Engineering and design
Module code: 521H3
Level 7 (Masters)
15 credits in spring semester
Teaching method: Laboratory, Lecture
Assessment modes: Coursework, Unseen examination
You will cover topics including:
- introduction to machine vision and relation to image processing
- camera technologies, lenses for machine vision, image formation and resolution, display technologies
- image acquisition hardware
- histogram manipulations
- linear invariant systems in two dimensions
- the convolution operation and its discrete implementation as mask operators
- first and second order differential edge detection operators, edge-filling techniques, Hough transform
- the 2D Fourier transform and frequency domain filters, 2D correlation
- scene segmentation methods and region filling
- pattern recognition techniques, shape descriptors, Fourier descriptors, template matching
- examples of machine vision systems in industry.
Module learning outcomes
- Design and critically analyse an optical camera system for capturing images and apply to processing the image for a given task.
- Design and critically assess methods for the segmentation of different types of image or video stream.
- Demonstrate systematic knowledge of the principles of two dimensional convolution operators and their relationship to image processing tasks.
- Design and critically analyse a image processing algorithm for a given real world task.