What will I learn on this module?
This module will give you a good understanding of the mathematical principles and practical implementation of computer vision systems. You will be taught the fundamental mathematics, statistics, and machine learning algorithms that allow you to build computer vision systems to gain insight into the contents of images and videos.
You will develop a computer vision system that is designed for a specific application, for example the detection and classification of speed limit signs for an autonomous car, the detection and recognition of a face for secure access systems, or the detection and classification of human activity in a video. You will follow rigorous engineering principles to build the system architecture and adopt a holistic and proportionate approach to mitigate security risks in system running. You will also be required to conduct independent learning of state-of-the-art computer vision techniques and gain the knowledge to continuously improve the system performance. Furthermore, you will need to evaluate the environmental and societal impacts of the solution you have built and recommend measures to minimise adverse outcomes.
How will I learn on this module?
You will learn the core material from a series of lectures and lab sessions that cover the fundamentals of computer vision systems and how they are programmed using current practices. The labs will provide you with progressive hands-on experience of developing and programming computer vision systems.
How will I be supported academically on this module?
You will be supported by formal lectures and interactive lab sessions as well as supplementary material and reading lists from the University’s e-learning platform.
What will I be expected to read on this module?
All modules at Northumbria include a range of reading materials that students are expected to engage with. Online reading lists (provided after enrolment) give you access to your reading material for your modules. The Library works in partnership with your module tutors to ensure you have access to the material that you need.
What will I be expected to achieve?
Knowledge & Understanding:
ML01. Demonstrate a wide-ranging knowledge and understanding of computer vision systems including the fundamental techniques, tools, design, development and testing of these systems.
ML02. Demonstrate an understanding of user, professional, ethical, environmental, social, and economic issues and risks surrounding the design, development, implementation, and operation of computer vision systems.
ML03. Demonstrate a critical knowledge and understanding of the software engineering principles required when developing computer vision systems.
Intellectual / Professional skills & abilities:
ML04. Demonstrate computational thinking in terms of the analysis and design of computer vision systems. This should include the selection of available approaches i.e., methods, theories, tools, and algorithms.
Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
ML05 – Demonstrate independent self-learning of computer vision literature and state-of-the-art research as well as the ability to use the gained knowledge to continuously improve computer vision systems.
How will I be assessed?
The summative assessment will be a detailed research report, accounting for 100% of the final mark, synthesising all skills acquired through developing a practical computer vision system. This involves coding a program for a chosen vision-based dataset and conducting an independent review of pertinent scholarly works. The report should adhere to the structure of a scholarly research article, detailing the methodology, design decisions, performance of the created model, and an analysis of the results. It should also include a critical reflection on the project's research questions and the development process, offering insights into potential improvements or optimizations for future efforts. The report must also consider the project’s impact on the environment, society, and ethics, detailing any actions taken to mitigate associated security concerns. Furthermore, it should incorporate a thorough risk assessment, pinpointing risks, identifying project-associated risks and the strategies employed to mitigate them..The word limit of the report will be 3500 words. You will receive both informative and confirmatory feedback on your assessments.
This assessment addresses Module Learning Outcomes – ML01, ML02, ML03, ML04 and ML05
On an on-going basis you will also receive formative feedback on exercises you are required to complete in lab sessions.
Pre-requisite(s)
N/A
Co-requisite(s)
N/A
Module abstract
This module will teach you the theory, principles, and practice behind the development of computer vision systems. Indicative topics include: introduction to computer vision, machine learning techniques, image representation and transformation, image classification, object detection, semantic segmentation, image generation, video understanding, multi-modal vision, and 3D vision. The taught theory will be complimented by research rich learning as well as computer vision system development. You will apply what you have learned in the lectures, labs and your own research to an assessed piece of work where you will design and develop a computer vision system that is able to understand semantic contents in image data. The module aims to provide you with some experience of programming computer vision systems as implemented in industry and so enhance your employability in that field.
Course info
UCAS Code G407
Credits 20
Level of Study Undergraduate
Mode of Study 3 years Full Time or 4 years with a placement (sandwich)/study abroad
Department Computer and Information Sciences
Location City Campus, Northumbria University
City Newcastle
Start September 2025
All information is accurate at the time of sharing.
Full time Courses are primarily delivered via on-campus face to face learning but could include elements of online learning. Most courses run as planned and as promoted on our website and via our marketing materials, but if there are any substantial changes (as determined by the Competition and Markets Authority) to a course or there is the potential that course may be withdrawn, we will notify all affected applicants as soon as possible with advice and guidance regarding their options. It is also important to be aware that optional modules listed on course pages may be subject to change depending on uptake numbers each year.
Contact time is subject to increase or decrease in line with possible restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors if this is deemed necessary in future.
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