Abstract:
Streamline-based techniques play an important role in visualizing and analyzing uncertain steady vector fields. It is a challenging problem to generate accurate streamlin...Show MoreMetadata
Abstract:
Streamline-based techniques play an important role in visualizing and analyzing uncertain steady vector fields. It is a challenging problem to generate accurate streamlines in uncertain vector fields due to the global uncertainty transportation. In this work, we present a novel probabilistic method for streamline computation on uncertain steady vector fields using a Bayesian framework. In our framework, a streamline is modeled as a state space model which captures the spatial coherence of integration steps and uncertainty in local distributions using the conditional prior density and the likelihood function. To approximate the posterior distribution for all the possible traces originating from a given seed position, a set of weighted samples are iteratively updated from which streamlines with higher likelihood can be derived. We qualitatively and quantitatively compare our method with alternative methods on different types of flow field data sets. Our method can generate possible streamlines with higher certainty and hence more accurate flow traces.
Published in: 2016 IEEE Pacific Visualization Symposium (PacificVis)
Date of Conference: 19-22 April 2016
Date Added to IEEE Xplore: 05 May 2016
Electronic ISBN:978-1-5090-1451-4
Electronic ISSN: 2165-8773
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Bayesian Model ,
- Streamline ,
- Posterior Probability ,
- Flow Field ,
- Bayesian Framework ,
- Vector Field ,
- Types Of Datasets ,
- State-space Model ,
- Integration Step ,
- Conditional Density ,
- Prior Density ,
- Steady Field ,
- Monte Carlo Simulation ,
- Step Size ,
- Probability Density ,
- Test Dataset ,
- Probability Density Function ,
- Unit Vector ,
- Mean Distance ,
- Block Size ,
- Uncertain Data ,
- Tracer Particles ,
- Particle Filter ,
- Maximum A Posteriori ,
- Von Mises ,
- Position Uncertainty ,
- Target Distribution ,
- Concentration Parameters
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Bayesian Model ,
- Streamline ,
- Posterior Probability ,
- Flow Field ,
- Bayesian Framework ,
- Vector Field ,
- Types Of Datasets ,
- State-space Model ,
- Integration Step ,
- Conditional Density ,
- Prior Density ,
- Steady Field ,
- Monte Carlo Simulation ,
- Step Size ,
- Probability Density ,
- Test Dataset ,
- Probability Density Function ,
- Unit Vector ,
- Mean Distance ,
- Block Size ,
- Uncertain Data ,
- Tracer Particles ,
- Particle Filter ,
- Maximum A Posteriori ,
- Von Mises ,
- Position Uncertainty ,
- Target Distribution ,
- Concentration Parameters