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20th Eurographics/IEEE VGT conference on visualization, EuroVis 2018

ISBN: (纸本)9783038680604

The proceedings contain 21 papers. The topics discussed include: evolutionary lines for flow visualization;colored stochastic shadow mapping for direct volume rendering;visualizing functional regions by analysis of geo-textual data;visual analysis of parallel interval events;comparative visual analysis of pelvic organ segmentations;improving provenance data interaction for visual storytelling in medical imaging data exploration;ChemoExplorer: a dashboard for the visual analysis of chemotherapy response in breast cancer patients;TapVis: a data visualization approach for assessment of alternating tapping performance in patients with Parkinsonâ€™s disease;sketching temporal uncertainty - an exploratory user study;and issues and suggestions for the development of a biodiversity data visualization support tool.

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Cluster-based analysis of multi-parameter distributions in cloud simulation ensembles

Cluster-based analysis of multi-parameter distributions in c...

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2019 conference on Vision, Modeling and visualization, VMV 2019

作者： Kumpf, Alexander Stumpfegger, Josef Westermann, Rüdiger Germany

ISBN: (纸本)9783038680987

The proposed approach enables a comparative visual exploration of multi-parameter distributions in time-varying 3D ensemble simulations. To investigate whether dominant trends in such distributions occur, we consider the simulation elements in each dataset—per ensemble member and time step—as elements in the multi-dimensional parameter space, and use t-SNE to project these elements into 2D space. To find groups of elements with similar parameter values in each time step, the resulting projections are clustered via k-Means. Since elements with similar data values can be disconnected in one single projection, we compute an ensemble of projections using multiple t-SNE runs and use evidence accumulation to determine sets of elements that are stably clustered together. We build upon per-cluster multi-parameter distribution functions to quantify cluster similarity, and merge clusters in different ensemble members. By applying the proposed approach to a time-varying ensemble, the temporal development of clusters, and in particular their stability over time can be analyzed. We apply this approach to analyze a time-varying ensemble of 3D cloud simulations. The visualizations via t-SNE, parallel coordinate plots and scatter plot matrices show dependencies between the simulation results and the simulation parameters used to generate the ensemble, and they provide insight into the temporal ensemble variability regarding the major trends in the multi-parameter distributions. © 2019 The Author(s) Eurographics Proceedings © 2019 The Eurographics Association.

关键词： Distribution functions

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visual Analytics for Decomposing Temporal Event Series of Production Lines 22

Visual Analytics for Decomposing Temporal Event Series of Pr...

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22nd International conference Information visualisation (iv) - Biomedical visualization, visualisation on Built and Rural Environments and Geometric Modelling and Imaging

作者： Herr, Dominik Beck, Fabian Ertl, Thomas Univ Stuttgart Inst Visualizat & Interact Syst Stuttgart Germany Univ Stuttgart Grad Sch Excellence Adv Mfg Engn Stuttgart Germany Univ Duisburg Essen Paluno Duisburg Germany

ISBN: (纸本)9781538672020

The temporal analysis of events in a production line helps manufacturing experts get a better understanding of the line's performance and provides ideas for improvement. Especially the identification of recurring error patterns is important, because these patterns can be an indicator of systematic production issues. We present a visual analytics approach to analyze event reports of a production line. Reported events are shown as a time series plot that can be decomposed into a trend, seasonal, and remainder component by applying Seasonal Trend decomposition using Loess (STL). To find specific event patterns, the data is filtered based on aspects such as the event description or the processed product. Identified temporal patterns can be extracted from the original event series and compared visually with each other. In addition to predefined settings, experts can define a subseries of the event series and the period length of STL's seasonal component through an automatically optimized brushing of the undecomposed plot. We developed the approach together with an industry partner. To evaluate our approach, we conducted two pair analytics sessions with our industry partner's experts. We demonstrate use cases from these sessions that showcase our approach's analytical potential. Moreover, we present general expert feedback that we collected through semi-structured interviews after the pair analytics sessions.

关键词： Manufacturing Industry 4.0 spatio-temporal data correlation analysis

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visual Attention and Haptic Control: A Cross-Study

Visual Attention and Haptic Control: A Cross-Study

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IEEE International conference on Multimedia Big data (BigMM)

作者： Hong Xue Tiesong Zhao Weiling Chen Qian Liu Shaohua Zheng Chang Wen Chen Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou China Dept. of Computer Science and Technology Dalian University of Technology Dalian China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China

The variety of multimedia big data has promoted emerging applications of multiple sensorial media (mulsemedia) types, in which haptic information attracts increasing attentions. Until now, the interaction between haptic signal and conventional audio-visual signals have not been fully investigated. In this work, we make an exploration on the cross-modal interactivity in task-driven scenarios. We first explore the correlation between visual attention and haptic control in three designed tasks: random-trajectory, fixed-trajectory and obstacle-avoidance. Then, we propose a visual-haptic interaction model that estimates kinesthetic position of haptic control with the information of gaze only. By incorporating a Long Short-Term Memory (LSTM) neural network, the proposed model provides effective prediction in the scenarios of fixed-trajectory and obstacle-avoidance, with its performance superior to other selected machine learning-based models. To further examine our model, we execute it in a haptic control task using visual guidance. Implementation results show a high task achievement rate.

关键词： Task analysis Haptic interfaces visualization Correlation Software Tracking Process control

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EuroVis 2018 - Eurographics/IEEE VGTC conference on visualization 2018, Posters

EuroVis 2018 - Eurographics/IEEE VGTC Conference on Visualiz...

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20th Eurographics/IEEE VGTC conference on visualization, EuroVis 2018

ISBN: (纸本)9783038680659

The proceedings contain 15 papers. The topics discussed include: CV3: visual exploration, assessment, and comparison of CVs;extending document exploration with image retrieval: concept and first results;visually exploring data provenance and quality of open data;case studies of shareable personal map visualization;an eye-tracking study on sparklines within textual context;network analysis for financial fraud detection;validation of quantitative measures for edge bundling by comparing with human feeling;exploring uncertainty in image segmentation ensembles;supporting visual parameter analysis of time series segmentation with correlation calculations;the impact of visualizing uncertainty on train trip selection;and categorizing uncertainties in the process of segmenting and labeling time series data.

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LDSScanner: Exploratory analysis of Low-Dimensional Structures in High-Dimensional datasets

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 236-245页

作者： Xia, Jiazhi Ye, Fenjin Chen, Wei Wang, Yusi Chen, Weifeng Ma, Yuxin Tung, Anthony K. H. Cent South Univ Changsha Hunan Peoples R China Zhejiang Univ Hangzhou Zhejiang Peoples R China Zhejiang Univ Finance & Econ Hangzhou Zhejiang Peoples R China Natl Univ Singapore Singapore Singapore

Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the x axis and y axis using 1D multidimensional scaling, respectively. Unlike traditional dimensionality reduction methods that preserve various kinds of distances among points, the LTSD-GD view presents the distribution of pointwise LTS (x axis) and the variation of LTS in structures (the combination of x axis and y axis). We design and implement a suite of visual tools for navigating and reasoning about intrinsic structures of a high-dimensional dataset. Three case studies verify the effectiveness of our approach.

关键词： High-dimensional data low-dimensional structure subspace manifold visual exploration

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A user-centered design and analysis of an electrostatic haptic touchscreen system for students with visual impairments

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES 2018年 109卷 102-111页

作者： Bateman, Amelia Zhao, Oliver K. Bajcsy, Andrea V. Jennings, Mathew C. Toth, Bryan N. Cohen, Alexa J. Horton, Emily L. Khattar, Anish Kuo, Ryan S. Lee, Felix A. Lim, Meilin K. Migasiuk, Laura W. Renganathan, Ramkesh Zhang, Amy Oliveira, Marcio A. Univ Maryland College Pk MD 20742 USA

Students who are visually impaired face unique challenges when learning mathematical concepts due to the visual nature of graphs, charts, tables, and plots. While touchscreens have been explored as a means to assist people with visual impairments in learning mathematical concepts, many devices are not standalone, were not developed with a user-centered design approach, and have not been tested with users who are visually impaired. This research details the user-centered design and analysis of an electrostatic touchscreen system for displaying graph-based visual information to individuals who are visually impaired. Feedback from users and experts within the visually-impaired community informed the iterative development of our software. We conducted a usability study consisting of locating haptic points in order to test the efficacy and efficiency of the system and to determine patterns of user interactions with the touchscreen. The results showed that: (1) participants correctly located haptic points with an accuracy rate of 69.83% and an average time of 15.34 s out of 116 total trials, (2) accuracy increased across trials, (3) efficient patterns of user interaction involved either a systematic approach or a rapid exploration of the screen, and (4) haptic elements placed near the corners of the screen were more easily located. Our user-centered design approach resulted in an intuitive interface for people with visual impairments and laid the foundation for demonstrating this device's potential to depict mathematical data shown in graphs. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Haptic visual impairment Assistive technology User-centered design Electrostatic touchscreen Mathematics education

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Intelligent exploration of Large-Scale data: What Can We Learn in Two Passes?

Intelligent Exploration of Large-Scale Data: What Can We Lea...

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IEEE International conference on Big data

作者： Chandrika Kamath Lawrence Livermore National Laboratory Livermore USA

ISBN: (数字)9781728108582

ISBN: (纸本)9781728108599

Exploring large-scale data to determine an analysis approach is often made difficult by the sheer size of the data. If the characteristics of the data, including any variations within the data set, are not taken into account, our choice of algorithms and associated parameters may not be optimal, resulting in possibly inaccurate conclusions drawn from the data. Iteratively refining the analysis approach, as is normally done with smaller data sets, becomes prohibitively expensive for large-scale data. A typical solution is to randomly subsample the data set and determine the analysis algorithms using the characteristics of this subsample. In this paper, we propose the use of an improved sampling algorithm that is modified to identify well-distributed samples in a single pass through the data set. We then describe how we can use this subsample to probe the data in a second pass. Using very simple, low-cost algorithms, we demonstrate that the additional insight gained in this second pass can improve the process of analyzing large-scale data sets.

关键词： data mining data visualization visual analytics Machine learning algorithms Big data Feature extraction

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Coupling the Uintah Framework and the VisIt Toolkit for Parallel In Situ data analysis and visualization and Computational Steering

Coupling the Uintah Framework and the VisIt Toolkit for Para...

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International Supercomputing conference (ISC) on High Performance

作者： Sanderson, Allen Humphrey, Alan Schmidt, John Sisneros, Robert Univ Utah Sci Imaging & Comp Inst Salt Lake City UT 84112 USA Univ Illinois Natl Ctr Supercomp Applicat Urbana IL 61801 USA

ISBN: (纸本)9783030024659;9783030024642

data analysis and visualization are an essential part of the scientific discovery process. As HPC simulations have grown, I/O has become a bottleneck, which has required scientists to turn to in situ tools for simulation data exploration. Incorporating additional data, such as runtime performance data, into the analysis or I/O phases of a workflow is routinely avoided for fear of excaberting performance issues. The paper presents how the Uintah Framework, a suite of HPC libraries and applications for simulating complex chemical and physical reactions, was coupled with VisIt, an interactive analysis and visualization toolkit, to allow scientists to perform parallel in situ visualization of simulation and runtime performance data. An additional benefit of the coupling made it possible to create a "simulation dashboard" that allowed for in situ computational steering and visual debugging.

关键词： In situ visualization Runtime performance data visual debugging Computational steering

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Creation of anatomical models from CT data

Creation of anatomical models from CT data

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Saratov Fall Meeting (SFM) on Laser Physics and Photonics XVIII;and Computational Biophysics and analysis of Biomedical data iv / 5th International Symposium on Optics and Biophotonics

作者： Alaytsev, Innokentiy K. Danilova, Tatyana V. Manturov, Alexey O. Mareev, Gleb O. Mareev, Oleg V. Yuri Gagarin State Tech Univ Saratov 77 Politech Skaya St Saratov 410054 Russia Saratov State Med Univ Bolshaya Kazachia St 112 Saratov 410012 Russia

ISBN: (数字)9781510620049

ISBN: (纸本)9781510620049

Computed tomography is a great source of biomedical data because it allows a detailed exploration of complex anatomical structures. Some structures are not visible on CT scans, and some are hard to distinguish due to partial volume effect. CT datasets require preprocessing before using them as anatomical models in a simulation system. The work describes segmentation and data transformation methods for an anatomical model creation from the CT data. The result models may be used for visual and haptic rendering and drilling simulation in a virtual surgery system.

关键词： anatomical model virtual reality computed tomography segmentation

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