Glossary Updates12 new terms added to the glossaries · October 2, 2026, 22:44 CEST
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Glossary · Machine vision

Image acquisition

Also known as: Image capture, Image grabbing

German: Bilderfassung

In machine vision, image acquisition is the process of capturing an image and making it available to the processing software, including triggering, exposure, sensor readout and transfer over the camera interface.

  • Machine vision

In one sentence

Image acquisition captures an image and transfers it to the processing software, covering trigger, exposure, readout and interface.

Example

A light barrier triggers image acquisition when a part reaches the inspection position, and the strobe fires in sync with the exposure.

How it applies

  • Engineering: Acquisition is the first step of the vision chain: trigger, lighting, exposure, readout and transfer. Standards such as GenICam, GigE Vision and USB3 Vision standardize camera control and data transfer. Hardware triggers give more predictable timing than software triggers sent over the network.
  • Commissioning: Trigger timing, strobe synchronization and part position are checked with the running line; many vision problems are acquisition problems.
  • Documentation: The documentation team should document trigger sources, delays, lighting control and interface settings, and whether images carry a Timestamp or part ID for traceability.

Image acquisition vs. image processing

Acquisition produces the image; Image processing extracts information from it. No algorithm compensates for a poorly acquired image.

By knowledge.aitechdoc.world · Published September 26, 2026 · Last reviewed

Source: AI TechDoc Blog editorial definition, based on machine vision practice

Definitions follow the cited standards and specifications. Where a source is a copyrighted publication, such as an ISO, IEC or EN standard, the definition is a close paraphrase, not a verbatim quotation, so as not to infringe copyright. We recommend reading the original publication. The sections “How it applies” are editorial commentary by AI TechDoc Blog and are not part of any standard.

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