniques that they have been taught. In addition, the module has a significant practical component so that students can appreciate how difficult it can be to apply the technology.

Module Content

Specific topics addressed in this module include:

  • image digitisation and colour;
  • camera modelling;
  • binary image processing including mathematical morphology, connected components analysis;
  • video analysis;
  • geometric image transforms;
  • noise and smoothing;
  • edge based processing including edge detection, contour extraction and representation;
  • feature processing including basic corner detection techniques and SIFT/SURF;
  • recognition techniques including template matching, statistical pattern recognition, and the Hough transform;

Topics will change a little bit from year to year.

Recommended Reading List

A Practical Introduction to Computer Vision with OpenCV, by Kenneth Dawson-Howe, Wiley, May 2014.


Image Processing, Analysis and Machine Vision. Milan Sonka, Vaclav Hlavac & Roger Boyle, Thompson, Third Edition 2008.


OpenCV 2 Computer Vision Application Programming Cookbook by Robert Laganière, PACKT Publishing, 2011.

Module Prerequisites

A working knowledge of C++

Assessment Details

The overall (annual) mark in this module is a weighted average of the written annual examination (80%) and the coursework (20%),  The coursewokrk consists of a number of computer vision programming assignments (done using C++ & OpenCV) during the semester. In the written examination students must answer 2 out of 3 exam questions.

The supplemental mark in this module is based only on the written supplemental examination.  This examination has one mandatory question which draws on the coursework and will require student to write C++ & OpenCV code.  In addition, similar to the annual written examination, students must answer 2 of the other 3 questions.

Module Website
Academic Year of Data2018/19