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Embedded Computer Vision

By Branislav Kisacanin , Shuvra S. Bhattacharyya , Sek Chai

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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. It also looks ahead, providing a sense of what applications can be expected in the future.

Full Description

  • ISBN13: 978-1-8480-0303-3
  • 312 Pages
  • User Level: Science
  • Publication Date: September 26, 2008
  • Available eBook Formats: PDF
  • eBook Price: $39.95
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Full Description
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.
Table of Contents

Table of Contents

  1. Part I: Introduction.
  2. Hardware Considerations for Embedded Vision Systems.
  3. Design Methodology for Embedded Computer Vision Systems.
  4. We Can Watch It For You Wholesale.
  5. Part II: Advances in Embedded Computer Vision.
  6. Using Robust Local Features on DSP
  7. based Embedded Systems.
  8. Benchmarks of Low
  9. level Vision algorithms for DSP, FPGA and Mobile PC Processors.
  10. SAD
  11. based Stereo matching Using FPGAs.
  12. Motion History Histograms for Human Action Recognition.
  13. Embedded Real
  14. time Surveillance Using Multimodal Mean Background Modeling.
  15. Implementation Considerations for Automotive Vision Systems on a Fixed
  16. point DSP.
  17. Towards OpenVL: Improving Real
  18. time Performance of Computer Vision Applications.
  19. Part III: Looking Ahead.
  20. Mobile Challenges for Embedded Computer Vision.
  21. Challenges in Video Analytics.
  22. Challenges of Embedded Computer vision in Automotive Safety Systems.
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