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Titlebook: Image-Based Visualization; Interactive Multidim Christophe Hurter Book 2016 Springer Nature Switzerland AG 2016

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发表于 2025-3-21 17:59:47 | 显示全部楼层 |阅读模式
书目名称Image-Based Visualization
副标题Interactive Multidim
编辑Christophe Hurter
视频videohttp://file.papertrans.cn/462/461542/461542.mp4
丛书名称Synthesis Lectures on Visualization
图书封面Titlebook: Image-Based Visualization; Interactive Multidim Christophe Hurter Book 2016 Springer Nature Switzerland AG 2016
描述Our society has entered a data-driven era, one in which not only are enormous amounts of data being generated daily but there are also growing expectations placed on the analysis of this data. Some data have become simply too large to be displayed and some have too short a lifespan to be handled properly with classical visualization or analysis methods. In order to address these issues, this book explores the potential solutions where we not only visualize data, but also allow users to be able to interact with it. Therefore, this book will focus on two main topics: large dataset visualization and interaction.Graphic cards and their image processing power can leverage large data visualization but they can also be of great interest to support interaction. Therefore, this book will show how to take advantage of graphic card computation power with techniques called GPGPUs (general-purpose computing on graphics processing units). As specific examples, this book details GPGPU usages to produce fast enough visualization to be interactive with improved brushing techniques, fast animations between different data representations, and view simplifications (i.e. static and dynamic bundling tec
出版日期Book 2016
版次1
doihttps://doi.org/10.1007/978-3-031-02601-0
isbn_softcover978-3-031-01473-4
isbn_ebook978-3-031-02601-0Series ISSN 2159-516X Series E-ISSN 2159-5178
issn_series 2159-516X
copyrightSpringer Nature Switzerland AG 2016
The information of publication is updating

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Image-Based Visualization978-3-031-02601-0Series ISSN 2159-516X Series E-ISSN 2159-5178
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Edge Bundling,nly rely on edge simplification algorithms which have been subject to increased research interest and a number of improvements and enhancements in recent years. Edge bundling techniques can also be divided into the three levels from the data transformation pipeline (Figure 1.2): data level, geometry level, and image level.
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Introduction,placed on their analysis (Thomas and Cook, 2005). With the support of companies like Google,. big data has become a fast-emerging technology. OpenData programs, in which data are available for free, are growing in number. A number of popular websites, instead of protecting their data against “script
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Data Density Maps,hen transforming data to graphical marks, a regular visualization system draws each graphical mark independently from the others: if a mark to be drawn happens to be at the same position as previously drawn marks, the system replaces (or merges using color blending) the pixels in the resulting image
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Animation with Large Datasets, aesthetic venue when exploring datasets and a great deal of previous research has shown that animations can help a user to investigate a dataset by providing non-disruptive artifacts when modifying view configuration (Bezerianos et al., 2010; Elmqvist et al., 2008; Hurter et al., 2009b). Previous w
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Research Outlook and Vision,dress the scalability issue and to do this the graphic card is a great help. The graphic cards and their GPU (graphical power unit) never stop improving in terms of computation power. The central processing unit (CPU) also does the same but the GPU and CPU differ in terms of their architecture and t
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