Study information

Introduction to Computer Vision - 2025 entry

MODULE TITLEIntroduction to Computer Vision CREDIT VALUE15
MODULE CODECOMM042 MODULE CONVENERUnknown
DURATION: TERM 1 2 3
DURATION: WEEKS 12
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).
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

Topics in this module will include:

Image formation: geometry, light, and cameras

Image processing: convolution, linear filters, Fourier transforms, 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, Markov random fields, graph cuts

Dense image correspondences: dense motion estimation, optical flow, stereo

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

3D vision: 3D pose estimation, calibration, structure from motion, SLAM, shape from shading, motion capture 

Deep learning for vision: neural networks, convolutional neural networks, object detection, semantic and instance segmentation, recurrent neural networks

LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 33 Guided Independent Study 117 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled Learning & Teaching activities 22 Lectures
Scheduled Learning & Teaching activities 11 Workshops/tutorials
Guided independent study 48 Coursework preparation
Guided independent study 69 Wider reading and self study

 

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
       
       
       
       
       
SUMMATIVE ASSESSMENT (% of credit)
Coursework 70 Written Exams 30 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework: workshop code 70 48 hours, code submission All Written feedback and model answers
Quiz 30 2 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
Coursework: workshop code (70%) Coursework: workshop code (48 hours, code submission, 70%) All Referral/deferral period
Quiz (30%) Quiz (2 hours, 30%) 1, 3-6 Referral/deferral period
       

 

RE-ASSESSMENT NOTES
Reassessment will be by coursework/quiz in the failed or deferred element only. For referred candidates, the module mark will be capped at 50%. For deferred candidates, the module mark will be uncapped.
 
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

Web based and Electronic Resources:

CVOnline: an online compendium of computer vision techniques: http://homepages.inf.ed.ac.uk/rbf/CVonline/

Website of R. Szelinski’s Computer Vision book (including a free electronic version of the book): http://szeliski.org/Book 
 

 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Set Bishop, John C. Pattern recognition and machine learning Springer 2006
Set Forsyth, David & Jean Ponce Computer vision: a modern approach 2nd Pearson 2011
Set Szeliski, Richard Computer vision: algorithms and applications 2nd Springer 2021
CREDIT VALUE 15 ECTS VALUE 15
PRE-REQUISITE MODULES COMM109
CO-REQUISITE MODULES
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Wednesday 16th April 2025 LAST REVISION DATE Wednesday 16th April 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.