2018
Gospodnetic, Petra; Banesh, Divya; Wolfram, Phillip; Petersen, Mark; Hagen, Hans; Ahrens, James; Rauhut, Markus
Ocean Current Segmentation at Different Depths and Correlation with Temperature in a MPAS-Ocean Simulation Proceedings Article
In: 2018 IEEE Scientific Visualization Conference (SciVis), pp. 62-66, 2018.
Abstract | Links | BibTeX | Tags: image processing, ocean current segmentation, ocean current visualization, ocean currents
@inproceedings{8823794,
title = {Ocean Current Segmentation at Different Depths and Correlation with Temperature in a MPAS-Ocean Simulation},
author = {Petra Gospodnetic and Divya Banesh and Phillip Wolfram and Mark Petersen and Hans Hagen and James Ahrens and Markus Rauhut},
url = {https://ieeexplore.ieee.org/abstract/document/8823794
https://dsscale.org/wp-content/uploads/2019/10/08823794_optimized.pdf},
doi = {10.1109/SciVis.2018.8823794},
year = {2018},
date = {2018-10-01},
booktitle = {2018 IEEE Scientific Visualization Conference (SciVis)},
pages = {62-66},
abstract = {When analyzing and interpreting results of an ocean simulation, the prevalent method in oceanography is to visualize the complete dataset. However, this can lead to data being missed or misinterpreted due to the distraction caused by the extraneous data of the simulation. Furthermore, when the data stretches over many layers in depth or over numerous time-steps, the ability to track attributes such as ocean currents becomes difficult due to the complexity of the data. We propose an image processing approach to simulation preprocessing for visualization purposes, which offers automation of ocean current tracking within a simulation and ocean current segmentation from the rest of the simulation data. Using the proposed approach, it is possible to automatically identify the most scientifically-relevant streams, extract them from the rest of the simulation and correlate their behavior with other simulation parameters.},
keywords = {image processing, ocean current segmentation, ocean current visualization, ocean currents},
pubstate = {published},
tppubtype = {inproceedings}
}
When analyzing and interpreting results of an ocean simulation, the prevalent method in oceanography is to visualize the complete dataset. However, this can lead to data being missed or misinterpreted due to the distraction caused by the extraneous data of the simulation. Furthermore, when the data stretches over many layers in depth or over numerous time-steps, the ability to track attributes such as ocean currents becomes difficult due to the complexity of the data. We propose an image processing approach to simulation preprocessing for visualization purposes, which offers automation of ocean current tracking within a simulation and ocean current segmentation from the rest of the simulation data. Using the proposed approach, it is possible to automatically identify the most scientifically-relevant streams, extract them from the rest of the simulation and correlate their behavior with other simulation parameters.
Banesh, Divya; Wendelberger, Joanne; Petersen, Mark; Ahrens, James; Hamann, Bernd
Change Point Detection for Ocean Eddy Analysis Proceedings Article
In: Proceedings of the Workshop on Visualisation in Environmental Sciences, pp. 27–33, Eurographics Association, Brno, Czech Republic, 2018, ISBN: 978-3-03868-063-5.
Abstract | Links | BibTeX | Tags: exploratory data analysis, image processing, object detection, regression analysis, time series analysis
@inproceedings{Banesh:2018:CPD:3310180.3310186,
title = {Change Point Detection for Ocean Eddy Analysis},
author = {Divya Banesh and Joanne Wendelberger and Mark Petersen and James Ahrens and Bernd Hamann},
url = {http://dl.acm.org/citation.cfm?id=3310180.3310186
https://dsscale.org/wp-content/uploads/2019/10/dbanesh_ChangeDetection_optimized.pdf},
isbn = {978-3-03868-063-5},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the Workshop on Visualisation in Environmental Sciences},
pages = {27--33},
publisher = {Eurographics Association},
address = {Brno, Czech Republic},
series = {EnvirVis '18},
abstract = {The detection and analysis of mesoscale ocean eddies is a complex task, made more difficult when simulated or observational ocean data are massive. We present the statistical approach of change point detection as a means to help scientists efficiently extract relevant scientific information. We demonstrate the value of change point detection for the characterization of eddy behavior in simulated ocean data. Our results show that change point detection helps with the identification of significant parameter values used in an algorithm or determination of time points that correspond to eddy activity of interest.},
keywords = {exploratory data analysis, image processing, object detection, regression analysis, time series analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The detection and analysis of mesoscale ocean eddies is a complex task, made more difficult when simulated or observational ocean data are massive. We present the statistical approach of change point detection as a means to help scientists efficiently extract relevant scientific information. We demonstrate the value of change point detection for the characterization of eddy behavior in simulated ocean data. Our results show that change point detection helps with the identification of significant parameter values used in an algorithm or determination of time points that correspond to eddy activity of interest.
: . .
1.
Gospodnetic, Petra; Banesh, Divya; Wolfram, Phillip; Petersen, Mark; Hagen, Hans; Ahrens, James; Rauhut, Markus
Ocean Current Segmentation at Different Depths and Correlation with Temperature in a MPAS-Ocean Simulation Proceedings Article
In: 2018 IEEE Scientific Visualization Conference (SciVis), pp. 62-66, 2018.
@inproceedings{8823794,
title = {Ocean Current Segmentation at Different Depths and Correlation with Temperature in a MPAS-Ocean Simulation},
author = {Petra Gospodnetic and Divya Banesh and Phillip Wolfram and Mark Petersen and Hans Hagen and James Ahrens and Markus Rauhut},
url = {https://ieeexplore.ieee.org/abstract/document/8823794
https://dsscale.org/wp-content/uploads/2019/10/08823794_optimized.pdf},
doi = {10.1109/SciVis.2018.8823794},
year = {2018},
date = {2018-10-01},
booktitle = {2018 IEEE Scientific Visualization Conference (SciVis)},
pages = {62-66},
abstract = {When analyzing and interpreting results of an ocean simulation, the prevalent method in oceanography is to visualize the complete dataset. However, this can lead to data being missed or misinterpreted due to the distraction caused by the extraneous data of the simulation. Furthermore, when the data stretches over many layers in depth or over numerous time-steps, the ability to track attributes such as ocean currents becomes difficult due to the complexity of the data. We propose an image processing approach to simulation preprocessing for visualization purposes, which offers automation of ocean current tracking within a simulation and ocean current segmentation from the rest of the simulation data. Using the proposed approach, it is possible to automatically identify the most scientifically-relevant streams, extract them from the rest of the simulation and correlate their behavior with other simulation parameters.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
When analyzing and interpreting results of an ocean simulation, the prevalent method in oceanography is to visualize the complete dataset. However, this can lead to data being missed or misinterpreted due to the distraction caused by the extraneous data of the simulation. Furthermore, when the data stretches over many layers in depth or over numerous time-steps, the ability to track attributes such as ocean currents becomes difficult due to the complexity of the data. We propose an image processing approach to simulation preprocessing for visualization purposes, which offers automation of ocean current tracking within a simulation and ocean current segmentation from the rest of the simulation data. Using the proposed approach, it is possible to automatically identify the most scientifically-relevant streams, extract them from the rest of the simulation and correlate their behavior with other simulation parameters.
2.
Banesh, Divya; Wendelberger, Joanne; Petersen, Mark; Ahrens, James; Hamann, Bernd
Change Point Detection for Ocean Eddy Analysis Proceedings Article
In: Proceedings of the Workshop on Visualisation in Environmental Sciences, pp. 27–33, Eurographics Association, Brno, Czech Republic, 2018, ISBN: 978-3-03868-063-5.
@inproceedings{Banesh:2018:CPD:3310180.3310186,
title = {Change Point Detection for Ocean Eddy Analysis},
author = {Divya Banesh and Joanne Wendelberger and Mark Petersen and James Ahrens and Bernd Hamann},
url = {http://dl.acm.org/citation.cfm?id=3310180.3310186
https://dsscale.org/wp-content/uploads/2019/10/dbanesh_ChangeDetection_optimized.pdf},
isbn = {978-3-03868-063-5},
year = {2018},
date = {2018-01-01},
booktitle = {Proceedings of the Workshop on Visualisation in Environmental Sciences},
pages = {27--33},
publisher = {Eurographics Association},
address = {Brno, Czech Republic},
series = {EnvirVis '18},
abstract = {The detection and analysis of mesoscale ocean eddies is a complex task, made more difficult when simulated or observational ocean data are massive. We present the statistical approach of change point detection as a means to help scientists efficiently extract relevant scientific information. We demonstrate the value of change point detection for the characterization of eddy behavior in simulated ocean data. Our results show that change point detection helps with the identification of significant parameter values used in an algorithm or determination of time points that correspond to eddy activity of interest.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The detection and analysis of mesoscale ocean eddies is a complex task, made more difficult when simulated or observational ocean data are massive. We present the statistical approach of change point detection as a means to help scientists efficiently extract relevant scientific information. We demonstrate the value of change point detection for the characterization of eddy behavior in simulated ocean data. Our results show that change point detection helps with the identification of significant parameter values used in an algorithm or determination of time points that correspond to eddy activity of interest.