This document discusses 2D machine vision. It defines machine vision, computer vision, and image processing, noting their overlap. It explains that machine vision is used for automatic inspection and analysis in industry applications like quality control and robot guidance. Computer vision involves acquiring, processing, and understanding images to produce information for decisions. Image processing involves mathematical operations on images. The document outlines four main application areas for 2D machine vision: locating, measuring, inspecting, and identifying. It provides brief examples for each. Finally, it summarizes the typical steps and key elements involved in 2D machine vision systems.

1. 2D Machine Vision
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2. What are we talking about in this module?
Machine
Vision
Image
processing
Computer
Vison
• The overlap between the following fields
– Machine Vision
– Computer Vision
– Image Processing
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3. What are we talking about in this module?
Machine
Vision
Image
processing
Computer
Vison
• Machine Vision
• Computer Vision
• Image Processing
Machine vision is the technology and methods used to provide
imaging-based automatic inspection and analysis for such
applications as automatic inspection, process control, and robot
guidance in industry. https://en.wikipedia.org/wiki/Machine_vision -
23-3-17
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4. What are we talking about in this module?
Machine
Vision
Image
processing
Computer
Vison
• Machine Vision
• Computer Vision
• Image Processing
Computer vision is a field that includes methods
for acquiring, processing, analyzing, and understanding images and,
in general, high-dimensional data from the real world in order to
produce numerical or symbolic information, e.g., in the forms of
decisions https://en.wikipedia.org/wiki/Computer_vision - 23-3-17
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5. What are we talking about in this module?
Machine
Vision
Image
processing
Computer
Vison
• Machine Vision
• Computer Vision
• Image Processing
Image processing is processing of images using mathematical
operations by using any form of signal processing for which the input
is an image, a series of images, or a video, such as a
photograph or video frame; the output of image processing may be
either an image or a set of characteristics or parameters related to
the image. https://en.wikipedia.org/wiki/Image_processing - 23-3-17
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6. Application Fields (1)
• We distinguish roughly 4 application fields for 2D machine Vision
– Locate (e.g. position & orientation)
• Determining position & orientation in regard to a
vision camera to pass data to a robot
– Measure (e.g. physical dimensions)
– Inspect (e.g. presence or absense)
– Identify (e.g. read codes/characters)
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7. Application Fields (2)
• We distinguish roughly 4 application fields for 2D machine Vision
– Locate (e.g. position & orientation)
– Measure (e.g. physical dimensions)
• Performing measurements/tolerance analysis on
(parts of) products
– Inspect (e.g. presence or absense)
– Identify (e.g. read codes/characters)
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8. Application Fields (3)
• We distinguish roughly 4 application fields for 2D machine Vision
– Locate (e.g. position & orientation)
– Measure (e.g. physical dimensions)
– Inspect (e.g. presence or absense)
• Checking whether or not a product is at the point
where you expect it to be
– Identify (e.g. read codes/characters)
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9. Application Fields (4)
• We distinguish roughly 4 application fields for 2D machine Vision
– Locate (e.g. position & orientation)
– Measure (e.g. physical dimensions)
– Inspect (e.g. presence or absense)
– Identify (e.g. read codes/characters)
• For example reading specific codes (like QR, barcodes
etc.) from a product
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10. 2D Machine vision steps (roughly)
• Imaging
– Take an image
• Processing and analysis
– Analyze the image to obtain a result
• Communication
– Send the result to the system in control of the process
• Action
– Take action depending on the vision system’s result
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11. Key elements 2D vision
Object
Lighting
Lens + sensor + electronics
Interface
Image
Image processing in software
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Camera
• In nearly all applications you’re trying to get as much contrast as
possible (to make it easier to determine for measuring/detecting
features in an image)