Image Acquisitionand Proces

8
Machine Vision
A generally accepted deÞnition of machine vision is “…the analysis of images to
extract data for controlling a process or activity.” Machine vision is a subdiscipline
of artiÞcial intelligence that uses video cameras or scanners to obtain information
about a given environment, and to extract data from digital images about objects in
the image. Machine vision takes an image in and outputs some level of description
about the objects in it (i.e., size, position, correlation, etc.). Although applications
for machine vision vary, there are generally only a few main categories:
• Quality assurance
• Sorting
• Material handling
• Robot guidance
• Calibration
Quality assurance represents probably the largest number of machine vision
applications in use today. All that quality assurance machine vision systems do is
inspect an image and decide whether the part should pass or fail, based on a
predetermined set of rules. Although humans can be excellent quality assurance
inspectors, vision systems are appropriate when images need to be inspected at
high speed (often production lines require part inspection rates up to many complex
parts per second), and for monotonous work. Vision systems do not become bored,
or lack concentration; therefore, quality assurance can be much more highly
controlled. Conversely, human inspectors can think and possibly expand or change
the pass/fail rules — this can be both advantageous and disadvantageous. If the
human Þnds a ßaw in the product that is not part of the pass/fail criteria, then it
can be added. On the contrary, humans can interpret rules incorrectly, often leading
to quality assurance issues.
As its name suggests, sorting is the categorization of parts. Vision systems can
detect part types based on predeÞned criteria and sort the parts accordingly. This
detection can also be an extension of quality assurance, where pass and fail parts
are segregated, and the production process continues without human interaction.
Sorting application examples include the sizing of apples in a packing plant, the
removal and remelting of excessively eccentric extruded copper tubes and the scrapping
of Þber Bragg gratings that do not Þt a bandwidth template.
Materials handling in hazardous environments can be simpliÞed and automated
using machine vision. Often, materials need to be handled quickly, and in areas that
do not conform to desirable working conditions for humans. When considering the
storage of products, warehouses populated by humans need to be well lit, and often
use climate control to keep workers comfortable, which could be inappropriate for
the product. Depending on the system used, machine vision systems can require
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