Computational Imaging Methods (G6087)

15 credits, Level 6

Autumn teaching

On this module, you’ll develop your knowledge and understanding of recent methodological developments for image analysis and reconstruction. We’ll describe a variety of common use-cases, and discuss limitations of current approaches and open challenges.

Key topics include:

  • principles and methods for inference in computational models of imaging data
  • approaches for standard computer vision tasks such as segmentation, detection and tracking
  • generative models and their application for tasks in image synthesis and analysis
  • 3D image reconstruction for photographic and medical imaging.

A range of relevant machine learning and statistical analysis techniques will be introduced as we discuss each of these topics.

You’ll be exposed to a range of applications across photographic and biomedical imaging domains. You’ll learn to develop and critique potential solutions for different problems.

This module has prerequisite requirements of prior training in fundamentals of machine learning or statistical modelling, relevant mathematics (linear algebra, probability, optimisation) and programming in a suitable language.

Teaching

25%: Lecture
50%: Practical (Laboratory)
25%: Seminar

Assessment

100%: Coursework (Report)

Contact hours and workload

This module is approximately 150 hours of work. This breaks down into about 44 hours of contact time and about 106 hours of independent study. The University may make minor variations to the contact hours for operational reasons, including timetabling requirements.

We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We鈥檙e planning to run these modules in the academic year 2026/27. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum.

We鈥檒l make sure to let you know of any material changes to modules at the earliest opportunity.

Courses

This module is offered on the following courses: