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Embedded Computer Vision (Advances in Computer Vision and Pattern Recognition)

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As a graduate student at Ohio State in the mid-1970s, I inherited a unique computer vision laboratory from the doctoral research of previous students. They had designed and built an early frame-grabber to deliver digitized color video from a (very large) electronic video camera on a tripod to a mini-computer (sic) with a (huge!) disk drive—about the size of four washing machines. They had also designed a binary image array processor and programming language, complete with a user’s guide, to facilitate designing software for this one-of-a-kind processor. The overall system enabled programmable real-time image processing at video rate for many operations.

I had the whole lab to myself. I designed software that detected an object in the field of view, tracked its movements in real time, and displayed a running description of the events in English. For example: “An object has appeared in the upper right corner . . . It ismoving down and to the left . . . Now the object is getting closer. . . The object moved out of sight to the left”—about like that. The algorithms were simple, relying on a sufficient image intensity difference to separate the object from the background (a plain wall). From computer vision papers I had read, I knew that vision in general imaging conditions is much more sophisticated. But it worked, it was great fun, and I was hooked.

A lot has changed since! Dissertation after dissertation, the computer vision research community has contributed many new techniques to expand the scope and reliability of real-time computer vision systems. Cameras changed from analog to digital and became incredibly small. At the same time, computers shrank from minicomputers to workstations to personal computers to microprocessors to digital signal processors to programmable digitalmedia systems on a chip. Disk drives became very small and are starting to give way to multi-gigabyte flash memories. Many computer vision systems are so small and embedded in other systems that we don’t even call them “computers” anymore. We call them automotive vision sensors, such as lane departure and blind spot warning sensors. We call them smart cameras and digital video recorders for video surveillance. We call them mobile phones (which happen to have embedded cameras and 5+ million lines of wideranging software), and so on.
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