Computer Vision

Module Identifier
CS34020
Module Title
Computer Vision
Academic Year
2026/2027
Semester
Semester 1
Co-ordinator
Professor Bernard Tiddeman
Reading List
View on Aspire
Pre-Requisite
CC12320 or CS12320
Exclusive (Any Acad Year)

Replacement for CS34110
Other Staff
Dr Wayne Aubrey

Assessment

Assessment Type

Assessment details

Proportion
Semester Assessment Computer vision assignment: 30 Hours 50%
Semester Exam Written Exam: 2 Hours Written exam, with limited open notes, based on a topic indicated in advance. 50%
Supplementary Assessment Computer vision assignment: 30 Hours 50%
Supplementary Exam Supplementary Exam: 2 Hours Will take the same form, under the terms of the Department's policy. 50%

Learning Outcomes

On successful completion of this module students should be able to:

  1. Express a consolidated and extended understanding and knowledge of Computer Vision techniques.
  2. Compare, critically evaluate and discuss competing methods.
  3. Explain the problems, techniques and difficulties associated with the different areas of Computer Vision.

Brief description

The module will introduce the subject of Computer Vision with applications in a variety of contexts, including robotics, security and image analysis. It will start with low-level vision such as edge detection, feature detection, and segmentation. Intermediate vision will describe various techniques to infer 3 dimensional information from images. Some high-level techniques will be introduced, leading to discussion of deep learning approaches.

Aims

The aim of the module is provide a grounding in applied computer vision, including real-time and low-level techniques through to high-level and deep learning approaches. It is intending to prepare students for major projects and employment opportunities utilising computer vision techniques.

Content

Foundations of vision: Image acquisition, sources of noise, human visual perception, and the evaluation and design of visual computing systems.

Edges and features: The image as landscape, edge detection, feature detection and representation, appearance as feature.

Motion: The video as a 3D dataset, feature tracking, background subtraction, modelling motion and change.

Objects: Grouping features, grouping motion, modelling variability. Learning models.

3D: Shape from X (shading, defocus, occlusion, photometric stereo). Multiview techniques (binocular, structure from motion). Direct 3D capture techniques (lidar, sonar).

Deep learning: Convolutional neural networks, attention mechanisms and Transformer architectures, generative networks, loss functions and training schemes.

Module skills

Skills type

Skills details
Application of Number Computer vision involves higher level mathematical concepts
Communication Exam writing develops written communication skills
Improving own Learning and Performance Independent learning is necessary to complete the module
Information Technology Information and communications technology is intrinsic to computer science.
Personal Development and Career planning There is a substantial demand for computer vision expertise.
Problem solving Problem solving is intrinsic to computing in general.
Research skills This is a research driven module; research skills will be exercised throughout
Subject Specific Skills Computer vision.
Team work This module will require individual rather than team work.

Notes

This module is at CQFW Level 6