Engineering and design

Image Processing

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.