There’s
lots of advice online about sensors and pixel sizes and so on, but
one of the best tips I’ve seen in a while is on the Adimec
blog.
The
accurately-but-not-very-concisely-titled, “Evaluating Machine
Vision Cameras versus Comparing Camera Specification Documents”
talks about measuring the photon transfer curve, which sounds like
hard work, but what it boils down to is this: choose
the camera that works best in your application.
Easy
to say, I know, but what I believe Adimec are getting at is that the
camera is just part of a system. That system includes the lighting,
the enclosure, the lens, and of course, the object you’re looking
at. So you put the camera in that environment and find out how well
it works.
Now
as a good engineer, you want to evaluate performance on the basis of
measurable criteria. But I think many machine vision people, (myself
included,) have a tendency to look at a pair of images from two
different cameras and pick the one that “looks” best. And this is
where the photon transfer curve comes in to play.
I’m
not going to regurgitate all the info about this that’s already on
the Adimec website – use the link above and you’ll find it all –
but I do urge you to learn more and figure out how to use it. Or if
you want to go to the source, look through my list of “Friends”
and go to Harvest Imaging
Maybe
it is more work, but it gives you data as a basis for selecting your
camera. Or just go with the vendor that offers the best freebies.
1 comment:
If there was no budget, choosing the "best" would be optimal. However if simply the second best camera would do the job with 50-90% lower price, which one would you choose..
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