Image Processing (521H3)
15 credits, Level 7 (Masters)
Image Processing provides you with an introduction to advanced image processing and computer vision topics. Computer vision is increasingly used as a powerful method to enable computers to understand the world around them. It has applications in many areas including autonomous factory production, security, biomedical imaging, autonomous vehicles and robotics.
This module will introduce key concepts, starting with basic operations and progressing to state-of-the-art deep learning architectures that enable computers to identify, track and understand objects in the real world. It will consist of a series of lectures and project labs. In the labs, you will learn how to solve a real world problem using Matlab’s Image Processing toolbox.
Capturing a good quality image is an important first step so you will learn about the lens optics, camera technology, and noise removal processes. You will then cover medium level processes such as edge detection, segmentation, blob analysis and colour processing. Once these have been mastered you will study the higher level subjects such as pattern matching, key point descriptors and deep learning convolutional neural networks.
29%: Practical (Laboratory)
25%: Coursework (Software exercise)
75%: Examination (Unseen examination)
Contact hours and workload
This module is approximately 150 hours of work. This breaks down into about 28 hours of contact time and about 122 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’re planning to run these modules in the academic year 2022/23. However, there may be changes to these modules in response to COVID-19, staff availability, student demand or updates to our curriculum. We’ll make sure to let our applicants know of material changes to modules at the earliest opportunity.
This module is offered on the following courses: