| Mode(s) of Study | Code | CATS Credits | ECTSCredits | Framework | HECoSCode |
|---|---|---|---|---|---|
| Flexible online | CV72O | 30 | 15 | FHEQ - L7 | computer vision |
Prerequisites and Co-requisites
- Prerequisite: Applied Machine Learning
Learning Outcomes
| Code | AttributesDeveloped | Outcomes |
|---|---|---|
| LO1 | Knowledge and Understanding | Analyse and articulate the applications of deep learning architectures, such as CNNs and Vision Transformers, in computer vision. |
| LO2 | Knowledge and Understanding | Demonstrate a comprehensive understanding of computer vision feature detection methods, including SIFT, SURF, and ORB. |
| LO3 | Intellectual Skills | Critically evaluate feature detection and image segmentation techniques in complex, dynamic environments. |
| LO4 | Technical/Practical Skills | Design and implement digital vision systems using Mask R-CNN, YOLO, and Faster R-CNN for real-world applications. |
| LO5 | Professional/Transferable Skills | Apply ethical considerations to the design and deployment of computer vision systems across diverse professional contexts. |
Student Workload
The methods of teaching and learning for this module are based on LSI's Technical 30 teaching system, consisting of the following activities.
| Activity | TotalHours |
|---|---|
| Introductory lecture | 1.50 |
| Concept learning (knowledge graph) | 36.00 |
| AI formative assessment | 18.00 |
| Workshop/Lab Sessions | 27.00 |
| Independent reading, exploration and practice | 153.50 |
| Summative assessment | 64.00 |
| Total: | 300.00 |
Teaching and Learning Methods
| Activity | Description |
|---|---|
| Introductory lecture | This first weekly session features a specially recorded video by the module leader for online learners, providing a comprehensive introduction to the module. The video outlines the significance of the subject and its interrelation with other modules. No preparation is required from students prior to this session. The module leader will also provide a structured breakdown of the content to be covered in the subsequent 10 sessions. Students will access a dedicated learning portal, which will include essential reference materials and suggestions for supplementary reading. The format and criteria for the summative assessment will be clearly outlined to ensure understanding of expectations. The video will also explain how the interactive learning platform utilises generative AI tools to enhance the learning experience, offering tailored resources and adaptive quizzes. A discussion board will promote peer interaction, allowing students to share insights and ask questions, while weekly reflection prompts will encourage critical thinking and deeper engagement with the material. A dedicated section for submitting queries will ensure timely responses from the module leader. This structured approach promotes a high-quality academic experience, supporting student engagement and learning in an online environment. |
| Concept learning (knowledge graph) | Our institution's approach to teaching is primarily based on flipped learning. Ahead of each weekly session (Workshop/Lab), students will be required to study the essential concepts that are used in the coming session so they are familiar with the theories and ideas related to that session. The study material will be in the form of written content, illustrations, pre-recorded lectures and tutorials, and other forms of content provided through the AGS. This content is self-navigated by the students, accommodating different learning styles and schedules, allowing students to watch or listen to them at their own pace and review them as needed. |
| AI formative assessment | Once each concept of the theory is studied, students will be prompted to engage in formative assessment with instant AI feedback. They include multiple-choice questions, socratic questions and answers, written questions, role-play and other AI-assisted practice scenarios. The purpose of this automated formative assessment is to provide students with immediate feedback on their understanding of module material and highlight any areas that need support or further study. They are also used to track student progress, boost motivation and promote accountability. |
| Workshop/Lab Sessions | For those studying in the self-paced online mode, live classes will be replaced by a set of flexible, self-directed activities designed to be completed independently. These include AI-led simulations, interactive videos, reflective prompts, and real-world case studies, all aimed at deepening understanding and fostering critical, independent thinking. AI avatars will simulate a classroom environment, allowing students to interact with virtual instructors for guidance or discussion, creating a more immersive experience. This structure promotes a high-quality, autonomous learning experience, equipping students with skills essential for both academic and professional success. Additionally, a dedicated feature for submitting questions to the module leader will provide timely responses and support. |
| Independent reading, exploration and practice | This activity challenges students to engage with the reference material and independently explore and analyse academic literature related to the course topic. Students are expected to select relevant sources, practice critical reading skills, and where applicable technical skills, and synthesise information from multiple references. This is an opportunity to enhance research abilities, critical thinking, and self-directed learning skills while broadening and deepening subject knowledge. |
| Summative assessment | Summative assessments are used to evaluate student learning at the end of a module. These assessments can take many forms, including exams, papers, or presentations. Instructors can use summative assessments to measure whether students have achieved the learning outcomes for the module and provide them with a sense of their overall progress. Summative assessments can also be used to evaluate the effectiveness of the teaching methods used in the module. |
Assessment Patterns
| Weighting | Format | Outcomes assessed |
|---|---|---|
| 40 |
Invigilated Exam
This is a time-limited examination undertaken during the summative assessment period under the School’s remote invigilation conditions to support quality and academic integrity. The examination enables students to demonstrate achievement of the module learning outcomes, particularly in relation to knowledge and understanding, and where relevant professional and transferable skills. Questions may include problems, issues or dilemmas reflecting situations students could encounter in professional practice. Students are expected to synthesise and apply their learning to analyse the issues and reach sound, reasoned judgements. To support preparation, formative assessment activities, including quizzes, dialogues and an AI-augmented assignment, are built into the module. Students receive actionable feedback from AI, staff and peers to help improve future work and develop their ability to give constructive feedback to others. |
I LO3 K LO1 K LO2 P LO5 |
| 60 |
Technical Analysis and Solution Assessment Coursework
This coursework assessment requires students to develop a solution to a complex problem in a simulated context, then analyse and reflect on the solution, the decisions they made, and the theory underpinning their approach. It assesses students’ ability to develop practical solutions, critically evaluate their work, and demonstrate their understanding of the module’s technical and theoretical concepts. |
I LO3 K LO1 K LO2 P LO5 T LO4 |
References/Indicative Reading List
| Importance | ISBN | Description |
|---|---|---|
| Core Textbook | 9781000401028 | Chowdhary, C. Thippa Reddy, G. Parameshachari, B. Computer Vision and Recognition Systems: Research Innovations and Trends. Apple Academic Press, 2022. |
| Core Textbook | 9781040029442 | Atiqur Rahman Ahad, M. Mahbub, U. Turk, M. Hartley, R. Computer Vision: Challenges, Trends, and Opportunities. Chapman and Hall/CRC, 2024 |
| Supplementary Reading | Use Python downloads to install Python on your computer and prepare for practical programming activities. Choose the installer for your operating system, set up your coding environment and begin creating, running and testing Python programs. | |
| Supplementary Reading | 9781788295628 | Shanmugamani, Rajalingappaa. Deep Learning for Computer Vision: Expert Techniques to Train Advanced Neural Networks using TensorFlow and Keras. Packt Publishing Ltd, 2018 |
| Supplementary Reading | Howse, J. and Minichino, J. (2020) Learning OpenCV 4 Computer Vision with Python 3. 3rd edn. Packt Publishing | |
| Supplementary Reading | 9780262048972 | Torralba, Antonio, Phillip Isola, and William T. Freeman. Foundations of Computer Vision. MIT Press, 2024 |
| Supplementary Reading | 9781800201774 | Hafsa, Asad. The Computer Vision Workshop. Packt Publishing, 2020 |
| Supplementary Reading | 9781788297684 | Dadhich, Abhinav. Practical Computer Vision. Packt Publishing, 2018 |
| Supplementary Reading | Use JupyterLab to write code, explanatory notes and outputs together in interactive notebooks. Run Python step by step, inspect results, create visualisations and organise practical experiments in a flexible workspace installed directly on your computer. | |
| Supplementary Reading | Dey, S. (2018) Hands-On Image Processing with Python: Expert Techniques for Advanced Image Analysis and Effective Interpretation of Image Data. Birmingham: Packt Publishing | |
| Supplementary Reading | Antiga, L., Stevens, E., Huang, H. and Viehmann, T. (2026) Deep Learning with PyTorch: Training and Applying Deep Learning and Generative AI Models. 2nd edn. Manning | |
| Supplementary Reading | Use the Python Tutorial to build programming skills through clear explanations and worked examples. Follow topics progressively, practise syntax and concepts, then apply what you learn directly when writing and testing your own Python code. | |
| Supplementary Reading | Timsina, P. (2024) Building Transformer Models with PyTorch 2.0. BPB Publications. | |
| Supplementary Reading | Use OpenCV-Python tutorials to practise image processing and computer vision through guided examples. Work with images, transformations, features and visual analysis techniques, then adapt the example code to strengthen understanding through direct experimentation and observation. | |
| Supplementary Reading | Use PyTorch Tutorials to develop deep learning skills through practical examples covering tensors, neural networks, training and model workflows. Follow the examples, run code and adapt techniques as you build confidence using PyTorch in practice. | |
| Supplementary Reading | 9781634857901 | Alexander, Sherri. Computer Vision & Simulation: Methods, Applications & Technology. Nova Science Publishers, 2016 |
| Supplementary Reading | Use the OpenCV-Python package to process images and video directly in Python. Load visual data, transform images, detect features, analyse shapes and build practical computer vision workflows within your own scripts, notebooks and project activities. | |
| Supplementary Reading | 9781292223049 | Gonzalez, Rafael C., and Richard E. Woods. Digital Image Processing. 4th edition, Pearson, 2017 |
| Supplementary Reading | Ayyadevara, V.K. and Reddy, Y. (2024) Modern Computer Vision with PyTorch: A Practical Roadmap from Deep Learning Fundamentals to Advanced Applications and Generative AI. 2nd edn. Birmingham: Packt Publishing | |
| Supplementary Reading | 9781483294599 | Wechsler, Harry. Computational vision. Elsevier, 2014 |
| Supplementary Reading | Use torchvision alongside PyTorch to work with computer vision datasets, model architectures and image transformations. Prepare images, access established models and build practical pipelines for classification, detection, segmentation and other vision-based deep learning project tasks. | |
| Supplementary Reading | Use PyTorch setup guidance to install PyTorch for your computer and chosen Python environment. Configure the appropriate installation, confirm everything works correctly and prepare the tools needed to build, train and test deep learning models. | |
| Supplementary Reading | 9783030343712 | Szeliski, Richard. Computer vision: algorithms and applications. Springer Nature, 2022 |
Module Review and Approval
| Version | Date | ReviewedBy | NextReview |
|---|---|---|---|
| 1.0 | dd MMMM yyyy | Dr Vimal Dwivedi | October 2027 |