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Similarity Measures for Enhancing Interactive Streamline Seeding

T McLoughlin, M. W Jones, R. S Laramee, R Malki, I Masters, C. D Hansen, Mark Jones Orcid Logo, Ian Masters Orcid Logo, Bob Laramee Orcid Logo

IEEE Transactions on Visualization and Computer Graphics, Volume: 19, Issue: 8, Pages: 1342 - 1353

Swansea University Authors: Mark Jones Orcid Logo, Ian Masters Orcid Logo, Bob Laramee Orcid Logo

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DOI (Published version): 10.1109/TVCG.2012.150

Abstract

Streamline seeding rakes are widely used in vector field visualization. We present new approaches for calculating similarity between integral curves (streamlines and pathlines). While others have used similarity distance measures, the computational expense involved with existing techniques is relati...

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Published in: IEEE Transactions on Visualization and Computer Graphics
ISSN: 1077-2626
Published: 2012
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URI: https://cronfa.swan.ac.uk/Record/cronfa15062
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spelling 2019-06-14T16:57:24.7470790 v2 15062 2013-06-13 Similarity Measures for Enhancing Interactive Streamline Seeding 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 6fa19551092853928cde0e6d5fac48a1 0000-0001-7667-6670 Ian Masters Ian Masters true false 7737f06e2186278a925f6119c48db8b1 0000-0002-3874-6145 Bob Laramee Bob Laramee true false 2013-06-13 SCS Streamline seeding rakes are widely used in vector field visualization. We present new approaches for calculating similarity between integral curves (streamlines and pathlines). While others have used similarity distance measures, the computational expense involved with existing techniques is relatively high due to the vast number of euclidean distance tests, restricting interactivity and their use for streamline seeding rakes. We introduce the novel idea of computing streamline signatures based on a set of curve-based attributes. A signature produces a compact representation for describing a streamline. Similarity comparisons are performed by using a popular statistical measure on the derived signatures. We demonstrate that this novel scheme, including a hierarchical variant, produces good clustering results and is computed over two orders of magnitude faster than previous methods. Similarity-based clustering enables filtering of the streamlines to provide a nonuniform seeding distribution along the seeding object. We show that this method preserves the overall flow behavior while using only a small subset of the original streamline set. We apply focus + context rendering using the clusters which allows for faster and easier analysis in cases of high visual complexity and occlusion. The method provides a high level of interactivity and allows the user to easily fine tune the clustering results at runtime while avoiding any time-consuming recomputation. Our method maintains interactive rates even when hundreds of streamlines are used. Journal Article IEEE Transactions on Visualization and Computer Graphics 19 8 1342 1353 1077-2626 3 7 2012 2012-07-03 10.1109/TVCG.2012.150 http://cs.swan.ac.uk/%7Ecsmark/PDFS/similaritymeasures.pdf COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2019-06-14T16:57:24.7470790 2013-06-13T13:57:06.0266081 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science T McLoughlin 1 M. W Jones 2 R. S Laramee 3 R Malki 4 I Masters 5 C. D Hansen 6 Mark Jones 0000-0001-8991-1190 7 Ian Masters 0000-0001-7667-6670 8 Bob Laramee 0000-0002-3874-6145 9 0015062-15042015114606.pdf similaritymeasures.pdf 2015-04-15T11:46:06.6670000 Output 664801 application/pdf Accepted Manuscript true 2015-04-14T00:00:00.0000000 true
title Similarity Measures for Enhancing Interactive Streamline Seeding
spellingShingle Similarity Measures for Enhancing Interactive Streamline Seeding
Mark Jones
Ian Masters
Bob Laramee
title_short Similarity Measures for Enhancing Interactive Streamline Seeding
title_full Similarity Measures for Enhancing Interactive Streamline Seeding
title_fullStr Similarity Measures for Enhancing Interactive Streamline Seeding
title_full_unstemmed Similarity Measures for Enhancing Interactive Streamline Seeding
title_sort Similarity Measures for Enhancing Interactive Streamline Seeding
author_id_str_mv 2e1030b6e14fc9debd5d5ae7cc335562
6fa19551092853928cde0e6d5fac48a1
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author_id_fullname_str_mv 2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones
6fa19551092853928cde0e6d5fac48a1_***_Ian Masters
7737f06e2186278a925f6119c48db8b1_***_Bob Laramee
author Mark Jones
Ian Masters
Bob Laramee
author2 T McLoughlin
M. W Jones
R. S Laramee
R Malki
I Masters
C. D Hansen
Mark Jones
Ian Masters
Bob Laramee
format Journal article
container_title IEEE Transactions on Visualization and Computer Graphics
container_volume 19
container_issue 8
container_start_page 1342
publishDate 2012
institution Swansea University
issn 1077-2626
doi_str_mv 10.1109/TVCG.2012.150
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://cs.swan.ac.uk/%7Ecsmark/PDFS/similaritymeasures.pdf
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description Streamline seeding rakes are widely used in vector field visualization. We present new approaches for calculating similarity between integral curves (streamlines and pathlines). While others have used similarity distance measures, the computational expense involved with existing techniques is relatively high due to the vast number of euclidean distance tests, restricting interactivity and their use for streamline seeding rakes. We introduce the novel idea of computing streamline signatures based on a set of curve-based attributes. A signature produces a compact representation for describing a streamline. Similarity comparisons are performed by using a popular statistical measure on the derived signatures. We demonstrate that this novel scheme, including a hierarchical variant, produces good clustering results and is computed over two orders of magnitude faster than previous methods. Similarity-based clustering enables filtering of the streamlines to provide a nonuniform seeding distribution along the seeding object. We show that this method preserves the overall flow behavior while using only a small subset of the original streamline set. We apply focus + context rendering using the clusters which allows for faster and easier analysis in cases of high visual complexity and occlusion. The method provides a high level of interactivity and allows the user to easily fine tune the clustering results at runtime while avoiding any time-consuming recomputation. Our method maintains interactive rates even when hundreds of streamlines are used.
published_date 2012-07-03T03:17:12Z
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