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Unit information: Image Processing and Computer Vision (Teaching Unit) in 2020/21

Please note: you are viewing unit and programme information for a past academic year. Please see the current academic year for up to date information.

Unit name Image Processing and Computer Vision (Teaching Unit)
Unit code COMS30030
Credit points 0
Level of study H/6
Teaching block(s) Teaching Block 1 (weeks 1 - 12)
Unit director Professor. Mirmehdi
Open unit status Not open

COMS10014 Mathematics for Computer Science A and COMS10013 Mathematics for Computer Science B or equivalent.

COMS10016 Imperative and Functional Programming and COMS10017 Object-Oriented Programming and Algorithms I or equivalent programming experience.

COMS20010 Algorithms II and COMS20011 Data-Driven Computer Science or equivalent.


EITHER Assessment Units COMS30032 Image Processing and Computer Vision (Exam assessment, 10 credits).

OR COMS30031 Image Processing and Computer Vision (Coursework assessment, 20 credits).

Please note:

COMS30030 is the Teaching Unit for the Image Processing and Computer Vision option.

Single Honours Computer Science students can choose to be assessed by either examination (10 credits, COMS30032) or coursework (20 credits, COMS30031) by selecting the appropriate co-requisite assessment unit.

Any other students that are permitted to take the Image Processing and Computer Vision option are assessed by examination (10 credits) and should be enrolled on the co-requisite exam assessment unit (COMS30031).

School/department Department of Computer Science
Faculty Faculty of Engineering


The aim of this unit is to give you an introduction to computational vision: the theory, principles, techniques, algorithms and applications. The unit is structured in terms of topics, each associated to a lecture, a follow-up seminar, laboratory sessions and self-study. For each topic, we will cover the underlying theory, the practical challenges, important algorithms and example applications. Practical implementation work will be conducted individually, with lab support, using C/C++ and OpenCV

Intended learning outcomes

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

  1. Demonstrate understanding of and be able to apply basic theoretical concepts and practical techniques used in image processing and computer vision.
  2. Have knowledge of and be able to apply key image analysis and manipulation software.
  3. Develop software solutions for image analysis applications.

Teaching details

Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions, problem sheets and self-directed exercises.

Teaching will take place over Weeks 1-7, with coursework support in weeks 8-10 and for students assessed by examination, consolidation and revision sessions in Weeks 11 and 12.

Assessment Details

Examination details:

January timed assessment (100%, 10 credits)


Coursework details:

Coursework, to be completed over weeks 8-10. (100%, 20 credits)

Reading and References

  • Forsyth, David A., Ponce, J. Computer Vision: A Modern Approach (2003).
  • Sonka, Milan, Hlavac, Vaclav and Boyle, Roger, Image Processing, Analysis, and Machine Vision (Cengage Learning, 2014)