research-article

A new weaving technique for handling overlapping regions

Authors:

Martin Luboschik,

Heidrun SchumannAuthors Info & Claims

AVI '10: Proceedings of the International Conference on Advanced Visual Interfaces

Pages 25 - 32

https://doi.org/10.1145/1842993.1842999

Published: 26 May 2010 Publication History

Abstract

The use of transparencies is a common strategy in visual representations to guarantee the visibility of different overlapping graphical objects, especially, if no visibility-deciding order is given (e.g., importance, depth). Alpha-blending, however, could generate new colors that are not specified by the given color scale and overlapping shapes may become difficult to be separated visually and the selection of specific elements would be difficult. In this paper, we present a new approach for representing overlapping regions: Instead of blending different colors, our weaving technique separates the original colors and shapes are easier to differentiate. Due to a deterministic weaving order, all overlapping objects are visible. We apply our approach to scatter plot visualizations to enhance the communication of overlapping clusters.

References

[1]

P. Baudisch and C. Gutwin. Multiblending: Displaying overlapping windows simultaneously without the drawbacks of alpha blending. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems (CHI '04), pages 367--374, 2004.

Digital Library

[2]

J. Chuang, D. Weiskopf, and T. Möller. Hue-preserving color blending. IEEE Transactions on Visualization and Computer Graphics, 15(6):1275--1282, 2009.

Digital Library

[3]

H. Dolfing. A visual analytics framework for feature and classifier engineering. Master's thesis, University of Konstanz, 2007.

[4]

G. Ellis and A. Dix. A taxonomy of clutter reduction for information visualisation. IEEE Transactions on Visualization and Computer Graphics, 13(6):1216--1223, 2007.

Digital Library

[5]

N. Elmqvist, P. Dragicevic, and J.-D. Fekete. Rolling the dice: Multidimensional visual exploration using scatterplot matrix navigation. IEEE Transactions on Visualization and Computer Graphics, 14(6):1141--1148, 2008.

[6]

S. Few. Solutions to the Problem of Over-Plotting in Graphs. Perceptual Edge - Visual Business Intelligence Newsletter, September/October 2008.

[7]

Y.-H. Fua, M. O. Ward, and E. A. Rundensteiner. Hierarchical parallel coordinates for exploration of large datasets. In Proceedings of IEEE Visualization '99 (VIS'99), pages 43--50, 1999.

Digital Library

[8]

H. Hagh-Shenas, S. Kim, V. Interrante, and C. Healey. Weaving versus blending: A quantitative assessment of the information carrying capacities of two alternative methods for conveying multivariate data with color. IEEE Transactions on Visualization and Computer Graphics, 13(6):1270--1277, 2007.

Digital Library

[9]

H. Hauser, F. Ledermann, and H. Doleisch. Angular brushing of extended parallel coordinates. In Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02), pages 127--134, 2002.

Digital Library

[10]

C. G. Healey. Choosing effective colours for data visualization. In Proceedings of IEEE Visualization (VIS'96), pages 263--271, 1996.

Digital Library

[11]

C. G. Healey, L. Tateosian, J. T. Enns, and M. Remple. Perceptually-based brush strokes for nonphotorealistic visualization. ACM Transactions on Graphics, 23(1):64--96, 2004.

Digital Library

[12]

G. Kindlmann, E. Reinhard, and S. Creem. Face-based luminance matching for perceptual colormap generation. In Proceedings of IEEE Visualization (VIS '02), pages 299--306, 2002.

Digital Library

[13]

K. T. McDonnell and K. Mueller. Illustrative parallel coordinates. Computer Graphics Forum, 27(3):1031--1038, 2008.

Digital Library

[14]

M. Novotny and H. Hauser. Outlier-preserving focus+context visualization in parallel coordinates. IEEE Transactions on Visualization and Computer Graphics, 12(5):893--900, 2006.

Digital Library

[15]

G. Ramos, G. Robertson, M. Czerwinski, D. Tan, P. Baudisch, K. Hinckley, and M. Agrawala. Tumble! splat! helping users access and manipulate occluded content in 2d drawings. In Proceedings of the Working Conference on Advanced Visual Interfaces (AVI'06), pages 428--435, 2006.

Digital Library

[16]

T. Schreck, M. Schüßler, K. Worm, and F. Zeilfelder. Butterfly plots for visual analysis of large point cloud data. In Proceedings of the 16th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG'08), pages 33--40, 2008.

[17]

C. Tominski, P. Schulze-Wollgast, and H. Schumann. 3d information visualization for time dependent data on maps. In Proceedings of the 9th International Conference on Information Visualisation (IV'05), pages 175--181, 2005.

Digital Library

[18]

T. Urness, V. Interrante, I. Marusic, E. Longmire, and B. Ganapathisubramani. Effectively visualizing multi-valued flow data using color and texture. In Proceedings of IEEE Visualization (VIS'03), pages 115--121, 2003.

Digital Library

[19]

L. Wang, J. Giesen, K. T. McDonnell, P. Zolliker, and K. Mueller. Color design for illustrative visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6):1739--1754, 2008.

Digital Library

[20]

C. W. William, W. Emigh, G. Liu, R. M. Taylor, J. T. Enns, and C. G. Healey. Oriented texture slivers: A technique for local value estimation of multiple scalar fields. In Proceedings of Graphics Interface (GI2000), 2000.

[21]

J. S. Yi, Y. ah Kang, J. Stasko, and J. A. Jacko. Toward a deeper understanding of the role of interaction in information visualization. IEEE Transactions on Visualization and Computer Graphics, 13(6):1224--1231, 2007.

Digital Library

Cited By

Lobato Nde Araujo TMeiguins Bdos Santos C(2024)A Review of Techniques for Reducing Shortcomings in Classical Information Visualization Charts2024 28th International Conference Information Visualisation (IV)10.1109/IV64223.2024.00013(1-7)Online publication date: 22-Jul-2024
https://doi.org/10.1109/IV64223.2024.00013
Shi RZhang J(2024)Empty Area Matters: Bivariate K-Nearest Neighbor Based Scatterplot Overlap Removal Algorithm2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI)10.1109/ICETCI61221.2024.10594437(775-779)Online publication date: 24-May-2024
https://doi.org/10.1109/ICETCI61221.2024.10594437
Li ZShi RLiu YLong SGuo ZJia SZhang J(2022)Dual Space Coupling Model Guided Overlap-Free ScatterplotIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209459(1-11)Online publication date: 2022
https://doi.org/10.1109/TVCG.2022.3209459
Show More Cited By

Index Terms

A new weaving technique for handling overlapping regions
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction devices
      1. Touch screens
  2. Interaction design

Recommendations

An interface mechanism for encapsulating weaving in class-based AOP
SPLAT '07: Proceedings of the 5th workshop on Software engineering properties of languages and aspect technologies

Aspect-oriented programming (AOP) separates crosscutting concerns from primary concerns. These concerns are woven together by a weaver. Although AOP provides a good module mechanism, it is not necessarily easy for a programmer to understand the overall ...
Advice weaving in AspectJ
AOSD '04: Proceedings of the 3rd international conference on Aspect-oriented software development

This paper describes the implementation of advice weaving in AspectJ. The AspectJ language picks out dynamic join points in a program's execution with pointcuts and uses advice to change the behavior at those join points. The core task of AspectJ's ...
Incremental aspect weaving: an approach for faster AOP learning
SAC '15: Proceedings of the 30th Annual ACM Symposium on Applied Computing

Aspect oriented programming (AOP) is a programming paradigm that provides mechanisms for encapsulating cross-cutting concerns. Developers who want to learn AOP often experiment with aspects and different Pointcuts on their existing code bases to try and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

AVI '10: Proceedings of the International Conference on Advanced Visual Interfaces

May 2010

427 pages

ISBN:9781450300766

DOI:10.1145/1842993

Editor:
Giuseppe Santucci
Dipartimento di Informatica e Sistemistica, Sapienza Universita di Roma, Italy

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Consulta Umbria SRL

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 May 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

AVI '10

Sponsor:

AVI '10: The International Conference on Advanced Visual Interfaces

May 26 - 28, 2010

Roma, Italy

Acceptance Rates

Overall Acceptance Rate 128 of 490 submissions, 26%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
387
Total Downloads

Downloads (Last 12 months)18
Downloads (Last 6 weeks)1

Reflects downloads up to 18 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lobato Nde Araujo TMeiguins Bdos Santos C(2024)A Review of Techniques for Reducing Shortcomings in Classical Information Visualization Charts2024 28th International Conference Information Visualisation (IV)10.1109/IV64223.2024.00013(1-7)Online publication date: 22-Jul-2024
https://doi.org/10.1109/IV64223.2024.00013
Shi RZhang J(2024)Empty Area Matters: Bivariate K-Nearest Neighbor Based Scatterplot Overlap Removal Algorithm2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI)10.1109/ICETCI61221.2024.10594437(775-779)Online publication date: 24-May-2024
https://doi.org/10.1109/ICETCI61221.2024.10594437
Li ZShi RLiu YLong SGuo ZJia SZhang J(2022)Dual Space Coupling Model Guided Overlap-Free ScatterplotIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2022.3209459(1-11)Online publication date: 2022
https://doi.org/10.1109/TVCG.2022.3209459
Trautner TBruckner S(2021)Line Weaver: Importance‐Driven Order Enhanced Rendering of Dense Line ChartsComputer Graphics Forum10.1111/cgf.1431640:3(399-410)Online publication date: 29-Jun-2021
https://doi.org/10.1111/cgf.14316
LYi SJo JSeo J(2021)Comparative Layouts Revisited: Design Space, Guidelines, and Future DirectionsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303041927:2(1525-1535)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3030419
Dharmavarapu PM.B.S S(2021)Aramid fibre as potential reinforcement for polymer matrix composites: a reviewEmergent Materials10.1007/s42247-021-00246-x5:5(1561-1578)Online publication date: 19-Jul-2021
https://doi.org/10.1007/s42247-021-00246-x
Lu MWang SLanir JFish NYue YCohen-Or DHuang H(2020)Winglets: Visualizing Association with Uncertainty in Multi-class ScatterplotsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.293481126:1(770-779)Online publication date: Jan-2020
https://doi.org/10.1109/TVCG.2019.2934811
Hu RSha TVan Kaick ODeussen OHuang H(2020)Data Sampling in Multi-view and Multi-class Scatterplots via Set Cover OptimizationIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.293479926:1(739-748)Online publication date: Jan-2020
https://doi.org/10.1109/TVCG.2019.2934799
Chen XGe TZhang JChen BFu CDeussen OWang Y(2020)A Recursive Subdivision Technique for Sampling Multi-class ScatterplotsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.293454126:1(729-738)Online publication date: Jan-2020
https://doi.org/10.1109/TVCG.2019.2934541
Zhou MAi TZhou GHu W(2020)A Visualization Method for Mining Colocation Patterns Constrained by a Road NetworkIEEE Access10.1109/ACCESS.2020.29801688(51933-51944)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2980168
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents