.png){kind=icon}
返回
Interpreting a dynamic and uncertain world: task-based control
.png){kind=icon}
OA
AI
DOI：10.1016/S0004-3702(98)00004-6.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper we show that it can be beneficial to have a high-level vision component that guides the reasoning of the whole vision system when interpreting a dynamic and uncertain world. This guidance is provided by an attentional mechanism that exploits knowledge of the specific problem being solved. Here we develop a general framework for such an attentional mechanism and its application to understanding dynamic scenes. This attentional mechanism can enable a vision system to perform a given domain task while expending minimal resources. We have developed a component that uses Bayesian networks combined with a deictic representation to select what, when and how to use processed data from a fixed camera. We apply two forms of Bayesian network, which (1) create a dynamic structure to reflect the spatial organisation of the data and (2) measure task relatedness. Together these give attentional focus making the reasoning performed relevant to the task. (C) 1998 Elsevier Science B.V.
Keyword:
high-level computer vision
surveillance
attention
event reasoning
visual behaviour
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:
13.9
论文数:
6.1K
被引数:
1.9W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
查看更多内容