That's a valid question. There's more than one way to create 3D in machine vision. Chuck's upcoming February feature, already out in the print edition of DN, discusses this subject. The simplest, easiest, cheapest method is by using two 2D cameras in stereo, as does this QuantumVision product. This roughly emulates the stereoscopic vision of humans (and other primates), in that both of our eyes used together creates 3D stereo images. Others use more complex math and/or special image sensors, and/or special image processing.
Basically, this is smaller than other stereo 3D cameras, and it's way smaller than other 2-camera 3D stereo cameras. Since it's a stereo 3D system, it's created with two 2D cameras, so there's really no new paradigm in that sense; you are still processing 2D data. You can process that data faster if you use the cameras independently. Another thing about this system is its rugged enclosure, which is why it's shown with water drops.
Ann, what's the use case for this type of system compared with a traditional 2D vision system or any of the stereo 3D systems? Am I saving money by going this route or is it purely a matter of increasing visibility without having to move to a totally new 3D paradigm?
Days after a massive, distributed denial-of-service attack took down dozens of major websites around the country, ARM Holdings plc is rolling out a pair of new processor architectures aimed at shoring up IoT security.
Dow Chemical and several other companies have launched a program in Omaha, Neb. to divert about 36 tons of plastics from landfills in its first phase, and convert it into energy used for cement production.
Both traditional automation companies and startups are developing technologies to improve processes on the factory floor, while smart sensors and other IoT-related technologies are improving how products are handled during transport and across the supply chain.
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