Machine Vision, 5 cr

Core content

• Machine vision in production automation: Typical applications (2D/3D). Typical system structures. Commonly used 3D imaging methods.
• Machine vision hardware:
  * Different system types (PC based system, smart camera based system)
  * Camera types and selection principles: Specifying camera resolution (field-of-view, spatial resolution) and resulting expected measurement resolution.
  * Lenses and other optical components: Specifying lens focal length.
  * Illumination in machine vision: Importance of illumination concerning the resulting image. Illumination methods and light sources.
• Machine vision software and image processing:
  Digital image. Typical functionality and special properties of machine vision software. Common programming concepts and methods in machine vision.
• Typical machine vision applications/tasks in production automation:
  Checking the presence/counting parts - methods
  Locating parts for robot pickup - robot and machine vision calibration
  Dimensional measurements - measurement accuracy and/or uncertainty
  

Complementary knowledge

• Typical color camera vs. grey-scale camera. Shutter types.
  Concepts of depth-of-focus/depth-of-field and optical resolution.
  Effect of different light colors (wavelengths).
  
• Understanding basic operating principles of the most commonly used machine vision software algorithms.
  Programming simple machine vision application.
• Communicating with other equipment.
  Calibrating machine vision system and combining camera and robot coordinates.
  Calculating measurement uncertainty.