research-article

Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps

Authors:

Nitesh V. Chawla,

Seung Hyun KimAuthors Info & Claims

IEEE Transactions on Visualization and Computer Graphics, Volume 25, Issue 1

Pages 1236 - 1245

https://doi.org/10.1109/TVCG.2018.2864808

Published: 01 January 2019 Publication History

Abstract

We present a novel visual representation and interface named the matrix of isosurface similarity maps (MISM) for effective exploration of large time-varying multivariate volumetric data sets. MISM synthesizes three types of similarity maps (i.e., self, temporal, and variable similarity maps) to capture the essential relationships among isosurfaces of different variables and time steps. Additionally, it serves as the main visual mapping and navigation tool for examining the vast number of isosurfaces and exploring the underlying time-varying multivariate data set. We present temporal clustering, variable grouping, and interactive filtering to reduce the huge exploration space of MISM. In conjunction with the isovalue and isosurface views, MISM allows users to identify important isosurfaces or isosurface pairs and compare them over space, time, and value range. More importantly, we introduce path recommendation that suggests, animates, and compares traversal paths for effectively exploring MISM under varied criteria and at different levels-of-detail. A silhouette-based method is applied to render multiple surfaces of interest in a visually succinct manner. We demonstrate the effectiveness of our approach with case studies of several time-varying multivariate data sets and an ensemble data set, and evaluate our work with two domain experts.

References

[1]

H. Akiba and K.-L. Ma. A tri-space visualization interface for analyzing time-varying multivariate volume data. In Proceedings of Eurographics - IEEE VGTC Symposium on Visualization, pages 115–122, 2007.

[2]

H. Akiba, C. Wang, and K.-L. Ma. AniViz: A template-based animation tool for volume visualization. IEEE Computer Graphics and Applications, 30 (5) pp. 61–71, 2010.

Digital Library

[3]

D. Angus, A. Smith, and J. Wiles. Conceptual recurrence plots: Revealing patterns in human discourse. IEEE Transactions on Visualization and Computer Graphics, 18 (6) pp. 988–997, 2012.

Digital Library

[4]

A. Biswas, S. Dutta, H.-W. Shen, and J. Woodring. An information-aware framework for exploring multivariate data sets. IEEE Transactions on Visualization and Computer Graphics, 19 (12) pp. 2683–2692, 2013.

Digital Library

[5]

S. Bruckner and T. Möller. Isosurface similarity maps. Computer Graphics Forum, 29 (3) pp. 773–782, 2010.

Digital Library

[6]

G.H. Bryan and J.M. Fritsch. A benchmark simulation for moist nonhydrostatic numerical models. Monthly Weather Review, 130 (12) pp. 2917–2928, 2002.

[7]

I. Demir, J. Kehrer, and R. Westermann. Screen-space silhouettes for visualizing ensembles of 3D isosurfaces. In Proceedings of IEEE Pacific Visualization Symposium, pages 204–208, 2016.

[8]

B.J. Frey and D. Dueck. Clustering by passing messages between data points. Science, 315: pp. 972–976, 2007.

[9]

S. Frey, F. Sadlo, and T. Ertl. Visualization of temporal similarity in field data. IEEE Transactions on Visualization and Computer Graphics, 18 (12) pp. 2023–2032, 2012.

Digital Library

[10]

M. Glatter, J. Huang, S. Ahern, J. Daniel, and A. Lu. Visualizing temporal patterns in large multivariate data using textual pattern matching. IEEE Transactions on Visualization and Computer Graphics, 14 (6) pp. 1467–1474, 2008.

Digital Library

[11]

M. Gleicher, D. Albers, R. Walker, I. Jusufi, C.D. Hansen, and J.C. Roberts. Visual comparison for information visualization. Information Visualization, 10 (4) pp. 289–309, 2011.

Digital Library

[12]

Y. Gu and C. Wang. TransGraph: Hierarchical exploration of transition relationships in time-varying volumetric data. IEEE Transactions on Visualization and Computer Graphics, 17 (12) pp. 2015–2024, 2011.

Digital Library

[13]

Y. Gu and C. Wang. iTree: Exploring time-varying data using indexable tree. In Proceedings of IEEE Pacific Visualization Symposium, pages 137–144, 2013.

[14]

H. Guo, H. Xiao, and X. Yuan. Scalable multivariate volume visualization and analysis based on dimension projection and parallel coordinates. IEEE Transactions on Visualization and Computer Graphics, 18 (9) pp. 1397–1410, 2012.

Digital Library

[15]

M. Haidacher, S. Bruckner, and E. Gröller. Volume analysis using multi-modal surface similarity. IEEE Transactions on Visualization and Computer Graphics, 17 (12) pp. 1969–1978, 2011.

Digital Library

[16]

H. Hoppe. Progressive meshes. In Proceedings of ACM SIGGRAPH Conference, pages 99–108, 1996.

[17]

M. Imre, J. Tao, and C. Wang. Efficient GPU-accelerated computation of isosurface similarity maps. In Proceedings of IEEE Pacific Visualization Symposium, pages 180–184, 2017.

[18]

H. Jänicke, M. Böttinger, and G. Scheuermann. Brushing of attribute clouds for the visualization of multivariate data. IEEE Transactions on Visualization and Computer Graphics, 14 (6) pp. 1459–1466, 2008.

Digital Library

[19]

T.J. Jankun-Kelly and K.-L. Ma. A study of transfer function generation for time-varying volume data. In Proceedings of Eurographics - IEEE TCVG Workshop on Volume Graphics, pages 51–66, 2001.

[20]

A. Joshi and P. Rheingans. Illustration-inspired techniques for visualizing time-varying data. In Proceedings of IEEE Visualization Conference, pages 679–686, 2005.

[21]

T. Karras. Maximizing parallelism in the construction of BVHs, octrees, and k-d trees. In Proceedings of ACM SIGGRAPH/Eurographics Conference on High-Performance Graphics, pages 33–37, 2012.

[22]

J. Kehrer and H. Hauser. Visualization and visual analysis of multifaceted scientific data: A survey. IEEE Transactions on Visualization and Computer Graphics, 19 (3) pp. 495–513, 2013.

Digital Library

[23]

O.D. Lampe, C. Correa, K.-L. Ma, and H. Hauser. Curve-centric volume reformation for comparative visualization. IEEE Transactions on Visualization and Computer Graphics, 15 (6) pp. 1235–1242, 2009.

Digital Library

[24]

X. Liu and H.-W. Shen. Association analysis for visual exploration of multivariate scientific data sets. IEEE Transactions on Visualization and Computer Graphics, 22 (1) pp. 955–964, 2016.

Digital Library

[25]

W.E. Lorensen and H.E. Cline. Marching cubes: A high resolution 3D surface construction algorithm. In Proceedings of ACM SIGGRAPH Conference, pages 163–169, 1987.

[26]

A. Lu and H.-W. Shen. Interactive storyboard for overall time-varying data visualization. In Proceedings of IEEE Pacific Visualization Symposium, pages 143–150, 2008.

[27]

K.-L. Ma. Visualizing time-varying volume data. IEEE Computing in Science and Engineering, 5 (2) pp. 34–42, 2003.

Digital Library

[28]

M.M. Malik, C. Heinzl, and M.E. Gröller, Comparative visualization for parameter studies of dataset series. IEEE Transactions on Visualization and Computer Graphics, 16 (5) pp. 829–840, 2010.

Digital Library

[29]

N. Marwan, J. Kurths, and P. Saparin. Generalised recurrence plot analysis for spatial data. Physics Letters A, 360 (4–5): pp. 545–551, 2007.

[30]

N. Sauber, H. Theisel, and H.-P. Seidel. Multifield-Graphs: An approach to visualizing correlations in multifield scalar data. IEEE Transactions on Visualization and Computer Graphics, 12 (5) pp. 917–924, 2006.

Digital Library

[31]

J. Schmidt, C. Heinzl, and S. Bruckner. VAICo: Visual analysis for image comparison. IEEE Transactions on Visualization and Computer Graphics, 19 (12) pp. 2090–2099, 2013.

Digital Library

[32]

D. Schneider, A. Wiebel, H. Carr, M. Hlawitschka, and G. Scheuermann. Interactive comparison of scalar fields based on largest contours with applications to flow visualization. IEEE Transactions on Visualization and Computer Graphics, 14 (6) pp. 1475–1482, 2008.

Digital Library

[33]

H.-W. Shen, L.-J. Chiang, and K.-L. Ma. A fast volume rendering algorithm for time-varying fields using a time-space partitioning (TSP) tree. In Proceedings of IEEE Visualization Conference, pages 371–377, 1999.

[34]

K. Stockinger, J. Shalf, K. Wu, and E.W. Bethel. Query-driven visualization of large data sets. In Proceedings of IEEE Visualization Conference, pages 167–174, 2005.

[35]

V. Verma and A.T. Pang. Comparative flow visualization. IEEE Transactions on Visualization and Computer Graphics, 10 (6) pp. 609–624, 2004.

Digital Library

[36]

C. Wang and J. Tao. Graphs in scientific visualization: A survey. Computer Graphics Forum, 36 (1) pp. 263–287, 2017.

Digital Library

[37]

C. Wang, H. Yu, R.W. Grout, K.-L. Ma, and J.H. Chen. Analyzing information transfer in time-varying multivariate data. In Proceedings of IEEE Pacific Visualization Symposium, pages 99–106, 2011.

[38]

C. Wang, H. Yu, and K.-L. Ma. Importance-driven time-varying data visualization. IEEE Transactions on Visualization and Computer Graphics, 14 (6) pp. 1547–1554, 2008.

Digital Library

[39]

J. Woodring and H.-W. Shen. Multi-variate, Multi-variate, time-varying, and comparative visualization with contextual cues. IEEE Transactions on Visualization and Computer Graphics, 12 (5) pp. 909–916, 2006.

Digital Library

[40]

J. Woodring, C. Wang, and H.-W. Shen. High dimensional direct rendering of time-varying volumetric data. In Proceedings of IEEE Visualization Conference, pages 417–424, 2003.

[41]

J.C. Yang, J. Hensley, H. Grün, and N. Thibieroz. Real-time concurrent linked list construction on the GPU. Computer Graphics Forum, 29 (4) pp. 1297–1304, 2010.

Digital Library

Cited By

Hong JHnatyshyn RSantos EMaciejewski RIsenberg T(2024)A Survey of Designs for Combined 2D+3D Visual RepresentationsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.338851630:6(2888-2902)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3388516
Han JZheng HBi C(2024)KD-INR: Time-Varying Volumetric Data Compression via Knowledge Distillation-Based Implicit Neural RepresentationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.334537330:10(6826-6838)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3345373
He XTao YYang SDai HLin H(2023)voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.318909429:10(4296-4311)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TVCG.2022.3189094
Show More Cited By

Index Terms

Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
    2. Shape modeling
2. Human-centered computing
  1. Human computer interaction (HCI)

Index terms have been assigned to the content through auto-classification.

Recommendations

Isosurface extraction from volume data using particle systems
Isosurface similarity maps
EuroVis'10: Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization

In this paper, we introduce the concept of isosurface similarity maps for the visualization of volume data. Isosurface similarity maps present structural information of a volume data set by depicting similarities between individual isosurfaces ...
Interactive Isosurface Ray Tracing of Time-Varying Tetrahedral Volumes

We describe a system for interactively rendering isosurfaces of tetrahedral finite-element scalar fields using coherent ray tracing techniques on the CPU. By employing state-of-the art methods in polygonal ray tracing, namely aggressive packet/frustum ...

Comments

Information & Contributors

Information

Published In

cover image IEEE Transactions on Visualization and Computer Graphics

IEEE Transactions on Visualization and Computer Graphics Volume 25, Issue 1

Jan. 2019

1266 pages

ISSN:1077-2626

Issue’s Table of Contents

1077-2626 © 2018 IEEE.

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 January 2019

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hong JHnatyshyn RSantos EMaciejewski RIsenberg T(2024)A Survey of Designs for Combined 2D+3D Visual RepresentationsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.338851630:6(2888-2902)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1109/TVCG.2024.3388516
Han JZheng HBi C(2024)KD-INR: Time-Varying Volumetric Data Compression via Knowledge Distillation-Based Implicit Neural RepresentationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.334537330:10(6826-6838)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3345373
He XTao YYang SDai HLin H(2023)voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific DataIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.318909429:10(4296-4311)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1109/TVCG.2022.3189094
Lu XXu YLi GChen YShan G(2023)MVST-SciVis: narrative visualization and analysis of compound events in scientific dataJournal of Visualization10.1007/s12650-022-00893-026:3(687-703)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1007/s12650-022-00893-0
Liu CLi YYang CYuan X(2019)Event-based exploration and comparison on time-varying ensemblesJournal of Visualization10.1007/s12650-019-00608-y23:1(111-123)Online publication date: 30-Oct-2019
https://dl.acm.org/doi/10.1007/s12650-019-00608-y
Imre MHan JDominski JChurchill MKube RChang CPeterka TGuo HWang C(2019)ContourNet: Salient Local Contour Identification for Blob Detection in Plasma Fusion Simulation DataAdvances in Visual Computing10.1007/978-3-030-33720-9_22(289-301)Online publication date: 7-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-33720-9_22

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents