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?
Engineers at Fuel Cell Energy have found a way to take advantage of a side reaction, unique to their carbonate fuel cell that has nothing to do with energy production, as a potential, cost-effective solution to capturing carbon from fossil fuel power plants.
To get to a trillion sensors in the IoT that we all look forward to, there are many challenges to commercialization that still remain, including interoperability, the lack of standards, and the issue of security, to name a few.
This is part one of an article discussing the University of Washington’s nationally ranked FSAE electric car (eCar) and combustible car (cCar). Stay tuned for part two, tomorrow, which will discuss the four unique PCBs used in both the eCar and cCars.
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