.png){kind=icon}
返回
Calibrating Distributed Camera Networks
.png){kind=icon}
OA
AI
DOI：10.1109/JPROC.2008.928759.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recent developments in wireless sensor networks have made feasible distributed camera networks, in which cameras and processing nodes may be spread over a wide geographical area, with no centralized processor and limited ability to communicate a large amount of information over long distances. This paper overviews distributed algorithms for the calibration of such camera networks- that is, the automatic estimation of each camera's position, orientation, and focal length. in particular, we discuss a decentralized method for obtaining the vision graph for a distributed camera network, in which each edge of the graph represents two cameras that image a sufficiently large part of the same environment. We next describe a distributed algorithm in which each camera performs a local, robust nonlinear optimization over the camera parameters and scene points of its vision graph neighbors in order to obtain an initial calibration estimate. We then show how a distributed inference algorithm based on belief propagation can refine the initial estimate to be both accurate and globally consistent.
Keyword:
Belief propagation
bundle adjustment
camera calibration
distributed algorithms
metric reconstruction
sensor networks
structure from motion
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
25.9
论文数:
9.9K
被引数:
4.5W
机构
引用论文
Production and specificity of monoclonal antibodies against calmodulin from dictyostelium discoideum
.png){kind=icon}
PREAI
分享收藏分享收藏
分享收藏
查看更多内容