Embedded Computer Vision
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Embedded Computer Vision

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Shuvra S. Bhattacharyya
Advances in Computer Vision and Pattern Recognition
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"This book brings together a wealth of experiences from leading researchers in the field of Embedded Computer vision, from both academic and industrial research labs. Lately there is a major shift in the way computer vision applications are implemented and even developed.

This book covers a broad range of challenges and trade offs brought by this paradigm shift. Part I, the introductory chapters, discusses pioneers in the field, providing an exposition of early work in the area necessary for understanding the present and future work. Part II, offers chapters, based on the most recent research and includes results from industry and academia. Finally the last part looks ahead, providing a sense of what major applications could be expected in the near future.

This book is a welcome collection of references, ideal for researchers, practitioners and graduate students. It provides historical perspective, the latest research results and a vision for future developments in this new field of embedded computer vision."
"Part I: Introduction.- Hardware Considerations for Embedded Vision Systems.- Design Methodology for Embedded Computer Vision Systems.- We Can Watch It For You Wholesale.- Part II: Advances in Embedded Computer Vision.- Using Robust Local Features on DSP-based Embedded Systems.- Benchmarks of Low-level Vision algorithms for DSP, FPGA and Mobile PC Processors.- SAD-based Stereo matching Using FPGAs.- Motion History Histograms for Human Action Recognition.- Embedded Real-time Surveillance Using Multimodal Mean Background Modeling.- Implementation Considerations for Automotive Vision Systems on a Fixed-point DSP.- Towards OpenVL: Improving Real-time Performance of Computer Vision Applications.- Part III: Looking Ahead.- Mobile Challenges for Embedded Computer Vision.- Challenges in Video Analytics.- Challenges of Embedded Computer vision in Automotive Safety Systems."
As a graduate student at Ohio State in the mid-1970s, I inherited a unique c- puter 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 driveabout the size of four washing machines. They had also - signed a binary image array processor and programming language, complete with a users guide, to facilitate designing software for this one-of-a-kindprocessor. 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 eldofview,trackeditsmovementsinrealtime,anddisplayedarunningdescription of the events in English. For example: An object has appeared in the upper right corner...Itismovingdownandtotheleft...Nowtheobjectisgettingcloser...The object moved out of sight to the leftabout like that. The algorithms were simple, relying on a suf cient 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.

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