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COMP61342 Computer Vision syllabus 2021-2022

COMP61342 materials

COMP61342 Computer Vision

Level 6
Credits: 15
Enrolled students: 94

Course leader: Angelo Cangelosi

Additional staff: view all staff

Additional requirements

  • Pre-requisites

    Basic knowledge of linear algebra, basic calculus, programming experience (C/C++ or Matlab programming)

Assessment methods

  • 50% Written exam
  • 50% Coursework
Sem 2 w26-27,31-33 Lecture Engineering Building B 2B.020 Blended Theatre 2 Tue 09:00 - 12:00 -
Sem 2 w26-27,31-33 Lab 1.8+1.10 Tue 13:00 - 16:00 -
Sem 2 w26-27,31-33 ONLINE DROP-IN Tue 17:00 - 18:00 -
Themes to which this unit belongs
  • Making Sense of Complex Data


This course unit detail provides the framework for delivery in 20/21 and may be subject to change due to any additional Covid-19 impact. Current students should see Blackboard/course unit related emails for any further updates.

This unit will give students a foundation in the subject of machine vision. This will involve gaining familiarity with algorithms for low-level and intermediate-level processing and considering the organisation of practical systems. Particular emphasis will be placed on the importance of representation in making explicit prior knowledge, control strategy and interpretting hypotheses.

This course unit treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic and statistical approaches. As such, it will also give students a foundation in the statistical methods of image analysis.

Topics covered in the course include perception of 3D scene structure from stereo; image filtering, smoothing, edge detection; segmentation and grouping;  learning, recognition, and search; tracking and motion estimation; behaviour modelling.

Emphasis will also be placed on the importance of understanding algorithmic stability and optimality as a framework for algorithmic design and research methodology.

This course unit is designed for students that are interested in Computer Vision, Artificial Intelligence, or Machine Learning. This course unit is also appropriate for students with an interest in Computer Graphics.


  • To introduce the basic concepts and algorithmic tools of computer vision.
  • To introduce the problems of building practical vision systems.
  • To explore the role of representation and inference.
  • To explore the statistical processes of image understanding and develop an understanding of advanced concepts and algorithms.
  • To discuss novel approaches to designing vision systems that learn.
  • To develop skills in evaluation of algorithms for the purposes of understanding research publications in this area.

Teaching methods


1 day per week (5 weeks)

Feedback methods

The assessment for this course unit is based on a combination of coursework and a closed-book exam. The coursework consists of: reports on a set of practical assignments carried out using MATLAB, an essay based on reading a collection of journal papers and a group presentation on selected research papers. Feedback will be provided via moodle and in person after the group presentations.

Study hours

Employability skills

  • Analytical skills
  • Group/team working
  • Oral communication
  • Research
  • Written communication

Learning outcomes

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

  • Have an understanding of common machine vision algorithms.
  • Have a knowledge of the statistical design of algorithms.
  • Have a knowledge of the properties of image data and be able to solve problems about extraction of features and other quantitative information.
  • Be able to design basic systems for image analysis and evaluate and justify the design.
  • Be able to write a program for the analysis of image data and prepare a technical report on the evaluation of this program on suitable test data.
  • Be able to work effectively as a member of a group to prepare presentations describing complex machine vision algorithms to their peers.

Reading list

No reading list found for COMP61342.

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.