Study information

Computer Vision and Project - 2025 entry

MODULE TITLEComputer Vision and Project CREDIT VALUE30
MODULE CODECOMM426Z MODULE CONVENERDr Sareh Rowlands (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated)
DESCRIPTION - summary of the module content
How do we recognise objects and people? How can we catch a ball? How do we navigate our way from our desk to the coffee machine, without bumping into each other? These seemingly simple tasks have represented a challenge for AI scientists for decades. Recent developments in computer vision have seen significant improvement in important applications (face detection in cameras, body tracking, and autonomous cars).  
 
Research Project: In this module, you will work on a research problem in an area relating to your programme of study, applying the tools and techniques that you have learned throughout the modules of the programme. This is an independent project, supervised by an expert from the relevant area, and culminates in writing a dissertation in the form of a research paper, describing your research and its results. 
AIMS - intentions of the module

This module will provide you with the fundamentals of computer vision, covering the essential challenges and key algorithms for solving a variety of vision problems. The course will provide both theoretical grounding in the relevant theories and a blend of classical and state-of-the-art approaches to computer vision problems. The course will focus on practical applications of computer vision and cover a broad range of problems, from low-level image processing to object recognition, tracking and 3D vision.

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

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

Module Specific Skills and Knowledge

1. Explain key computer vision problems and their mathematical formulation
2. Design and implement vision algorithms in a high-level language

Discipline Specific Skills and Knowledge

3. Analyse and propose solutions for computer vision problems
4. Select appropriate statistical representations, features and algorithms to suit problem specificities

Personal and Key Transferable / Employment Skills and Knowledge

5. Understand and appreciate the limitations of the state-of-the-art
6. Critically read and report on research papers

 

SYLLABUS PLAN - summary of the structure and academic content of the module

The course will cover the following topics:

Image formation: geometry, light, and cameras

Image processing: convolution, linear filters, image gradients, geometric transformations

Feature extraction & matching: corners, edges, blobs, and lines; feature descriptors (SIFT), feature matching and tracking

Object detection and recognition: K-NN, bag-of-words, scanning windows & Viola-Jones

Image segmentation: active contours, graph cuts

Dense image correspondences: dense motion estimation, optical flow

Shape reconstruction: 2D and 3D shape modelling and reconstruction, 3D morphable models

3D vision:  calibration, structure from motion

Deep learning for vision: neural networks, convolutional neural networks, recurrent neural networks

Applied deep learning: object detection, semantic and instance segmentation

Video understanding:  videos and action recognition

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 66 Guided Independent Study 234 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning & Teaching activities 66 Asynchronous online learning activities, skill-based exercises and practical work
Guided independent study 200 Project work
Guided independent study 34 Background reading and self-study Including preparation for online content, reflection on taught material, wider reading and completion of assessments 

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Practical exercises, weekly assigned work/exercises/forum discussion at the end of each sub-session and the end of week activities 1 hour per week All Written feedback provided summarising performance and key areas for improvement 
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Project task (practical work and report) 70 96 hours, code submission All Written feedback and model
Quiz 30 4 hours 1, 3-6 Written feedback via ELE
         
         
         

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Project task (practical work and report) Project task (practical work and report) (70%) All Referral/deferral period
Quiz Quiz (30%) 1, 3-6 Referral/deferral period
       

 

RE-ASSESSMENT NOTES
RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

  • Bishop, John C., Pattern recognition and machine learning, Springer, 2006.             
  • Forsyth, David & Jean Ponce, Computer vision: a modern approach, 2nd Pearson, 2011.
  • Szeliski, Richard, Computer vision: algorithms and applications, 2nd Springer, 2021.

Web based and Electronic Resources:

Other Resources:

Reading list for this module:

There are currently no reading list entries found for this module.

CREDIT VALUE 30 ECTS VALUE 15
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING Yes
ORIGIN DATE Tuesday 30th September 2025 LAST REVISION DATE Wednesday 1st October 2025
KEY WORDS SEARCH Computer vision, object recognition and detection, semantic and instance segmentation, tracking, pattern recognition, deep learning.

Please note that all modules are subject to change, please get in touch if you have any questions about this module.