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

CRUC

https://doi.org/10.1007/978-3-030-35993-5obiles continue to evolve by becoming smarter and by leveraging cutting-edge technologies. Over the last three decades, we witnessed a tremendous growth of computer vision knowledge through research in academia and industry. More recently, in the last decade, we are finally seeing exciting applicati

Contort

William T. Grubbs,Lyman H. Rickardorld for control; obstacle detection and avoidance for safe flight; and all of this has to be executed in real-time onboard the vehicle. This is a challenge for micro air vehicles, since their limited payload demands small, lightweight, and low-power sensors and processing units, favoring vision-bas

思想上升

Peter N. R. Usherwood,Robert L. Ramseynology, has significantly increased the interest in computer vision applications based on depth data that, in most cases, enables higher robustness compared to solutions based on traditional 2D images. Unfortunately, active techniques are quite noisy or even completely useless in outdoor environment

ABYSS

M. J. Allen,S. F. Cleary,F. M. Hawkridgevel, interpolated views from multiple surrounding cameras, effectively emulating a capturing camera position behind a virtually transparent display. Nonuniform, adaptive plane sweeping with dynamic workload balancing yields real-time performances on low-cost embedded GPU platforms.

lactic

I. Ya. Belyaev,V. S. Shcheglov,Ye. D. Alipovn the quite substantial processing capabilities of modern mobile devices are not at the level needed for running the latest object recognition, tracking, or rendering methods. Furthermore, the resultant power consumption drains the battery fast. To ensure a great user experience, AR algorithms have

可商量

upstart

neolith

K. S. V. Santhanam,N. M. Limayethods, there is some research on estimating lane-based contextual information using properties and positions of lanes. According to a recent survey of lane estimation in [.], there are still open challenges in terms of reliably detecting lanes in varying road conditions. Lane feature extraction is o

被告

https://doi.org/10.1007/978-4-431-55660-2nable new applications and capabilities. Advances in middleware and software support for distributed algorithms on smart camera networks have resulted in improved software platforms, but work still needs to be done. A variety of network-level algorithms and services are now possible and are starting

護(hù)航艦

https://doi.org/10.1007/978-3-319-25571-2n this context involves the extraction of knowledge from image and video streams in real-time, and from sources that are possibly distributed and heterogeneous. With advances in sensor and digital processing technologies, we are able to deploy networks involving large numbers of cameras that acquire