Advances in Embedded Computer Vision

This illuminating collection offers a fresh look at the very latest advances in the field of embedded computer vision. Emerging areas covered by this comprehensive text/reference include the embedded realization of 3D vision technologies for a variety of applications, such as stereo cameras on mobile devices. Recent trends towards the development of small unmanned aerial vehicles (UAVs) with embedded image and video processing algorithms are also examined. Topics and features: discusses in detail three major success stories the development of the optical mouse, vision for consumer robotics, and vision for automotive safety; reviews state-of-the-art research on embedded 3D vision, UAVs, automotive vision, mobile vision apps, and augmented reality; examines the potential of embedded computer vision in such cutting-edge areas as the Internet of Things, the mining of large data streams, and in computational sensing; describes historical successes, current implementations, and future challenges.

Offers a fresh look at the very latest advances in the field of embedded computer vision Reviews state-of-the-art research on embedded 3D vision, UAVs, automotive vision, mobile vision apps, and augmented reality Examines the potential of embedded computer vision in such cutting-edge areas as the Internet of Things, the mining of large data streams, and in computational sensing Includes supplementary material: sn.pub/extras

Inhalt

Part I: Success Stories.- The Optical Mouse.- Consumer Robotics.- Embedded Vision in Advanced Driver Assistance Systems.- Part II: State of the Art.- Computer Vision for Micro Air Vehicles.- Stereo Vision Algorithms Suited to Constrained FPGA Cameras.- Plane Sweeping in Eye-gaze Corrected, Tele-immersive 3D Video Conferencing.- Challenges in Embedded Vision for Augmented Reality.- Tic-Tac-Tandroid.- Vehicle Detection Onboard Small Unmanned Aircraft.- Vision-based Lane Analysis.- Part III: Future Challenges.- Distributed Smart Cameras in the Age of Cloud Computing and the Internet-of-Things.- Data-driven Stream Mining Systems for Computer Vision.- Designing Vision Systems that See Better.