Video visualization

G. Daniel, Min Chen
{"title":"Video visualization","authors":"G. Daniel, Min Chen","doi":"10.2312/conf/EG2013/tutorials/t2","DOIUrl":null,"url":null,"abstract":"Video data, generated by the entertainment industry, security and traffic cameras, video conferencing systems, video emails, and so on, is perhaps most time-consuming to process by human beings. In this paper, we present a novel methodology for \"summarizing\" video sequences using volume visualization techniques. We outline a system pipeline for capturing videos, extracting features, volume rendering video and feature data, and creating video visualization. We discuss a collection of image comparison metrics, including the linear dependence detector, for constructing \"relative\" and \"absolute\" difference volumes that represent the magnitude of variation between video frames. We describe the use of a few volume visualization techniques, including volume scene graphs and spatial transfer functions, for creating video visualization. In particular, we present a stream-based technique for processing and directly rendering video data in real time. With the aid of several examples, we demonstrate the effectiveness of using video visualization to convey meaningful information contained in video sequences.","PeriodicalId":372131,"journal":{"name":"IEEE Visualization, 2003. VIS 2003.","volume":"93 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"113","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Visualization, 2003. VIS 2003.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2312/conf/EG2013/tutorials/t2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 113

Abstract

Video data, generated by the entertainment industry, security and traffic cameras, video conferencing systems, video emails, and so on, is perhaps most time-consuming to process by human beings. In this paper, we present a novel methodology for "summarizing" video sequences using volume visualization techniques. We outline a system pipeline for capturing videos, extracting features, volume rendering video and feature data, and creating video visualization. We discuss a collection of image comparison metrics, including the linear dependence detector, for constructing "relative" and "absolute" difference volumes that represent the magnitude of variation between video frames. We describe the use of a few volume visualization techniques, including volume scene graphs and spatial transfer functions, for creating video visualization. In particular, we present a stream-based technique for processing and directly rendering video data in real time. With the aid of several examples, we demonstrate the effectiveness of using video visualization to convey meaningful information contained in video sequences.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
视频可视化
由娱乐行业、安全和交通摄像机、视频会议系统、视频电子邮件等产生的视频数据,可能是人类处理最耗时的。在本文中,我们提出了一种使用体积可视化技术来“总结”视频序列的新方法。我们概述了一个系统管道,用于捕获视频,提取特征,体绘制视频和特征数据,以及创建视频可视化。我们讨论了一组图像比较度量,包括线性依赖检测器,用于构建表示视频帧之间变化幅度的“相对”和“绝对”差异体积。我们描述了一些体可视化技术的使用,包括体场景图和空间传递函数,用于创建视频可视化。特别地,我们提出了一种基于流的实时处理和直接渲染视频数据的技术。通过几个例子,我们证明了使用视频可视化来传达视频序列中包含的有意义的信息的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Voxels on fire [computer animation] Chameleon: an interactive texture-based rendering framework for visualizing three-dimensional vector fields Fast volume segmentation with simultaneous visualization using programmable graphics hardware Adaptive design of a global opacity transfer function for direct volume rendering of ultrasound data Visualization experiences and issues in deep space exploration
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1