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SurfRiver: Flattening Stream Surfaces for comparative visualization

IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2021年第6期27卷 2783-2795页

作者： Zhang, Jun Tao, Jun Wang, Jian-Xun Wang, Chaoli Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Natl Supercomp Ctr Guangzhou Guangzhou Peoples R China Univ Notre Dame Dept Aerosp & Mech Engn Notre Dame IN 46556 USA

We present SurfRiver, a new visual transformation approach that flattens stream surfaces in 3D to rivers in 2D for comparative visualization. Leveraging the TextFlow-like visual metaphor, SurfRiver untangles the convoluted individual stream surfaces along the flow direction and maps them along the horizontal direction of the abstract river view. It stacks multiple surfaces along the vertical direction of the river view. This visual mapping makes it easy for users to track along the flow direction and align stream surfaces for comparative study. Through brushing and linking, the river view is connected to the spatial surface view for collective reasoning. SurfRiver can be used to examine a single stream surface, investigate seeding sensitivity or variability of a family of surfaces from a group of related seeding curves, or explore a collection of representative surfaces. We describe our optimization solution to achieve the desirable mapping, present SurfRiver interface and interactions, and report results from different flow fields to demonstrate its efficacy. Feedback from a domain expert also indicates the promise of SurfRiver.

关键词： visualization Rivers Rendering (computer graphics) Three-dimensional displays Streaming media Computer science Shape Flow visualization stream surfaces visual transformation comparative visualization

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A comparative visualization tool for ocean data analysis based on mode water regions

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JOURNAL OF visualization 2020年第2期23卷 313-329页

作者： Yano, Midori Itoh, Takayuki Tanaka, Yuusuke Matsuoka, Daisuke Araki, Fumiaki Ochanomizu Univ Bunkyo Ku 2-1-1 Otsuka Tokyo 1128610 Japan Japan Agcy Marine Earth Sci & Technol Kanazawa Ku 3173-25 Showa Machi Yokohama Kanagawa 2360001 Japan

Ocean data have been improved with the enhancement of observed values and the evolution of computational technologies. It has also been verified based on the reproducibility of various ocean phenomena. Mode water is one of the indicators for assessing ocean data because of its special properties. However, its definition differs for each ocean data. Besides, its observation is primarily performed by 2D analysis using the cutting plane of the ocean space. Therefore, reproducibility of the ocean space may have not been fully examined. Here, this paper presents a visual analysis tool for the feature of ocean data based on the 3D shape comparison of the mode water regions among three ocean datasets. Our comparison is based on similarity measure from shape appearances of the mode water regions extracted as isosurfaces. Users can interact with shape similarity data and a pair of isosurfaces. Our visualization tool supports to easily explore the relationship of different variable thresholds that are used for conditions of the mode water region and observe the specified parts of the pair in the 3D space. We demonstrate the availability and potential benefit of this approach through three examples that searched for the best conditions and expert feedback.

关键词： comparative visualization Scientific visualization Volume dataset Isosurface Ocean data Mode water

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Implementation of comparative visualization pedagogy for structural dynamics

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2018年第5期26卷 1894-1902页

作者： Mahajan, Suryakant K. Sonparote, Ranjan S. Visvesvaraya Natl Inst Technol Dept Appl Mech Nagpur Maharashtra India

CALSIDOF, a self-learning tool based on juxtaposition comparative visualization pedagogy is developed and implemented for structural dynamics. The assessment of the effectiveness of the tool was carried out using a questionnaire survey. This survey indicates that the comparative visualization pedagogy approach of CALSIDOF can help in understanding and excite the interest in the subject.

关键词： comparative visualization computer-aided learning juxtaposition pedagogy single-degree-of-freedom system

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Worlds-in-Wedges: Combining Worlds-in-Miniature and Portals to Support comparative Immersive visualization of Forestry Data 26

Worlds-in-Wedges: Combining Worlds-in-Miniature and Portals ...

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26th IEEE Conference on Virtual Reality and 3D User Interfaces (VR)

作者： Nam, Jung Who McCullough, Krista Tveite, Joshua Espinosa, Maria Molina Perry, Charles H. Wilson, Barry T. Keefe, Daniel F. Univ Minnesota Comp Sci & Engn Minneapolis MN 55455 USA US Forest Serv USDA Northern Res Stn Madison WI USA

ISBN: (纸本)9781728113777

Virtual reality (VR) environments are typically designed so users feel present in a single virtual world at a time, but this creates a problem for applications that require visual comparisons (e.g., forest scientists comparing multiple data-driven virtual forests). To address this, we present Worlds-in-Wedges, a 3D user interface and visualization technique that supports comparative immersive visualization by dividing the virtual space surrounding the user into volumetric wedges. There are three visual/interactive levels. The first, worlds-in-context, visualizes high-level relationships between the worlds (e.g., a map for worlds that are related in space). The second level, worlds-in-miniature, is a multi-instance implementation of the World-in-Miniature technique extended to support mutlivariate glyph visualization. The third level, worlds-in-wedges, displays multiple large-scale worlds in wedges that act as volumetric portals. The interface supports navigation, selection, and view manipulation. Since the techniques were inspired directly by problems facing forest scientists, the interface was evaluated by building a complete multivariate data visualization of the US Forest Service Forest Inventory and Analysis public dataset. Scientist user feedback and lessons from iterative design are reported.

关键词： worlds-in-miniature 3D user interface presence comparative visualization

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Glyph-based comparative visualization for Diffusion Tensor Fields

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2016年第1期22卷 797-806页

作者： Zhang, Changgong Schultz, Thomas Lawonn, Kai Eisemann, Elmer Vilanova, Anna Delft Univ Technol Comp Graph & Visualizat Grp NL-2600 AA Delft Netherlands Univ Bonn Visualizat & Med Image Anal Grp Bonn Germany Univ Magdeburg Visualizat Grp D-39106 Magdeburg Germany

Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging modality that enables the in-vivo reconstruction and visualization of fibrous structures. To inspect the local and individual diffusion tensors, glyph-based visualizations are commonly used since they are able to effectively convey full aspects of the diffusion tensor. For several applications it is necessary to compare tensor fields, e.g., to study the effects of acquisition parameters, or to investigate the influence of pathologies on white matter structures. This comparison is commonly done by extracting scalar information out of the tensor fields and then comparing these scalar fields, which leads to a loss of information. If the glyph representation is kept, simple juxtaposition or superposition can be used. However, neither facilitates the identification and interpretation of the differences between the tensor fields. Inspired by the checkerboard style visualization and the superquadric tensor glyph, we design a new glyph to locally visualize differences between two diffusion tensors by combining juxtaposition and explicit encoding. Because tensor scale, anisotropy type, and orientation are related to anatomical information relevant for DTI applications, we focus on visualizing tensor differences in these three aspects. As demonstrated in a user study, our new glyph design allows users to efficiently and effectively identify the tensor differences. We also apply our new glyphs to investigate the differences between DTI datasets of the human brain in two different contexts using different b-values, and to compare datasets from a healthy and HIV-infected subject.

关键词： Glyph Design comparative visualization Diffusion Tensor Field

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MoComp: A Tool for comparative visualization between Takes of Motion Capture Data 16

MoComp: A Tool for Comparative Visualization between Takes o...

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3rd International Symposium on Movement and Computing (MOCO)

作者： Malmstrom, Carl Zhang, Yaying Pasquier, Philippe Schiphorst, Thecla Bartram, Lyn Simon Fraser Univ Sch Interact Arts & Technol 102 Ave Surrey BC Canada

ISBN: (纸本)9781450343077

We present MoComp, an interactive visualization tool that allows users to identify and understand differences in motion between two takes of motion capture data. In MoComp, the body part position and motion is visualized focusing on angles of the joints making up each body part. This makes the tool useful for between-take and even between-subject comparison of particular movements since the angle data is independent of the size of the captured subject.

关键词： Movement visualization comparative visualization Motion Capture

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Visual Analysis of Multi-outcome Causal Graphs

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2025年第1期31卷 656-666页

作者： Fan, Mengjie Yu, Jinlu Weiskopf, Daniel Cao, Nan Wang, Huai-Yu Zhou, Liang Peking Univ Peking Univ Hlth Sci Ctr Inst Med Technol Beijing Peoples R China Peking Univ Natl Inst Hlth Data Sci NIHDS Beijing Peoples R China Chalmers Univ Technol Gothenburg Sweden Peking Univ NIHDS Beijing Peoples R China Univ Stuttgart Visualizat Res Ctr VISUS Stuttgart Germany Tongji Univ IDV Lab Shanghai Peoples R China Beijing Univ Chinese Med Inst Tradit Chinese Med Constitut & Prevent Treat Beijing Peoples R China

We introduce a visual analysis method for multiple causal graphs with different outcome variables, namely, multi-outcome causal graphs. Multi-outcome causal graphs are important in healthcare for understanding multimorbidity and comorbidity. To support the visual analysis, we collaborated with medical experts to devise two comparative visualization techniques at different stages of the analysis process. First, a progressive visualization method is proposed for comparing multiple state-of-the-art causal discovery algorithms. The method can handle mixed-type datasets comprising both continuous and categorical variables and assist in the creation of a fine-tuned causal graph of a single outcome. Second, a comparative graph layout technique and specialized visual encodings are devised for the quick comparison of multiple causal graphs. In our visual analysis approach, analysts start by building individual causal graphs for each outcome variable, and then, multi-outcome causal graphs are generated and visualized with our comparative technique for analyzing differences and commonalities of these causal graphs. Evaluation includes quantitative measurements on benchmark datasets, a case study with a medical expert, and expert user studies with real-world health research data.

关键词： Causal graph visualization and visual analysis causal discovery comparative visualization visual analysis in medicine

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Loops: Leveraging Provenance and visualization to Support Exploratory Data Analysis in Notebooks

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2025年第1期31卷 1213-1223页

作者： Eckelt, Klaus Gadhave, Kiran Lex, Alexander Streit, Marc Johannes Kepler Univ Linz Linz Austria Univ Utah Salt Lake City UT USA

Exploratory data science is an iterative process of obtaining, cleaning, profiling, analyzing, and interpreting data. This cyclical way of working creates challenges within the linear structure of computational notebooks, leading to issues with code quality, recall, and reproducibility. To remedy this, we present Loops, a set of visual support techniques for iterative and exploratory data analysis in computational notebooks. Loops leverages provenance information to visualize the impact of changes made within a notebook. In visualizations of the notebook provenance, we trace the evolution of the notebook over time and highlight differences between versions. Loops visualizes the provenance of code, markdown, tables, visualizations, and images and their respective differences. Analysts can explore these differences in detail in a separate view. Loops not only makes the analysis process transparent but also supports analysts in their data science work by showing the effects of changes and facilitating comparison of multiple versions. We demonstrate our approach's utility and potential impact in two use cases and feedback from notebook users from various backgrounds. This paper and all supplemental materials are available at https://***/79eyn.

关键词： comparative visualization computational notebooks provenance data science

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A Deep Learning for Optimization and visualization of Expressway Toll Lane Management

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IEEE ACCESS 2025年 13卷 7801-7818页

作者： Klaykul, Pattarapon Lee, Wilaiporn Srisomboon, Kanabadee Pipanmekaporn, Luepol Prayote, Akara King Mongkuts Univ Technol North Bangkok Dept Comp & Informat Sci Bangkok 10800 Thailand King Mongkuts Univ Technol North Bangkok Dept Elect & Comp Engn Bangkok 10800 Thailand

Managing the integration of Electronic Toll Collection (ETC) and Manual Toll Collection (MTC) systems is complex, especially during peak hours due to high traffic volumes. While ETC is more efficient, an inappropriate proportion of ETC lanes can increase congestion, making optimal ETC lane selection crucial. This paper proposes a novel framework combining deep learning and multi-objective optimization to improve toll plaza efficiency. A Gated Recurrent Unit (GRU) model with Optuna hyperparameter tuning predicts average queue lengths for various ETC lane proportions. The predictions guide the identification of the ideal ETC lane configuration to minimize queue lengths and balance traffic flow between MTC and ETC lanes. The framework dynamically adjusts ETC lane proportions based on real-time traffic data to ensure optimal lane allocations during peak hours. A multi-objective optimization function is applied to balance key objectives such as minimizing queue length, reducing queue time, lowering operational costs, and optimizing ETC utilization. Simulations with real-world data from high-traffic toll plazas demonstrate the framework's effectiveness, reducing queue lengths by up to 95.03% during peak hours, decreasing operational costs by 28.72%, and improving overall toll plaza performance. The results are visualized with graphs showing predicted average queue lengths across different ETC lane proportions, aiding stakeholders in understanding the relationship between ETC adoption and congestion. The framework provides insights into operational costs and resource allocation, enhancing financial and operational planning. Validated through extensive simulations with real-time data, this research advances empirical studies with thorough model validation and introduces innovative visualization techniques for toll plaza management.

关键词： Optimization Deep learning Real-time systems Data models Costs Predictive models Vehicle dynamics Resource management Optimization models Manuals Electronic toll collection (ETC) deep learning multi-objective optimization queue length prediction dynamic lane allocation toll plaza optimization real-time traffic data comparative visualization

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A framework for visual comparison of scalar fields with uncertainty

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VISUAL COMPUTER 2025年第7期41卷 4431-4448页

作者： Leonhardt, Viktor Wiebel, Alexander Garth, Christoph RPTU Kaiserslautern Landau Dept Comp Sci Gottlieb Daimler Str D-67663 Kaiserslautern Rheinland Pfalz Germany Hsch Worms Univ Appl Sci Dept Comp Sci Erenburgerstr 19 D-67549 Worms Rheinland Pfalz Germany

Scientists working with uncertain data, such as climate simulations, medical images, or ensembles of physical simulations, regularly confront the problem of comparing observations, e.g., to identify similarities, differences, or patterns. Current approaches in comparative visualization of uncertain scalar fields mainly rely on juxtaposition of both data and uncertainties, where each is represented using, e.g., color mapping or volume rendering. While interpretation of uncertain scalar data from visual encodings is already cognitively challenging, comparison of uncertain fields without explicit visualization support adds a further layer of complexity. In this paper, we present a theoretical framework to devise and describe a class of techniques that directly visualize differences between two or more uncertain scalar fields in a single image. We model each such technique as a combination of one or more interpolation stages, with the application of distance measures on random variables to the resulting distributions, and an appropriate visual encoding. Our framework captures existing methods and lends itself well to formulating new comparative visualization techniques for uncertain data for different visualization scenarios. Furthermore, by modeling uncertain scalar field differences as random variables themselves, we enable additional opportunities for comparison. We demonstrate the usefulness of our framework and its properties by applying it to effective comparative visualization techniques for several synthetic and real-world data sets.

关键词： Uncertainty visualization comparative visualization Ensembles visualization framework

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