CSU44053 – Computer Vision

Module CodeCSU44053
Module Name Computer Vision
ECTS Weighting [1]5 ECTS
Semester TaughtSemester 1
Module Coordinator/s  Dr. Kenneth Dawson Howe

Module Learning Outcomes

On successful completion of this module, students will be able to:

  1. Design solutions to real-world problems using computer vision;
  2. Develop working computer vision systems using C++;
  3. Critically appraise computer vision techniques;
  4. Explain, compare and contrast computer vision techniques.

Module Content

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.

Specific topics addressed in this module include:

  • Image digitisation and colour;
  • Camera modelling;
  • Binary image processing;
  • Region based processing including connected components analysis, watershed segmentation and mean shift segmentation;
  • Video analysis;
  • Geometric image transforms;
  • Edge based processing including edge detection, contour extraction and representation;
  • Feature processing including basic corner detection techniques and SIFT;
  • Recognition techniques including template matching, statistical pattern recognition, and the Hough transform;
  • Topics will change somewhat from year to year.

Teaching and Learning Methods

Material is presented through lectures with around 4 pre-recorded lecture sessions provided per week. At the end of each topic a small mini-test is given to ensure that students are understanding and remaining engaged with the module material. Each week a live online Q&A session is scheduled to briefly review the week’s material and to deal with any questions which may arise.

Group tutorials are run roughly once per week in live online sessions and are used to get the students making use of the material to solve real world problems. Groups of 3 or 4 students are given 10-15 minutes to solve a problem and then groups propose solutions to the class which are discussed (in terms of issues/appropriateness).

Students are also asked to solve real problems in the assignments, so that the difficulties of applying the technology can be better understood.

Assessment Details

Assessment ComponentBrief Description Learning Outcomes Addressed% of TotalWeek SetWeek Due
Examination2 hour in-person examination (or real-time online examination if in person examinations are not possible) where students must answer 2 out of 3 questions.LO1, LO3, LO450%N/AN/A
Mini-testsSmall quizzes to be completed after studying the prerecorded lecture session in an area. Students must submit a meaningful attempt at least 7 out of the 14 mini-tests in this module.LO3, LO410%Week 1-11Week 1-11
Assignment where students are asked to do certain tasks using Open CV to provide familiarity with the platform.L020%Week 2Week 3
Computer Vision Problem Solving Assignment, including design, implementation, evaluation and report writing. Students must submit a meaningful attempt at this assignment.LO1, LO2, LO3, LO430%Week 4Week 8
Computer Vision Problem Solving Assignment, similar to an exam question(s)where a report has to be written describing how to solve the problem(s) including details of how the techniques work.LO1, LO3, LO410%Week 9Week 11

Reassessment Details

Examination (3 hours, 100%).

This exam will be an in-person examination (or real-time online examination if in person examinations are not possible). 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/or other material. In addition, similar to the annual written examination, students must answer 2 of the other 3 questions.

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by: 33 hours
Lectures (a mixture of prerecorded & live)22 hours
Laboratory0 hours
Tutorial or seminar11 hours
Other0 hours
Independent Study (outside scheduled contact hours), broken down by:80 hours
Preparation for classes/exams and review of material40 hours
Completion of assessments (including examination, if applicable)40 hours
Total Hours113 hours

Recommended Reading List

  • A Practical Introduction to Computer Vision with OpenCV, by Kenneth Dawson-Howe, Wiley, May 2014.
  • Image Processing, Analysis and Machine Vision by Milan Sonka, Vaclav Hlavac & Roger Boyle, Thompson, Third Edition 2008.

Module Pre-requisites

Prerequisite modules: N/A

Other/alternative non-module prerequisites: Competence in C++. Competence in advanced mathematics.

Module Co-requisites


Module Website


Link to classes for the first two weeks (for students who may wish to switch to this module)