Module Specification

Advanced Computer Vision

London school of INNOVATION

Module Specification

Advanced Computer Vision: Unveil Advanced Techniques in Cutting-Edge Computer Vision


Computer Vision is concerned with processing images with computers to understand images like human vision does. The computer vision system can output high-level information about the contents of an image, including detecting objects in a single image and tracking them across a stream of images. During this module, students will look at different imaging systems and some of the well-established methods for image analysis. They will consider image processing techniques based on modern deep neural networks. They will look at image enhancement and restoration, image classification and feature engineering, convolutional neural network architectures, and facial recognition. They will learn how to track multiple objects in image streams, and the role of GAN architecture in computer vision. 


Code Number of Credits ECTS Credits Framework HECoS code
CV72 30 15 FHEQ - L7 computer vision (100968)

Learning outcomes

Code Attributes developed Outcomes
LO1 Knowledge and Understanding Conceptual understanding of digital imaging systems and visual representations across various spectrum technologies.
LO2 Intellectual Skills Critically analyse computer vision principles and their use in deep learning technologies.
LO3 Intellectual Skills Evaluate advanced object detection methods and generative models for real-time computer vision applications.
LO4 Technical/Practical Skills Design and apply image enhancement and restoration techniques using spatial filters and transformations.
LO5 Technical/Practical Skills Develop convolutional neural network architectures for image processing and classification tasks.
LO6 Professional/Transferable Skills Design ethical AI models and cloud infrastructure for deep learning solutions.

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
I LO2
I LO3
T LO4
T LO5
P LO6
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
I LO2
I LO3
P LO6

Student workload

Activity Total hours
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
300.00

Prerequisites and Co-requisites

  • Prerequisite: Applied Machine Learning 1

Content Structure

Week Chapter Name Chapter Description
Week 1 Digital Imaging Systems In this chapter, students will unravel the complexities of digital imaging systems across the electromagnetic spectrum and explore how computer vision is utilised across various fields such as healthcare and autonomous vehicles. Students begin to understand how different sensing arrays are applied and the impact they have in real-world scenarios.
Week 2 Visual Representation Focus turns to the technicalities of digital image construction, interpreting pixel composition, colour representations, and the manipulation of bit planes. Students gain foundational knowledge crucial for processing and analysing visual information in both theoretical and practical contexts.
Week 3 Image Enhancement and Restoration This chapter covers methods to enhance image quality, from noise reduction to detail sharpening, using various transformations and spatial filters. Students will grasp the procedural transformations needed to optimise visuals for further processing and gain proficiency in improving image analysis accuracy.
Week 4 Segmentation Strategies Students will delve into the crucial task of image segmentation, learning how to dissect images into meaningful sections, which is essential for semantic understanding. The chapter introduces approaches ranging from threshold-based to clustering and recursive methods.
Week 5 Image Classification and Feature Extraction This chapter centres on assigning images to predefined categories and extracting distinguishing characteristics using methods such as HOG and SIFT. Students will learn to navigate the end-to-end process of feature engineering within the computer vision pipeline.
Week 6 Convolutional Neural Networks Students investigate Convolutional Neural Networks in detail, understanding their role in image processing and the construction of CNN models, including popular architectures like AlexNet, VGGNet, and ResNet. Essential elements such as vision transformers are also discussed for their feature extraction capabilities.
Week 7 Object Detection The chapter introduces object detection in computer vision, examining the two-stage detector process and reviewing RCNN networks, focusing on the employment of bounding boxes and object classification. Students learn precision object localisation within images through hands-on training.
Week 8 Two-Stage Detectors Explored Building upon the two-stage detection models, this chapter presents the methods to enhance their efficiency, and students explore progressive advancements from RCNNs to faster and more sophisticated detectors like Masked RCNNs and their roles in image segmentation.
Week 9 YOLO (You Only Look Once) Framework The YOLO (You Only Look Once) methodology for object detection is unpacked, showing how it revolutionises the task by executing bounding box prediction and classification simultaneously. The chapter provides insights into how YOLO facilitates real-time detection in computer vision applications.
Week 10 Innovations in Generative Modelling The final chapter explores generative models, particularly GANs, and their innovations in synthetic image generation. Additionally, it presents the vital concept of Explainable AI, teaching students how to imbue transparency within AI processes, crucial for reliability and ethical considerations.

Module References

Type Description
Book Gonzalez, Rafael C., and Richard E. Woods. "Digital Image Processing", 4th edition, Pearson, (2017)
Book Shanmugamani, Rajalingappaa. "Deep Learning for Computer Vision: Expert Techniques to Train Advanced Neural Networks using TensorFlow and Keras' Packt Publishing Ltd, (2018)
Video The video is focused on CNNs and their application for Computer Vision

Methods of teaching/learning


Introductory lecture (1.50 hours)

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.


Concept learning (knowledge graph) (36.00 hours)

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 (18.00 hours)

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 (27.00 hours)

The 9 weekly sessions following the introduction (weeks 2 to 10) will be dedicated to teaching the contents of the module during interactive workshops. These sessions will complement the theory with practice, experience or analysis. Their purpose is to advance the student's cognition from 'knowledge' to 'understand' and 'apply'.

Depending on the nature of the content, challenges and learning activities will be pre-designed to apply flipped learning, and may include hands-on project work, group discussions or debates, roleplay, simulation, case study or other presentation, 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. There will be an opportunity also for Q&A in every session.


Independent reading, exploration and practice (153.50 hours)

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 (64.00 hours)

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.

Programmes this module appears on

Programme Term Type
1 AI and Machine Learning (MSc) 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: Unveil Advanced Techniques in Cutting-Edge Computer Vision (CV72)