Abstract
The ability to extract and follow time-varying flow features in volume data generated from large-scale numerical simulations enables scientists to effectively see and validate modeled phenomena and processes. Extracted features often take much less storage space and computing resources to visualize. Most feature extraction and tracking methods first identify features of interest in each time step independently, then correspond these features in consecutive time steps of the data. Since these methods handle each time step separately, they do not use the coherency of the feature along the time dimension in the extraction process. In this paper, we present a prediction-correction method that uses a prediction step to make the best guess of the feature region in the subsequent time step, followed by growing and shrinking the border of the predicted region to coherently extract the actual feature of interest. This method makes use of the temporal-space coherency of the data to accelerate the extraction process while implicitly solving the tedious correspondence problem that previous methods focus on. Our method is low cost with very little storage overhead, and thus facilitates interactive or runtime extraction and visualization, unlike previous methods which were largely suited for batch-mode processing due to high computational cost.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE Pacific Visualization Symposium, PacificVis 2009 - Proceedings |
| Pages | 17-24 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Jul 21 2009 |
| Event | IEEE Pacific Visualization Symposium, PacificVis 2009 - Beijing, China Duration: Apr 20 2009 → Apr 23 2009 |
Other
| Other | IEEE Pacific Visualization Symposium, PacificVis 2009 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 4/20/09 → 4/23/09 |
Fingerprint
Keywords
- Feature representation
- I.4.6 [computer graphics]: Segmentation
- Region growing, partitioning; I.4.7 [computer graphics]: Feature measurement
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Software
Cite this
Interactive feature extraction and tracking by utilizing region coherency. / Muelder, Chris; Ma, Kwan-Liu.
IEEE Pacific Visualization Symposium, PacificVis 2009 - Proceedings. 2009. p. 17-24 4906833.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Interactive feature extraction and tracking by utilizing region coherency
AU - Muelder, Chris
AU - Ma, Kwan-Liu
PY - 2009/7/21
Y1 - 2009/7/21
N2 - The ability to extract and follow time-varying flow features in volume data generated from large-scale numerical simulations enables scientists to effectively see and validate modeled phenomena and processes. Extracted features often take much less storage space and computing resources to visualize. Most feature extraction and tracking methods first identify features of interest in each time step independently, then correspond these features in consecutive time steps of the data. Since these methods handle each time step separately, they do not use the coherency of the feature along the time dimension in the extraction process. In this paper, we present a prediction-correction method that uses a prediction step to make the best guess of the feature region in the subsequent time step, followed by growing and shrinking the border of the predicted region to coherently extract the actual feature of interest. This method makes use of the temporal-space coherency of the data to accelerate the extraction process while implicitly solving the tedious correspondence problem that previous methods focus on. Our method is low cost with very little storage overhead, and thus facilitates interactive or runtime extraction and visualization, unlike previous methods which were largely suited for batch-mode processing due to high computational cost.
AB - The ability to extract and follow time-varying flow features in volume data generated from large-scale numerical simulations enables scientists to effectively see and validate modeled phenomena and processes. Extracted features often take much less storage space and computing resources to visualize. Most feature extraction and tracking methods first identify features of interest in each time step independently, then correspond these features in consecutive time steps of the data. Since these methods handle each time step separately, they do not use the coherency of the feature along the time dimension in the extraction process. In this paper, we present a prediction-correction method that uses a prediction step to make the best guess of the feature region in the subsequent time step, followed by growing and shrinking the border of the predicted region to coherently extract the actual feature of interest. This method makes use of the temporal-space coherency of the data to accelerate the extraction process while implicitly solving the tedious correspondence problem that previous methods focus on. Our method is low cost with very little storage overhead, and thus facilitates interactive or runtime extraction and visualization, unlike previous methods which were largely suited for batch-mode processing due to high computational cost.
KW - Feature representation
KW - I.4.6 [computer graphics]: Segmentation
KW - Region growing, partitioning; I.4.7 [computer graphics]: Feature measurement
UR - http://www.scopus.com/inward/record.url?scp=67650556952&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67650556952&partnerID=8YFLogxK
U2 - 10.1109/PACIFICVIS.2009.4906833
DO - 10.1109/PACIFICVIS.2009.4906833
M3 - Conference contribution
AN - SCOPUS:67650556952
SN - 9781424444045
SP - 17
EP - 24
BT - IEEE Pacific Visualization Symposium, PacificVis 2009 - Proceedings
ER -