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Other titles in the Advances in Pattern Recognition series:
Embedded Computer Vision (Advances in Pattern Recognition)by Sameer Singh
Synopses & Reviews
Embedded Computer Vision, exemplified by the migration from powerful workstations to embedded processors in computer vision applications, is a new and emerging field that enables an associated shift in application development and implementation. This comprehensive volume brings together a wealth of experiences from leading researchers in the field of embedded computer vision, from both academic and industrial research centers, and covers a broad range of challenges and trade-offs brought about by this paradigm shift. Part I provides an exposition of basic issues and applications in the area necessary for understanding the present and future work. Part II offers chapters based on the most recent research and results. Finally, the last part looks ahead, providing a sense of what major applications could be expected in the near future, describing challenges in mobile environments, video analytics, and automotive safety applications. Features: • Discusses the latest state-of-the-art techniques in embedded computer vision • Presents a thorough introductory section on hardware and architectures, design methodologies, and video analytics to aid the reader's understanding through the following chapters • Offers emphasis on tackling important problems for society, safety, security, health, mobility, connectivity, and energy efficiency • Discusses evaluation of trade-offs required to design cost-effective systems for successful products • Explores the advantages of various architectures, development of high-level software frameworks and cost-effective algorithmic alternatives • Examines issues of implementation on fixed-point processors, presented through an example of an automotive safety application • Offers insights from leaders in the field on what future applications will be This book is a welcome collection of stand-alone articles, ideal for researchers, practitioners, and graduate students. It provides historical perspective, the latest research results, and a vision for future developments in the emerging field of embedded computer vision. Supplementary material can be found at http://www.embeddedvisioncentral.com.
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.
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
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.
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