Right, that's stereo 3D, using two or more 2D cameras. There are other methods for achieving 3D in machine vision, though, done with a single camera using, for example, image triangulation as I mention briefly here:
Just wanted to address your comment on stereo vision for cameras. To be spacially aware, all you need is two pictures taken from different positions. To get that, you can have two cameras spaced at a known distance and compair synced frames. OR, the computer looks at two successive frames and, knowing the speed of the car, calculates all of the distances in the critical part of the scene to the function running. Typically a function like collision aviodance.
As all our parts get smaller, 3D MV should see a real upswing in usage. Machine placement tolerances are getting so low (5um), 3D will be required to compensate for temperature variations in the parts and equipment.
Thanks for the detailed reply, Craig. Your description of automotive 3D sounds like it's the type where multiple 2D cameras create stereo images that make up 3D images. There are some other methods used in industrial MV that are more complex and costly. And 2D is not always sufficient for MV, which is why there's more 3D happening there.
The reason I guessed that the small cameras you had described were for high end cars is because you said the one in the story was old news. But several other sources I found, as well as comments here from the manufacturer, described possible use of the cube camera in the story for automotive apps, meaning mainstream ones. Anyway, thanks for all the input.
These cameras are and will be for the hi end market, Audi, BMW etc. One of the basic differences that I see between machine vision and automotive is that, with the forward looking smart cameras, the requirements are the camera be spacially accurate. With machine vision, the camera must be accurate enough to do the job in 2D. With hi end automotive vision applications heading towards being able to do dynamic collision avoidance (moving car vs. moving object), the modern camera must work with scene recognition, the 3D brother of 2D pattern recognition. So one camera, using multiple frames of video will generate a moving 3D 'map' of the scene ahead, two cameras are not required, which simplifies the calibration and hardward required. Scene recognition for automotive aplications is a new frontier, obviously the robot industry has been working on it awhile. The autonomous vech. competition was very interesting. Next the car will have to figure out if the object is okay to run over or, apply the brakes determining that the car behind has time to brake as well! ;^) And that joke alludes to the newest systems for autos that allow a top or adjustable 360 degree view of the car on the dash display.
The difference in requirements between cameras for automotive applications and macine vision, for inspection or gaging, are large. Watching for a car in a blind spot, keeping an eye on the lane edge marker, or checking the position of the right-side passenger are much easier than inspecting a part for proper threads or correct dimensions. Also, dtermining part orientation is a demanding application as well. My point being that the two applications are very different and as a result, comparisons between them, (the two different types), are of marginal value.
From someone in the automotive camera business, yes, this is 'old hat'. One inch square was the old standard. The new form factor that we are designing to is a 18-20mm sided cube. The automotive smart cameras tend to have a module with the imager on it, video goes parallel out of the imager chip into a DSP on the next board of the camera. Usually, it is located very near the camera to avoid signaling issues. In the case of a front view smart camera, the lens peek out of the windshield above the rear view mirror. The DSP is on on a circuit board directly above it. I think most car cameras, rear view or forward view (usually a smart type), are based on 1/3" imagers. Now we are moving on to 1/4, 1/5" and smaller. This is where the German car camera market is at currently.
Spot on, Chuck. I also see potential applications in perimeter protection and in airport and city center security. Most of use know about London's 10,000 cameras (or whatever the specific number is), which monitor activity to keep an eye on crime and terrorist threats. For perimeter and airports, the TSA stuff we see isn't where the cutting-edge research activity is. Here's a piece I did a couple of years ago about some interesting IBM stuff. (Who knew IBM was into perimeter and airport protection?)
Tina, thanks for all the input on the SmartVue camera, especially from the app development perspective. My experience accords with Jon's, that in vision system engineers are interested less and less in coding and more and more in faster, easier app development.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.