Applied Image Processing

Module No. UEP053HM

Tutor: Dr. Mel Smith

Aims of the course

  • To offer a practical introduction to applied image processing, for a potential system user/integrator.
  • To provide an understanding of the importance of a visual sense, within the manufacturing cycle.
  • To explore the justification for applied image processing.
  • To consider the interfacing of vision equipment in a computer integrated manufacturing environment eg: RS232, robotic guidance, data acquisition, closed loop/process control.
  • To provide an awareness in potential improvements in safety, reliability, flexibility and product quality, within the manufacturing process.
  • To consider economic justifications for vision applications.

How is this module assessed?

  • (a) A 2.5 hour examination
  • (b) A single assignment
  • Assessment Weighting
    Examination = 60%
    Assignment = 40%
  • Referral: Students may be referred in either element, and be required to submit further assignment work.
  • Second Attempt: Attendance is a requirement for a second attempt at this module.

Learning outcomes

  • The student will be able to demonstrate a detailed critical understanding of the importance of scene constraints, selection of appropriate optics, and effective lighting, in the context of the application of image analysis to realistic tasks.
  • The student will be familiar with, and be able to specify in detail, the hardware and software components of an advanced vision system.
  • The student will be able to independently design and evaluate a practical working solution to an advanced vision application problem.
  • The student will be able to critically review the latest research, and be able to discuss potential industrial applications, as well as independently evaluate advanced issues relating to systems interfacing to complex items of associated equipment within the operating environment.

Further reading

  • Computer Vision, D Ballard, C Brown, 1982.
  • Digital Image Processing and Computer Vision, R Schalkoff, 1989.
  • Introduction to Robotics, A J Critchlow, 1985.
  • Machine Vision, Automated Visual Representations and Robot Vision, D Vernon , 1991.
  • Journal of Advanced Imaging, Cygnus Publishing, ISSN 1042-0711
  • Image Processing Europe Journal, UK Industrial Vision Association, Pennwell Publishing Co.
  • The Image Processing Handbook (2nd Ed.) by J.C.Russ, CRC Press, ISBN 0-8493-2516-1, 1995.
  • M. L. Smith, R. J. Stamp, The automatic visual inspection of textured ceramic tiles, submitted for publication in the journal: Computers in Industry , January 1999.

Course content

  • An appreciation of hardware elements: determination of appropriate lighting, camera, optical configuration, frames-store, resolution v field of view, monochrome v colour.
  • Image acquisition and display: photosensitive devices, digitisation.
  • Image preprocessing: brightness/contrast enhancement, standard mappings: negation, thresholding, sharpening, smoothing.
  • Image segmentation: edge detection, boundary detection.
  • Feature extraction: area, perimeter, shape descriptions.
  • Interfacing and data collection.
  • Typical applications: component identification, quality control, security, medical applications, robot guidance.
  • Management appraisal: why vision, safety and reliability, quality, flexibility, economic justification.

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