2020
Sane, Sudhanshu; Bujack, Roxana; Garth, Christoph; Childs, Hank
A Survey of Seed Placement and Streamline Selection Techniques Journal Article
In: Computer Graphics Forum, vol. 39, no. 3, pp. 785-809, 2020.
Abstract | Links | BibTeX | Tags: • Human-centered computing → Scientific visualization, CCS Concepts
@article{https://doi.org/10.1111/cgf.14036,
title = {A Survey of Seed Placement and Streamline Selection Techniques},
author = {Sudhanshu Sane and Roxana Bujack and Christoph Garth and Hank Childs},
url = {http://www.google.com/url?q=http%3A%2F%2Fwww.informatik.uni-leipzig.de%2F~bujack%2F2020Sane.pdf&sa=D&sntz=1&usg=AOvVaw1ezsZzR1EQcQw-61qWNOD-},
doi = {https://doi.org/10.1111/cgf.14036},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Computer Graphics Forum},
volume = {39},
number = {3},
pages = {785-809},
abstract = {Streamlines are an extensively utilized flow visualization technique for understanding, verifying, and exploring computational fluid dynamics simulations. One of the major challenges associated with the technique is selecting which streamlines to display. Using a large number of streamlines results in dense, cluttered visualizations, often containing redundant information and occluding important regions, whereas using a small number of streamlines could result in missing key features of the flow. Many solutions to select a representative set of streamlines have been proposed by researchers over the past two decades. In this state-of-the-art report, we analyze and classify seed placement and streamline selection (SPSS) techniques used by the scientific flow visualization community. At a high-level, we classify techniques into automatic and manual techniques, and further divide automatic techniques into three strategies: density-based, feature-based, and similarity-based. Our analysis evaluates the identified strategy groups with respect to focus on regions of interest, minimization of redundancy, and overall computational performance. Finally, we consider the application contexts and tasks for which SPSS techniques are currently applied and have potential applications in the future.},
keywords = {• Human-centered computing → Scientific visualization, CCS Concepts},
pubstate = {published},
tppubtype = {article}
}
Streamlines are an extensively utilized flow visualization technique for understanding, verifying, and exploring computational fluid dynamics simulations. One of the major challenges associated with the technique is selecting which streamlines to display. Using a large number of streamlines results in dense, cluttered visualizations, often containing redundant information and occluding important regions, whereas using a small number of streamlines could result in missing key features of the flow. Many solutions to select a representative set of streamlines have been proposed by researchers over the past two decades. In this state-of-the-art report, we analyze and classify seed placement and streamline selection (SPSS) techniques used by the scientific flow visualization community. At a high-level, we classify techniques into automatic and manual techniques, and further divide automatic techniques into three strategies: density-based, feature-based, and similarity-based. Our analysis evaluates the identified strategy groups with respect to focus on regions of interest, minimization of redundancy, and overall computational performance. Finally, we consider the application contexts and tasks for which SPSS techniques are currently applied and have potential applications in the future.
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1.
Sane, Sudhanshu; Bujack, Roxana; Garth, Christoph; Childs, Hank
A Survey of Seed Placement and Streamline Selection Techniques Journal Article
In: Computer Graphics Forum, vol. 39, no. 3, pp. 785-809, 2020.
@article{https://doi.org/10.1111/cgf.14036,
title = {A Survey of Seed Placement and Streamline Selection Techniques},
author = {Sudhanshu Sane and Roxana Bujack and Christoph Garth and Hank Childs},
url = {http://www.google.com/url?q=http%3A%2F%2Fwww.informatik.uni-leipzig.de%2F~bujack%2F2020Sane.pdf&sa=D&sntz=1&usg=AOvVaw1ezsZzR1EQcQw-61qWNOD-},
doi = {https://doi.org/10.1111/cgf.14036},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Computer Graphics Forum},
volume = {39},
number = {3},
pages = {785-809},
abstract = {Streamlines are an extensively utilized flow visualization technique for understanding, verifying, and exploring computational fluid dynamics simulations. One of the major challenges associated with the technique is selecting which streamlines to display. Using a large number of streamlines results in dense, cluttered visualizations, often containing redundant information and occluding important regions, whereas using a small number of streamlines could result in missing key features of the flow. Many solutions to select a representative set of streamlines have been proposed by researchers over the past two decades. In this state-of-the-art report, we analyze and classify seed placement and streamline selection (SPSS) techniques used by the scientific flow visualization community. At a high-level, we classify techniques into automatic and manual techniques, and further divide automatic techniques into three strategies: density-based, feature-based, and similarity-based. Our analysis evaluates the identified strategy groups with respect to focus on regions of interest, minimization of redundancy, and overall computational performance. Finally, we consider the application contexts and tasks for which SPSS techniques are currently applied and have potential applications in the future.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Streamlines are an extensively utilized flow visualization technique for understanding, verifying, and exploring computational fluid dynamics simulations. One of the major challenges associated with the technique is selecting which streamlines to display. Using a large number of streamlines results in dense, cluttered visualizations, often containing redundant information and occluding important regions, whereas using a small number of streamlines could result in missing key features of the flow. Many solutions to select a representative set of streamlines have been proposed by researchers over the past two decades. In this state-of-the-art report, we analyze and classify seed placement and streamline selection (SPSS) techniques used by the scientific flow visualization community. At a high-level, we classify techniques into automatic and manual techniques, and further divide automatic techniques into three strategies: density-based, feature-based, and similarity-based. Our analysis evaluates the identified strategy groups with respect to focus on regions of interest, minimization of redundancy, and overall computational performance. Finally, we consider the application contexts and tasks for which SPSS techniques are currently applied and have potential applications in the future.