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that, ideally, a camera with multiple lighting configurations could be installed and the vision system works out the best combination of imaging parameters for itself. Deep learning, on the
other hand, Williamson noted, is great for recognising defects – but not for doing measurements, which is part of what traditional machine vision systems do. Te other area of
growing importance to how machine vision operates and therefore how it is sold, said Williamson, is the advances being made in algorithms. ‘Going back to the 1990s, a smart camera had such a small processor that it could only do very basic processing, which meant
Amazon for machine vision isn’t going to happen any time soon, because … customers that are most happy engage more with … services
the algorithms were poor and it wasn’t reliable. Nowadays a smart camera is using the most advanced algorithms possible, so it can perform as well as a PC-based vision system. Tat’s why a lot of applications – such as hyperspectral systems, for example – are going from PC to smart camera.’ Te ability to put highly competent algorithms on smart cameras is also opening up new applications that Williamson said were impossible 10 years ago. Hyperspectral imaging is a good example of an
upcoming application for Williamson, because it historically required a specialised skill set. Tis, in turn, meant a high entry-level cost, simply to cover the research to set it up. ‘Now, with deep
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