IEEE Transactions on Visualization and Computer Graphics最新文献_第8页

Objective Observer-Relative Flow Visualization in Curved Spaces for Unsteady 2D Geophysical Flows. 目的非定常二维地球物理流动曲线空间的观察者相对流动可视化。

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2021-02-01 Epub Date: 2021-01-28 DOI: 10.1109/TVCG.2020.3030454

Peter Rautek, Matej Mlejnek, Johanna Beyer, Jakob Troidl, Hanspeter Pfister, Thomas Theubl, Markus Hadwiger

{"title":"Objective Observer-Relative Flow Visualization in Curved Spaces for Unsteady 2D Geophysical Flows.","authors":"Peter Rautek, Matej Mlejnek, Johanna Beyer, Jakob Troidl, Hanspeter Pfister, Thomas Theubl, Markus Hadwiger","doi":"10.1109/TVCG.2020.3030454","DOIUrl":"https://doi.org/10.1109/TVCG.2020.3030454","url":null,"abstract":"Computing and visualizing features in fluid flow often depends on the observer, or reference frame, relative to which the input velocity field is given. A desired property of feature detectors is therefore that they are objective, meaning independent of the input reference frame. However, the standard definition of objectivity is only given for Euclidean domains and cannot be applied in curved spaces. We build on methods from mathematical physics and Riemannian geometry to generalize objectivity to curved spaces, using the powerful notion of symmetry groups as the basis for definition. From this, we develop a general mathematical framework for the objective computation of observer fields for curved spaces, relative to which other computed measures become objective. An important property of our framework is that it works intrinsically in 2D, instead of in the 3D ambient space. This enables a direct generalization of the 2D computation via optimization of observer fields in flat space to curved domains, without having to perform optimization in 3D. We specifically develop the case of unsteady 2D geophysical flows given on spheres, such as the Earth. Our observer fields in curved spaces then enable objective feature computation as well as the visualization of the time evolution of scalar and vector fields, such that the automatically computed reference frames follow moving structures like vortices in a way that makes them appear to be steady.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":"283-293"},"PeriodicalIF":5.2,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TVCG.2020.3030454","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38486683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning. PlotThread:使用强化学习创建富有表现力的故事情节可视化。

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2021-02-01 Epub Date: 2021-01-28 DOI: 10.1109/TVCG.2020.3030467

Tan Tang, Renzhong Li, Xinke Wu, Shuhan Liu, Johannes Knittel, Steffen Koch, Lingyun Yu, Peiran Ren, Thomas Ertl, Yingcai Wu

{"title":"PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning.","authors":"Tan Tang, Renzhong Li, Xinke Wu, Shuhan Liu, Johannes Knittel, Steffen Koch, Lingyun Yu, Peiran Ren, Thomas Ertl, Yingcai Wu","doi":"10.1109/TVCG.2020.3030467","DOIUrl":"https://doi.org/10.1109/TVCG.2020.3030467","url":null,"abstract":"Storyline visualizations are an effective means to present the evolution of plots and reveal the scenic interactions among characters. However, the design of storyline visualizations is a difficult task as users need to balance between aesthetic goals and narrative constraints. Despite that the optimization-based methods have been improved significantly in terms of producing aesthetic and legible layouts, the existing (semi-) automatic methods are still limited regarding 1) efficient exploration of the storyline design space and 2) flexible customization of storyline layouts. In this work, we propose a reinforcement learning framework to train an AI agent that assists users in exploring the design space efficiently and generating well-optimized storylines. Based on the framework, we introduce PlotThread, an authoring tool that integrates a set of flexible interactions to support easy customization of storyline visualizations. To seamlessly integrate the AI agent into the authoring process, we employ a mixed-initiative approach where both the agent and designers work on the same canvas to boost the collaborative design of storylines. We evaluate the reinforcement learning model through qualitative and quantitative experiments and demonstrate the usage of PlotThread using a collection of use cases.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":"294-303"},"PeriodicalIF":5.2,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TVCG.2020.3030467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38486690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 32

Visual Reasoning Strategies for Effect Size Judgments and Decisions. 效应大小判断和决策的视觉推理策略。

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2021-02-01 Epub Date: 2021-01-28 DOI: 10.1109/TVCG.2020.3030335

Alex Kale, Matthew Kay, Jessica Hullman

{"title":"Visual Reasoning Strategies for Effect Size Judgments and Decisions.","authors":"Alex Kale, Matthew Kay, Jessica Hullman","doi":"10.1109/TVCG.2020.3030335","DOIUrl":"https://doi.org/10.1109/TVCG.2020.3030335","url":null,"abstract":"Uncertainty visualizations often emphasize point estimates to support magnitude estimates or decisions through visual comparison. However, when design choices emphasize means, users may overlook uncertainty information and misinterpret visual distance as a proxy for effect size. We present findings from a mixed design experiment on Mechanical Turk which tests eight uncertainty visualization designs: 95% containment intervals, hypothetical outcome plots, densities, and quantile dotplots, each with and without means added. We find that adding means to uncertainty visualizations has small biasing effects on both magnitude estimation and decision-making, consistent with discounting uncertainty. We also see that visualization designs that support the least biased effect size estimation do not support the best decision-making, suggesting that a chart user's sense of effect size may not necessarily be identical when they use the same information for different tasks. In a qualitative analysis of users' strategy descriptions, we find that many users switch strategies and do not employ an optimal strategy when one exists. Uncertainty visualizations which are optimally designed in theory may not be the most effective in practice because of the ways that users satisfice with heuristics, suggesting opportunities to better understand visualization effectiveness by modeling sets of potential strategies.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":"272-282"},"PeriodicalIF":5.2,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TVCG.2020.3030335","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38486679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 52

VisCode: Embedding Information in Visualization Images using Encoder-Decoder Network. 使用编码器-解码器网络在可视化图像中嵌入信息。

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2021-02-01 Epub Date: 2021-01-28 DOI: 10.1109/TVCG.2020.3030343

Peiying Zhang, Chenhui Li, Changbo Wang

{"title":"VisCode: Embedding Information in Visualization Images using Encoder-Decoder Network.","authors":"Peiying Zhang, Chenhui Li, Changbo Wang","doi":"10.1109/TVCG.2020.3030343","DOIUrl":"https://doi.org/10.1109/TVCG.2020.3030343","url":null,"abstract":"We present an approach called VisCode for embedding information into visualization images. This technology can implicitly embed data information specified by the user into a visualization while ensuring that the encoded visualization image is not distorted. The VisCode framework is based on a deep neural network. We propose to use visualization images and QR codes data as training data and design a robust deep encoder-decoder network. The designed model considers the salient features of visualization images to reduce the explicit visual loss caused by encoding. To further support large-scale encoding and decoding, we consider the characteristics of information visualization and propose a saliency-based QR code layout algorithm. We present a variety of practical applications of VisCode in the context of information visualization and conduct a comprehensive evaluation of the perceptual quality of encoding, decoding success rate, anti-attack capability, time performance, etc. The evaluation results demonstrate the effectiveness of VisCode.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":"326-336"},"PeriodicalIF":5.2,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TVCG.2020.3030343","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38584094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 26

Info Vis Reviewers Info-Vis审查员

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2021-02-01 DOI: 10.1109/tvcg.2020.3033652

引用次数: 0

VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection. VATLD:一个评估、理解和改进交通灯检测的可视化分析系统。

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2021-02-01 Epub Date: 2021-01-28 DOI: 10.1109/TVCG.2020.3030350

Liang Gou, Lincan Zou, Nanxiang Li, Michael Hofmann, Arvind Kumar Shekar, Axel Wendt, Liu Ren

{"title":"VATLD: A Visual Analytics System to Assess, Understand and Improve Traffic Light Detection.","authors":"Liang Gou, Lincan Zou, Nanxiang Li, Michael Hofmann, Arvind Kumar Shekar, Axel Wendt, Liu Ren","doi":"10.1109/TVCG.2020.3030350","DOIUrl":"https://doi.org/10.1109/TVCG.2020.3030350","url":null,"abstract":"Traffic light detection is crucial for environment perception and decision-making in autonomous driving. State-of-the-art detectors are built upon deep Convolutional Neural Networks (CNNs) and have exhibited promising performance. However, one looming concern with CNN based detectors is how to thoroughly evaluate the performance of accuracy and robustness before they can be deployed to autonomous vehicles. In this work, we propose a visual analytics system, VATLD, equipped with a disentangled representation learning and semantic adversarial learning, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications. The disentangled representation learning extracts data semantics to augment human cognition with human-friendly visual summarization, and the semantic adversarial learning efficiently exposes interpretable robustness risks and enables minimal human interaction for actionable insights. We also demonstrate the effectiveness of various performance improvement strategies derived from actionable insights with our visual analytics system, VATLD, and illustrate some practical implications for safety-critical applications in autonomous driving.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":"261-271"},"PeriodicalIF":5.2,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TVCG.2020.3030350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38507930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

StructGraphics: Flexible Visualization Design through Data-Agnostic and Reusable Graphical Structures. StructGraphics:通过数据不可知和可重用的图形结构进行灵活的可视化设计。

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2021-02-01 Epub Date: 2021-01-28 DOI: 10.1109/TVCG.2020.3030476

Theophanis Tsandilas

{"title":"StructGraphics: Flexible Visualization Design through Data-Agnostic and Reusable Graphical Structures.","authors":"Theophanis Tsandilas","doi":"10.1109/TVCG.2020.3030476","DOIUrl":"https://doi.org/10.1109/TVCG.2020.3030476","url":null,"abstract":"Information visualization research has developed powerful systems that enable users to author custom data visualizations without textual programming. These systems can support graphics-driven practices by bridging lazy data-binding mechanisms with vector-graphics editing tools. Yet, despite their expressive power, visualization authoring systems often assume that users want to generate visual representations that they already have in mind rather than explore designs. They also impose a data-to-graphics workflow, where binding data dimensions to graphical properties is a necessary step for generating visualization layouts. In this paper, we introduce StructGraphics, an approach for creating data-agnostic and fully reusable visualization designs. StructGraphics enables designers to construct visualization designs by drawing graphics on a canvas and then structuring their visual properties without relying on a concrete dataset or data schema. In StructGraphics, tabular data structures are derived directly from the structure of the graphics. Later, designers can link these structures with real datasets through a spreadsheet user interface. StructGraphics supports the design and reuse of complex data visualizations by combining graphical property sharing, by-example design specification, and persistent layout constraints. We demonstrate the power of the approach through a gallery of visualization examples and reflect on its strengths and limitations in interaction with graphic designers and data visualization experts.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":"315-325"},"PeriodicalIF":5.2,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TVCG.2020.3030476","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38584106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Chartem: Reviving Chart Images with Data Embedding. 图表:恢复图表图像与数据嵌入。

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2021-02-01 Epub Date: 2021-01-28 DOI: 10.1109/TVCG.2020.3030351

Jiayun Fu, Bin Zhu, Weiwei Cui, Song Ge, Yun Wang, Haidong Zhang, He Huang, Yuanyuan Tang, Dongmei Zhang, Xiaojing Ma

{"title":"Chartem: Reviving Chart Images with Data Embedding.","authors":"Jiayun Fu, Bin Zhu, Weiwei Cui, Song Ge, Yun Wang, Haidong Zhang, He Huang, Yuanyuan Tang, Dongmei Zhang, Xiaojing Ma","doi":"10.1109/TVCG.2020.3030351","DOIUrl":"https://doi.org/10.1109/TVCG.2020.3030351","url":null,"abstract":"In practice, charts are widely stored as bitmap images. Although easily consumed by humans, they are not convenient for other uses. For example, changing the chart style or type or a data value in a chart image practically requires creating a completely new chart, which is often a time-consuming and error-prone process. To assist these tasks, many approaches have been proposed to automatically extract information from chart images with computer vision and machine learning techniques. Although they have achieved promising preliminary results, there are still a lot of challenges to overcome in terms of robustness and accuracy. In this paper, we propose a novel alternative approach called Chartem to address this issue directly from the root. Specifically, we design a data-embedding schema to encode a significant amount of information into the background of a chart image without interfering human perception of the chart. The embedded information, when extracted from the image, can enable a variety of visualization applications to reuse or repurpose chart images. To evaluate the effectiveness of Chartem, we conduct a user study and performance experiments on Chartem embedding and extraction algorithms. We further present several prototype applications to demonstrate the utility of Chartem.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":"337-346"},"PeriodicalIF":5.2,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TVCG.2020.3030351","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38369646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 18

SciVis Program Committee SciVis项目委员会

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2021-02-01 DOI: 10.1109/tvcg.2020.3033682

引用次数: 0

Fuzzy Spreadsheet: Understanding and Exploring Uncertainties in Tabular Calculations 模糊电子表格：理解和探索表格计算中的不确定性

IF 5.2 1区计算机科学

IEEE Transactions on Visualization and Computer Graphics Pub Date : 2020-12-16 DOI: 10.31219/osf.io/j5g4b

V. Dhanoa, C. Walchshofer, A. Hinterreiter, E. Gröller, M. Streit

{"title":"Fuzzy Spreadsheet: Understanding and Exploring Uncertainties in Tabular Calculations","authors":"V. Dhanoa, C. Walchshofer, A. Hinterreiter, E. Gröller, M. Streit","doi":"10.31219/osf.io/j5g4b","DOIUrl":"https://doi.org/10.31219/osf.io/j5g4b","url":null,"abstract":"Spreadsheet-based tools provide a simple yet effective way of calculating values, which makes them the number-one choice for building and formalizing simple models for budget planning and many other applications. A cell in a spreadsheet holds one specific value and gives a discrete, overprecise view of the underlying model. Therefore, spreadsheets are of limited use when investigating the inherent uncertainties of such models and answering what-if questions. Existing extensions typically require a complex modeling process that cannot easily be embedded in a tabular layout. In Fuzzy Spreadsheet, a cell can hold and display a distribution of values. This integrated uncertainty-handling immediately conveys sensitivity and robustness information. The fuzzification of the cells enables calculations not only with precise values but also with distributions, and probabilities. We conservatively added and carefully crafted visuals to maintain the look and feel of a traditional spreadsheet while facilitating what-if analyses. Given a user-specified reference cell, Fuzzy Spreadsheet automatically extracts and visualizes contextually relevant information, such as impact, uncertainty, and degree of neighborhood, for the selected and related cells. To evaluate its usability and the perceived mental effort required, we conducted a user study. The results show that our approach outperforms traditional spreadsheets in terms of answer correctness, response time, and perceived mental effort in almost all tasks tested.","PeriodicalId":13376,"journal":{"name":"IEEE Transactions on Visualization and Computer Graphics","volume":" ","pages":"1-1"},"PeriodicalIF":5.2,"publicationDate":"2020-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48176786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1