2018
Ware, Colin; Turton, Terece; Bujack, Roxana; Samsel, Francesca; Shrivastava, Piyush; Rogers, David
Measuring and Modeling the Feature Detection Threshold Functions of Colormaps Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, pp. 1-1, 2018, ISSN: 1077-2626, (LA-UR-18-21476).
Abstract | Links | BibTeX | Tags: color perception, Colormapping, feature extraction, frequency measurements, Image color analysis, sea measurements, sensitivity, spatial resolution, Task analysis
@article{8413174,
title = {Measuring and Modeling the Feature Detection Threshold Functions of Colormaps},
author = {Colin Ware and Terece Turton and Roxana Bujack and Francesca Samsel and Piyush Shrivastava and David Rogers},
url = {https://ieeexplore.ieee.org/document/8413174},
doi = {10.1109/TVCG.2018.2855742},
issn = {1077-2626},
year = {2018},
date = {2018-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
pages = {1-1},
abstract = {Pseudocoloring is one of the most common techniques used in scientific visualization. To apply pseudocoloring to a scalar field, the field value at each point is represented using one of a sequence of colors (called a colormap). One of the principles applied in generating colormaps is uniformity and previously the main method for determining uniformity has been the application of uniform color spaces. Here we present a new method for evaluating the feature discrimination threshold function across a colormap. The method is used in crowdsourced studies for the direct evaluation of nine colormaps for three feature sizes. The results are used to test the hypothesis that a uniform color space (CIELAB) gives too much weight to chromatic differences compared to luminance differences because of the way it was constructed. The hypothesis that feature discrimination can be predicted solely on the basis of luminance is also tested. The results reject both hypotheses and we demonstrate how reduced weights on the green-red and blue-yellow terms of the CIELAB color space creates a more accurate model when the task is the detection of smaller features in colormapped data. Both the method itself and modified CIELAB can be used in colormap design and evaluation.},
note = {LA-UR-18-21476},
keywords = {color perception, Colormapping, feature extraction, frequency measurements, Image color analysis, sea measurements, sensitivity, spatial resolution, Task analysis},
pubstate = {published},
tppubtype = {article}
}
Pseudocoloring is one of the most common techniques used in scientific visualization. To apply pseudocoloring to a scalar field, the field value at each point is represented using one of a sequence of colors (called a colormap). One of the principles applied in generating colormaps is uniformity and previously the main method for determining uniformity has been the application of uniform color spaces. Here we present a new method for evaluating the feature discrimination threshold function across a colormap. The method is used in crowdsourced studies for the direct evaluation of nine colormaps for three feature sizes. The results are used to test the hypothesis that a uniform color space (CIELAB) gives too much weight to chromatic differences compared to luminance differences because of the way it was constructed. The hypothesis that feature discrimination can be predicted solely on the basis of luminance is also tested. The results reject both hypotheses and we demonstrate how reduced weights on the green-red and blue-yellow terms of the CIELAB color space creates a more accurate model when the task is the detection of smaller features in colormapped data. Both the method itself and modified CIELAB can be used in colormap design and evaluation.
2015
Woodring, Jonathan; Petersen, Mark; Schmeiber, Andre; Patchett, John; Ahrens, James; Hagen, Hans
In Situ Eddy Analysis in a High-Resolution Ocean Climate Model Proceedings Article
In: IEEE, 2015, (LA-UR-pending).
Abstract | Links | BibTeX | Tags: climate modeling, collaborative development, feature analysis, feature extraction, high performance computing, In situ analysis, mesoscale eddies, ocean modeling, online analysis, revision control, simulation, software engineering, supercomputing
@inproceedings{Woodring2015,
title = {In Situ Eddy Analysis in a High-Resolution Ocean Climate Model},
author = {Jonathan Woodring and Mark Petersen and Andre Schmeiber and John Patchett and James Ahrens and Hans Hagen},
url = {http://ieeexplore.ieee.org/document/7192723/},
year = {2015},
date = {2015-01-01},
publisher = {IEEE},
abstract = {An eddy is a feature associated with a rotating body of fluid, surrounded by a ring of shearing fluid. In the ocean, eddies are 10 to 150 km in diameter, are spawned by boundary currents and baroclinic instabilities, may live for hundreds of days, and travel for hundreds of kilometers. Eddies are important in climate studies because they transport heat, salt, and nutrients through the world’s oceans and are vessels of biological productivity. The study of eddies in global ocean-climate models requires large-scale, high-resolution simulations. This poses a problem for feasible (timely) eddy analysis, as ocean simulations generate massive amounts of data, causing a bottleneck for traditional analysis workflows. To enable eddy studies, we have developed an in situ workflow for the quantitative and qualitative analysis of MPAS-Ocean, a high-resolution ocean climate model, in collaboration with the ocean model research and development process. Planned eddy analysis at high spatial and temporal resolutions will not be possible with a post- processing workflow due to various constraints, such as storage size and I/O time, but the in situ workflow enables it and scales well to ten-thousand processing elements.},
note = {LA-UR-pending},
keywords = {climate modeling, collaborative development, feature analysis, feature extraction, high performance computing, In situ analysis, mesoscale eddies, ocean modeling, online analysis, revision control, simulation, software engineering, supercomputing},
pubstate = {published},
tppubtype = {inproceedings}
}
An eddy is a feature associated with a rotating body of fluid, surrounded by a ring of shearing fluid. In the ocean, eddies are 10 to 150 km in diameter, are spawned by boundary currents and baroclinic instabilities, may live for hundreds of days, and travel for hundreds of kilometers. Eddies are important in climate studies because they transport heat, salt, and nutrients through the world’s oceans and are vessels of biological productivity. The study of eddies in global ocean-climate models requires large-scale, high-resolution simulations. This poses a problem for feasible (timely) eddy analysis, as ocean simulations generate massive amounts of data, causing a bottleneck for traditional analysis workflows. To enable eddy studies, we have developed an in situ workflow for the quantitative and qualitative analysis of MPAS-Ocean, a high-resolution ocean climate model, in collaboration with the ocean model research and development process. Planned eddy analysis at high spatial and temporal resolutions will not be possible with a post- processing workflow due to various constraints, such as storage size and I/O time, but the in situ workflow enables it and scales well to ten-thousand processing elements.
2011
Williams, Sean; Petersen, Mark; Bremer, Peer-Timo; Hecht, Matthew; Pascucci, Valerio; Ahrens, James; Hlawitschka, Mario; Hamann, Bernd
Adaptive extraction and quantification of geophysical vortices Journal Article
In: Visualization and Computer Graphics, IEEE Transactions on, vol. 17, no. 12, pp. 2088–2095, 2011, (LA-UR-11-04444).
Abstract | Links | BibTeX | Tags: adaptive extraction, feature extraction, Geophysical Vortices, Quantification, statistical data analysis, Vortex extraction
@article{williams2011adaptive,
title = {Adaptive extraction and quantification of geophysical vortices},
author = {Sean Williams and Mark Petersen and Peer-Timo Bremer and Matthew Hecht and Valerio Pascucci and James Ahrens and Mario Hlawitschka and Bernd Hamann},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AdaptiveExtractionAndQuantificaitonOfGeophysicalVortices.pdf},
year = {2011},
date = {2011-01-01},
journal = {Visualization and Computer Graphics, IEEE Transactions on},
volume = {17},
number = {12},
pages = {2088--2095},
publisher = {IEEE},
abstract = {We consider the problem of extracting discrete two-dimensional vortices from a turbulent flow. In our approach we use a reference model describing the expected physics and geome try of an idealized vortex. The model allows us to derive a novel correlation between the size of the vortex and its strength, measured as the square of its strain minus the square of its vorticity. For vortex detection in real models we use the strength parameter to locate potential vortex cores, then measure the similarity of our ideal analytical vortex and the real vortex core for different strength thresholds. This approach provides a metric for how well a vortex core is modeled by an ideal vortex. Moreover, this provides insight into the problem of choosing the thresholds that identify a vortex. By selecting a target coefficient of determination (i.e., statistical confidence), we determine on a per-vortex basis what threshold of the strength parameter would be required to extract that vortex at the chosen confidence. We validate our approach on real dat a from a global ocean simulation and derive from it a map of expected vortex strengths over the global ocean.},
note = {LA-UR-11-04444},
keywords = {adaptive extraction, feature extraction, Geophysical Vortices, Quantification, statistical data analysis, Vortex extraction},
pubstate = {published},
tppubtype = {article}
}
We consider the problem of extracting discrete two-dimensional vortices from a turbulent flow. In our approach we use a reference model describing the expected physics and geome try of an idealized vortex. The model allows us to derive a novel correlation between the size of the vortex and its strength, measured as the square of its strain minus the square of its vorticity. For vortex detection in real models we use the strength parameter to locate potential vortex cores, then measure the similarity of our ideal analytical vortex and the real vortex core for different strength thresholds. This approach provides a metric for how well a vortex core is modeled by an ideal vortex. Moreover, this provides insight into the problem of choosing the thresholds that identify a vortex. By selecting a target coefficient of determination (i.e., statistical confidence), we determine on a per-vortex basis what threshold of the strength parameter would be required to extract that vortex at the chosen confidence. We validate our approach on real dat a from a global ocean simulation and derive from it a map of expected vortex strengths over the global ocean.
: . .
1.
Ware, Colin; Turton, Terece; Bujack, Roxana; Samsel, Francesca; Shrivastava, Piyush; Rogers, David
Measuring and Modeling the Feature Detection Threshold Functions of Colormaps Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, pp. 1-1, 2018, ISSN: 1077-2626, (LA-UR-18-21476).
@article{8413174,
title = {Measuring and Modeling the Feature Detection Threshold Functions of Colormaps},
author = {Colin Ware and Terece Turton and Roxana Bujack and Francesca Samsel and Piyush Shrivastava and David Rogers},
url = {https://ieeexplore.ieee.org/document/8413174},
doi = {10.1109/TVCG.2018.2855742},
issn = {1077-2626},
year = {2018},
date = {2018-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
pages = {1-1},
abstract = {Pseudocoloring is one of the most common techniques used in scientific visualization. To apply pseudocoloring to a scalar field, the field value at each point is represented using one of a sequence of colors (called a colormap). One of the principles applied in generating colormaps is uniformity and previously the main method for determining uniformity has been the application of uniform color spaces. Here we present a new method for evaluating the feature discrimination threshold function across a colormap. The method is used in crowdsourced studies for the direct evaluation of nine colormaps for three feature sizes. The results are used to test the hypothesis that a uniform color space (CIELAB) gives too much weight to chromatic differences compared to luminance differences because of the way it was constructed. The hypothesis that feature discrimination can be predicted solely on the basis of luminance is also tested. The results reject both hypotheses and we demonstrate how reduced weights on the green-red and blue-yellow terms of the CIELAB color space creates a more accurate model when the task is the detection of smaller features in colormapped data. Both the method itself and modified CIELAB can be used in colormap design and evaluation.},
note = {LA-UR-18-21476},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Pseudocoloring is one of the most common techniques used in scientific visualization. To apply pseudocoloring to a scalar field, the field value at each point is represented using one of a sequence of colors (called a colormap). One of the principles applied in generating colormaps is uniformity and previously the main method for determining uniformity has been the application of uniform color spaces. Here we present a new method for evaluating the feature discrimination threshold function across a colormap. The method is used in crowdsourced studies for the direct evaluation of nine colormaps for three feature sizes. The results are used to test the hypothesis that a uniform color space (CIELAB) gives too much weight to chromatic differences compared to luminance differences because of the way it was constructed. The hypothesis that feature discrimination can be predicted solely on the basis of luminance is also tested. The results reject both hypotheses and we demonstrate how reduced weights on the green-red and blue-yellow terms of the CIELAB color space creates a more accurate model when the task is the detection of smaller features in colormapped data. Both the method itself and modified CIELAB can be used in colormap design and evaluation.
2.
Woodring, Jonathan; Petersen, Mark; Schmeiber, Andre; Patchett, John; Ahrens, James; Hagen, Hans
In Situ Eddy Analysis in a High-Resolution Ocean Climate Model Proceedings Article
In: IEEE, 2015, (LA-UR-pending).
@inproceedings{Woodring2015,
title = {In Situ Eddy Analysis in a High-Resolution Ocean Climate Model},
author = {Jonathan Woodring and Mark Petersen and Andre Schmeiber and John Patchett and James Ahrens and Hans Hagen},
url = {http://ieeexplore.ieee.org/document/7192723/},
year = {2015},
date = {2015-01-01},
publisher = {IEEE},
abstract = {An eddy is a feature associated with a rotating body of fluid, surrounded by a ring of shearing fluid. In the ocean, eddies are 10 to 150 km in diameter, are spawned by boundary currents and baroclinic instabilities, may live for hundreds of days, and travel for hundreds of kilometers. Eddies are important in climate studies because they transport heat, salt, and nutrients through the world’s oceans and are vessels of biological productivity. The study of eddies in global ocean-climate models requires large-scale, high-resolution simulations. This poses a problem for feasible (timely) eddy analysis, as ocean simulations generate massive amounts of data, causing a bottleneck for traditional analysis workflows. To enable eddy studies, we have developed an in situ workflow for the quantitative and qualitative analysis of MPAS-Ocean, a high-resolution ocean climate model, in collaboration with the ocean model research and development process. Planned eddy analysis at high spatial and temporal resolutions will not be possible with a post- processing workflow due to various constraints, such as storage size and I/O time, but the in situ workflow enables it and scales well to ten-thousand processing elements.},
note = {LA-UR-pending},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
An eddy is a feature associated with a rotating body of fluid, surrounded by a ring of shearing fluid. In the ocean, eddies are 10 to 150 km in diameter, are spawned by boundary currents and baroclinic instabilities, may live for hundreds of days, and travel for hundreds of kilometers. Eddies are important in climate studies because they transport heat, salt, and nutrients through the world’s oceans and are vessels of biological productivity. The study of eddies in global ocean-climate models requires large-scale, high-resolution simulations. This poses a problem for feasible (timely) eddy analysis, as ocean simulations generate massive amounts of data, causing a bottleneck for traditional analysis workflows. To enable eddy studies, we have developed an in situ workflow for the quantitative and qualitative analysis of MPAS-Ocean, a high-resolution ocean climate model, in collaboration with the ocean model research and development process. Planned eddy analysis at high spatial and temporal resolutions will not be possible with a post- processing workflow due to various constraints, such as storage size and I/O time, but the in situ workflow enables it and scales well to ten-thousand processing elements.
3.
Williams, Sean; Petersen, Mark; Bremer, Peer-Timo; Hecht, Matthew; Pascucci, Valerio; Ahrens, James; Hlawitschka, Mario; Hamann, Bernd
Adaptive extraction and quantification of geophysical vortices Journal Article
In: Visualization and Computer Graphics, IEEE Transactions on, vol. 17, no. 12, pp. 2088–2095, 2011, (LA-UR-11-04444).
@article{williams2011adaptive,
title = {Adaptive extraction and quantification of geophysical vortices},
author = {Sean Williams and Mark Petersen and Peer-Timo Bremer and Matthew Hecht and Valerio Pascucci and James Ahrens and Mario Hlawitschka and Bernd Hamann},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AdaptiveExtractionAndQuantificaitonOfGeophysicalVortices.pdf},
year = {2011},
date = {2011-01-01},
journal = {Visualization and Computer Graphics, IEEE Transactions on},
volume = {17},
number = {12},
pages = {2088--2095},
publisher = {IEEE},
abstract = {We consider the problem of extracting discrete two-dimensional vortices from a turbulent flow. In our approach we use a reference model describing the expected physics and geome try of an idealized vortex. The model allows us to derive a novel correlation between the size of the vortex and its strength, measured as the square of its strain minus the square of its vorticity. For vortex detection in real models we use the strength parameter to locate potential vortex cores, then measure the similarity of our ideal analytical vortex and the real vortex core for different strength thresholds. This approach provides a metric for how well a vortex core is modeled by an ideal vortex. Moreover, this provides insight into the problem of choosing the thresholds that identify a vortex. By selecting a target coefficient of determination (i.e., statistical confidence), we determine on a per-vortex basis what threshold of the strength parameter would be required to extract that vortex at the chosen confidence. We validate our approach on real dat a from a global ocean simulation and derive from it a map of expected vortex strengths over the global ocean.},
note = {LA-UR-11-04444},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We consider the problem of extracting discrete two-dimensional vortices from a turbulent flow. In our approach we use a reference model describing the expected physics and geome try of an idealized vortex. The model allows us to derive a novel correlation between the size of the vortex and its strength, measured as the square of its strain minus the square of its vorticity. For vortex detection in real models we use the strength parameter to locate potential vortex cores, then measure the similarity of our ideal analytical vortex and the real vortex core for different strength thresholds. This approach provides a metric for how well a vortex core is modeled by an ideal vortex. Moreover, this provides insight into the problem of choosing the thresholds that identify a vortex. By selecting a target coefficient of determination (i.e., statistical confidence), we determine on a per-vortex basis what threshold of the strength parameter would be required to extract that vortex at the chosen confidence. We validate our approach on real dat a from a global ocean simulation and derive from it a map of expected vortex strengths over the global ocean.