Module Specification

Advanced Computer Vision

Module Specification

Advanced Computer Vision: Engineer advanced vision systems and impact industries



Advancements in deep learning have propelled computer vision to new heights, enabling machines to perform complex visual tasks with remarkable accuracy. This evolution has made computer vision a critical component in industries ranging from medical diagnostics to autonomous vehicles.

This module delves into feature detection methods such as SIFT, SURF, and ORB for shape and texture recognition, forming the foundation for advanced tasks. You will explore image segmentation and object detection using tools like Mask R-CNN, YOLO, and Faster R-CNN. The curriculum includes deep learning architectures like CNNs and Vision Transformers, and practical experience with frameworks such as TensorFlow and PyTorch. Real-time processing and multi-object tracking will prepare you to tackle dynamic environments.

By mastering these advanced techniques, you will be equipped to design, implement, and evaluate complex vision systems that address real-world challenges. The hands-on approach ensures you gain practical skills applicable to diverse industries. Engaging with case studies, you will understand the transformative potential of computer vision and position yourself at the forefront of innovation.


Mode(s) of Study Code CATS Credits ECTS Credits Framework HECoS code
Full-time Blended Learning
Part-time Blended Learning
CV72 30 15 FHEQ - L7 computer vision (100968)

Prerequisites and Co-requisites

  • Prerequisite: Applied Machine Learning

Learning Outcomes

Code Attributes developed 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.

Content Structure

Week Topic
Week 1 Introductory lecture
Introduces the module, outlining its relevance to the field and connections to other topics. It provides an overview of the content structure, key references, and assessment details.
Week 2 Computer Vision Fundamentals
Establish a strong foundation in computer vision to understand how machines perceive and interpret visual information. Appreciate the importance of enabling computers to analyse images and videos, setting the stage for learning advanced techniques that solve real-world problems. Explore the transformative impact of computer vision across various industries, fostering an appreciation for its potential to revolutionise technology.
Week 3 Feature Detection and Description
Master feature detection techniques like SIFT, SURF, and ORB to enable recognition of shapes and textures in images. Understand why detecting key features is critical for interpreting visual data and how it underpins more advanced computer vision tasks. Develop the ability to identify and describe significant elements within images, paving the way for complex analysis and recognition systems.
Week 4 Image Segmentation Techniques
Explore methods for dividing images into meaningful segments to understand their structure and content. Learn why segmenting images is essential for tasks like object detection and scene understanding. Gain insights into the importance of isolating regions of interest, which is foundational for developing sophisticated computer vision applications.
Week 5 Advanced Object Detection
Delve into cutting-edge object detection algorithms like Mask R-CNN, YOLO, and Faster R-CNN to detect and classify objects within images. Understand why accurate object detection is vital for applications ranging from autonomous vehicles to surveillance. Develop the skills to implement systems that can identify multiple objects in real time, addressing challenges in dynamic environments.
Week 6 Deep Learning Foundations
Discover the power of deep learning in computer vision, focusing on Convolutional Neural Networks (CNNs). Understand why deep learning has revolutionised image analysis and recognition tasks. Learn to leverage neural networks to extract complex features, enabling advanced capabilities in visual perception and interpretation.
Week 7 Vision Transformer Architectures
Explore transformer-based architectures like Vision Transformers to advance beyond traditional CNNs. Understand why transformers offer a novel approach to image analysis, capturing global relationships within visual data. Grasp the significance of these models in pushing the boundaries of what's possible in computer vision.
Week 8 Implementing Deep Learning Frameworks
Harness the power of frameworks like TensorFlow and PyTorch to implement complex deep learning models. Understand why these tools are essential for efficient development and experimentation in computer vision. Develop practical skills to build, train, and optimise neural networks, empowering you to bring sophisticated vision systems to life.
Week 9 Transfer Learning and Network Adaptation
Apply transfer learning to adapt pre-trained networks for new tasks with limited data. Understand why leveraging existing models accelerates development and improves performance in complex applications. Learn to fine-tune networks, saving time and resources while achieving high accuracy in specialised computer vision tasks.
Week 10 Real-Time Processing and Multi-Object Tracking
Equip yourself with techniques for processing video streams and data in real time, vital for applications like surveillance and autonomous systems. Understand why speed and latency are critical in dynamic environments. Learn to implement multi-object tracking, addressing the challenges of detecting and following multiple entities simultaneously in live settings.

References/Indicative Reading List

Importance ISBN Description
Core Textbook 9781788295628 Shanmugamani, Rajalingappaa. Deep Learning for Computer Vision: Expert Techniques to Train Advanced Neural Networks using TensorFlow and Keras. Packt Publishing Ltd, 2018
Core Textbook 9781800201774 Hafsa, Asad. The Computer Vision Workshop. Packt Publishing, 2020
Core Textbook 9781788297684 Dadhich, Abhinav. Practical Computer Vision. Packt Publishing, 2018
Supplementary Reading 9783030343712 Szeliski, Richard. Computer vision: algorithms and applications. Springer Nature, 2022
Supplementary Reading 9781493306084 Wechsler, Harry. Computational vision. Elsevier, 2014
Supplementary Reading 9780262048972 Torralba, Antonio, Phillip Isola, and William T. Freeman. Foundations of Computer Vision. MIT Press, 2024
Supplementary Reading 9781634857901 Alexander, Sherri. Computer Vision & Simulation: Methods, Applications & Technology. Nova Science Publishers, 2016
Supplementary Reading 9781292223049 Gonzalez, Rafael C., and Richard E. Woods. Digital Image Processing. 4th edition, Pearson, 2017

Student Workload

The methods of teaching and learning for this module are based on the School's Technical 30 teaching system, consisting of the following activities.

Activity Total hours
Introductory lecture

This is the first weekly session, dedicated to providing a comprehensive introduction to the module. The module leader will present an overview of the subject, elucidating its importance within various digital engineering professions and its interrelation with other modules. Students will need no preparation ahead of attending this session.

The module leader will provide a structured breakdown of the content to be covered in the subsequent 9 sessions. Students will also receive an outline of the essential reference materials, alongside suggestions for supplementary reading. The format and criteria for the summative assessment will be delineated, followed by a dedicated period for questions and answers.

A recording of the session will be available to facilitate async engagement for any other student who missed the class, also offering an opportunity to review the content again.

1.50
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.

36.00
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.

18.00
Workshop/Lab Sessions

Those studying in the blended learning mode will attend these 9 weekly classes (in person or remotely) during weeks 2 to 10. These sessions will complement the theory already studied during the preceding week (in our flipped-learning model), with discussions, analysis, practice or experience . They will be interactive and participatory, rather than one-way lectures. There will also be an opportunity for Q&A in every session. Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning. They may include hands-on project work, group discussions or debates, roleplay, simulation, case studies, presentations, and other learning activities and opportunities. These workshops present an opportunity to apply critical thinking and problem-solving skills. They also encourage collaboration and foster a sense of community among students.

27.00
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.

153.50
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.

64.00
300.00

Assessment Patterns

Weighting Format Outcomes assessed
60% Technical Analysis and Solution Assessment
This assessment requires students to develop a solution to a complex problem within a simulated domain, followed by a detailed analysis and reflection on their design and its theoretical underpinnings. The aim is to assess students' abilities to design practical solutions, critically analyse their work, and articulate their understanding of the technical and theoretical aspects of the module.
K LO1
K LO2
I LO3
T LO4
P LO5
40% Invigilated Exam
This is a time-limited and closed-book exam with a mix of multiple-choice and analytical written questions that students undertake during the summative assessment period as scheduled under the School’s remote invigilation conditions to ensure quality and academic integrity.

The exam enables the students to demonstrate their successful attainment of the module learning outcomes, primarily related to knowledge and understanding, and secondarily related to Professional/Transferable Skills.

The analytical written questions will consist of problem questions representing issues and dilemmas students are likely to encounter in professional life and students have to synthesise and apply what they have learnt on the module in order to produce sound and reasoned judgements with respect to the problem.

To enable the students to practice and prepare, various formative assessment activies, including quizzes and a AI-augmented assignments and mock exams are built into the module. Additionally, throughout the course, students will regularly receive feedback on their knowledge and assignments from AI as well as peers and staff to indicate how to improve future work and how to give constructive feedback to others.
K LO1
K LO2
I LO3
P LO5

Programmes this module appears on

Programme Term Type
1 MSc AI and Machine Learning 2 Optional
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change.
Module Spec: Advanced Computer Vision: Engineer advanced vision systems and impact industries (CV72)