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machine vision (computer vision)

Machine vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion (ADC) and digital signal processing (DSP). The resulting data goes to a computer or robot controller. Machine vision is similar in complexity to voice recognition.

Two important specifications in any vision system are the sensitivity and the resolution. Sensitivity is the ability of a machine to see in dim light, or to detect weak impulses at invisible wavelengths. Resolution is the extent to which a machine can differentiate between objects. In general, the better the resolution, the more confined the field of vision. Sensitivity and resolution are interdependent. All other factors held constant, increasing the sensitivity reduces the resolution, and improving the resolution reduces the sensitivity.

Human eyes are sensitive to electromagnetic wavelength s ranging from 390 to 770 nanometers (nm). Video cameras can be sensitive to a range of wavelengths much wider than this. Some machine-vision systems function at infrared (IR), ultraviolet (UV), or X-ray wavelengths.

Binocular (stereo) machine vision requires a computer with an advanced processor. In addition, high-resolution cameras, a large amount of random access memory (RAM), and artificial intelligence (AI) programming are required for depth perception.

Machine vision is used in various industrial and medical applications. Examples include:

  • Electronic component analysis
  • Signature identification
  • Optical character recognition
  • Handwriting recognition
  • Object recognition
  • Pattern recognition
  • Materials inspection
  • Currency inspection
  • Medical image analysis

The term machine vision is often associated with industrial applications of a computer's ability to see, while the term computer vision is often used to describe any type of technology in which a computer is tasked with digitizing an image, processing the data it contains and taking some kind of action.

This was last updated in July 2016

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