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Titlebook: Advances in Embedded Computer Vision; Branislav Kisačanin,Margrit Gelautz Book 2014 Springer International Publishing Switzerland 2014 Com

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发表于 2025-3-21 16:49:52 | 显示全部楼层 |阅读模式
期刊全称Advances in Embedded Computer Vision
影响因子2023Branislav Kisačanin,Margrit Gelautz
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发行地址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 augme
学科分类Advances in Computer Vision and Pattern Recognition
图书封面Titlebook: Advances in Embedded Computer Vision;  Branislav Kisačanin,Margrit Gelautz Book 2014 Springer International Publishing Switzerland 2014 Com
影响因子.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..
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https://doi.org/10.1007/978-1-4684-8718-3he optical mouse chip tracked the motion of light dots in its field of view, and reported motion with a pair of 2-bit Gray codes for . and . relative position—just like the mechanical mice of the time. Besides the chip, the only other electronic components in the mouse were the LED illuminators.
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K. S. V. Santhanam,N. M. Limayeent-aware filtering of imagery elevated the human analyst’s task from vehicle detection to detection verification, presumably a much less repetitive, fatiguing, and error-prone task. The system was successfully demonstrated as part of a Search And Rescue operation.
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K. S. V. Santhanam,N. M. Limaye in detail using real-world lane data to explore its effectiveness for embedded realization and adaptability to varying contextual information such as lane types and environmental conditions. Accuracy of more than 90 % is obtained during the evaluation of the proposed method using real-world driving data.
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