Knowledge · Cameras

Selecting cameras for Computer Vision

Camera selection starts with geometry and task, not megapixels. Use sensor type, optics, lighting, FOV and triggering to shortlist hardware with confidence.

Industrial cameras for Computer Vision

Start here

Geometry first: line scan or area scan?

This hub covers full camera selection after geometry is set. Start with the fork below: decide whether your scene is a continuous web or belt, or a discrete field of view per cycle. That choice drives sensor geometry, encoder needs and lens strategy.

Line scan vs area scan

The selection fork: continuous motion vs discrete FOV, encoder sync and when each geometry wins on the factory floor.

Optics and illumination

Lens and lighting are part of the sensor

Resolution on the datasheet means nothing if the lens does not deliver the FOV and contrast your algorithm needs. Pair lens, lighting and triggering with the camera geometry you chose above.

Industrial lenses for machine vision

Beyond hardware

Camera chosen? Pick your vision architecture

Sensor geometry is step one. Step two is how you process images: smart camera vs PC, classical rules vs AI, 2D vs 3D for robot vision. The vision architecture guide links those decisions without repeating sensor basics.

Go to vision architecture

Ready to compare camera SKUs?

Browse our camera catalog or share your FOV, interface and cycle-time targets. We help you match geometry to available models.