module specification

The module is designed to impart essential mathematical principles and concepts of computer vision alongside its practical applications. The module encompasses core topics in image formation and low-level image processing; mid-level scene representation; model-based description and tracking. Appropriate hardware/software tools will be integrated into the module to enable students to apply and test computer vision algorithms on real world data sets.

The objectives of this module are to:
• enable students to gain understanding of essential concepts of computer vision algorithms and systems
• develop students' expertise in analysing, designing, and testing vision algorithms and systems
• train students in using appropriate hardware/software tools for solving common computer vision problems
• prepare students with postgraduate level research and report writing skills

Same as the entry requirement of MSc Robotics with Artificial Intelligence course.
Available for Study Abroad? (YES)

Introduction to Computer Vision (CV) and basic concepts: image formation, light, colour and human visual perception, geometric transformation, geometric camera models, review of basic mathematics, high level insight of CV applications [LO1];

Pre-processing: image representation, pixels, point and neighbourhood operations, filtering, histogram, transforms, mathematical morphology, segmentation [LO1, LO2, LO3];

Features: edge, corner, colour, texture, boundary and shape, scale invariant feature transform (SIFT) [LO1, LO2, LO3];

Recognition: introduction to pattern recognition, linear regression, decision function and elementary statistical decision theory, classifier, parameter estimation, clustering, dimensionality reduction, template matching [LO1, LO2];

Advanced concepts, applications, and challenges: convolutional neural networks (CNN), deep learning, motion estimation and object tracking, gesture recognition and challenges, real world problems and limitations of CV [LO4].

The module will be delivered by a mixture of lectures, tutorials/workshops, synchronous/asynchronous e-Learning/ blended learning. Students will be expected to carry out directed independent background study to familiarise themselves with the platforms and tools that will be used during the module.

All required learning material for the module (lectures/ tutorials/ workshops/ recordings / links etc) will be available to students on University VLE (Weblearn). The module page on VLE will be continuously updated with announcements and need based additional information to ensure appropriate support for students’ learning.
Students will be encouraged to keep logbook for reflective learning and regular feedback.

At the end of this module you should be able to:
LO1. Understand and apply underpinning mathematics and/or physics governing computer vision algorithms/systems;
LO2. Demonstrate sound understating of the theory and operation of computer vision algorithms/systems, and a critical awareness of current problems and new insights;
LO3. Use software/hardware and modelling tools to analyse, implement selected aspects of computer vison algorithms/systems;
LO4. Develop postgraduate level skills in literature review, critical evaluation of results and report writing by exploring advanced topics and/or recent related to computer vision algorithms/systems.
LO5. Show awareness of legal, social, ethical and professional (LSEP) issues particularly important in computer vision algorithms and systems.

Reading list link: https://rl.talis.com/3/londonmet/lists/33741659-132C-7459-7F50-B91DFD0A690A.html?lang=en-GB&login=1

Core Text:
1. Richard Szeliski (2021). Computer Vision: Algorithms and Applications.
2. E. R. Davies (2017). Computer Vision: Principles, Algorithms, Applications, Learning, Academic Press.
3. Gonzalez et al (2008). Digital Image Processing.
http://catalogue.londonmet.ac.uk/record=b1556623~S1
4. Sonka, Hlavac & Boyle (2015). Image Processing, Analysis and Machine Vision.
http://catalogue.londonmet.ac.uk/record=b1677110~S1
5. Oge Marques (2011). Practical image and video processing using MATLAB, Wiley-IEEE Press. http://catalogue.londonmet.ac.uk/record=b1672029~S1

Other Reading:
• Christopher M. Bishop (2006). Pattern recognition and machine learning.
http://catalogue.londonmet.ac.uk/record=b1576490~S1
• Hafsa Asad, Vishwesh Ravi Shrimali, et al. (2020). The Computer Vision Workshop: Develop the skills you need to use computer vision algorithms, Packt Publishing.
• Sergios Theodoridis et al.(2010). Introduction to pattern recognition: a MATLAB approach. http://catalogue.londonmet.ac.uk/record=b1609582~S1
• Wesley E. Snyder, Hairong Qi (2010). Machine Vision, Cambridge University Press. http://catalogue.londonmet.ac.uk/record=b1765736~S1
• Jonathan Fernandes (2019). Introduction to Deep Learning with OpenCV, AV Presentation from LinkedIn Learning http://catalogue.londonmet.ac.uk/record=b2268222~S1
• Chris Solomon, Toby Breckon (2011). Fundamentals of digital image processing : a practical approach with examples in Matlab http://catalogue.londonmet.ac.uk/record=b1681274~S1
• Stockman and Shapiro (2001). Computer Vision.

Research Databases:
IEEE Xplore: http://catalogue.londonmet.ac.uk/record=b1572993~S1
ACM Digital Library: http://catalogue.londonmet.ac.uk/record=b1251310~S1
Wiley Online Library: http://catalogue.londonmet.ac.uk/record=b1585296~S1
London Met Library: https://student.londonmet.ac.uk/library/

Journals:
IET Computer Vision: https://ietresearch.onlinelibrary.wiley.com/
IEEE Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing,
IEEE Transactions on Computational Imagining
ELSEVIER Pattern Recognition
Springer Internal Journal of Computer Vision: http://catalogue.londonmet.ac.uk/record=b2045879~S1
IPSJ Transactions on Computer Vision and Applications: https://ipsjcva.springeropen.com/
Visualization in Engineering: https://viejournal.springeropen.com/

LinkedIn Learning: https://www.linkedin.com/learning/me?u=57118729