Image Processing (521H3)

15 credits, Level 7 (Masters)

Spring teaching

This module gives you 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 the concepts, starting with basic operations and finish with state-of-the-art deep learning architectures that enable computers to identify, track and understand objects in the real world. 

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 pattern matching, key point descriptors and deep learning convolutional neural networks.

Topics include:

• an introduction to image processing and elements of computer vision
• camera technologies, lenses for machine vision, image formation and resolution
• de-noising images
• histogram manipulations
• linear invariant systems in two dimensions, the discrete convolution operator
• first- and second-order differential edge detection operators, edge-filling techniques
• the Hough transform
• scene segmentation methods and morphological operators
• colour transforms
• pattern recognition techniques: shape descriptors, Fourier descriptors, template matching, SIFT
• common classification methods for object recognition
• examples of computer vision and image processing systems in industry.

 

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 2024/25. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum.

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