This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Similarity-Guided Streamline Placement with Error Evaluation
November/December 2007 (vol. 13 no. 6)
pp. 1448-1455
ASCII Text
x 
 
Yuan Chen, Jonathan Cohen, Julian Krolik, "Similarity-Guided Streamline Placement with Error Evaluation," IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1448-1455, November/December, 2007.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2007.70595,
author = {Yuan Chen and Jonathan Cohen and Julian Krolik},
title = {Similarity-Guided Streamline Placement with Error Evaluation},
journal ={IEEE Transactions on Visualization and Computer Graphics},
volume = {13},
number = {6},
issn = {1077-2626},
year = {2007},
pages = {1448-1455},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2007.70595},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Similarity-Guided Streamline Placement with Error Evaluation
IS - 6
SN - 1077-2626
SP1448
EP1455
EPD - 1448-1455
A1 - Yuan Chen,
A1 - Jonathan Cohen,
A1 - Julian Krolik,
PY - 2007
KW - Adaptive streamlines
KW - vector field reconstruction
KW - shape matching
VL - 13
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
Most streamline generation algorithms either provide a particular density of streamlines across the domain or explicitly detect features, such as critical points, and follow customized rules to emphasize those features. However, the former generally includes many redundant streamlines, and the latter requires Boolean decisions on which points are features (and may thus suffer from robustness problems for real-world data). We take a new approach to adaptive streamline placement for steady vector fields in 2D and 3D. We define a metric for local similarity among streamlines and use this metric to grow streamlines from a dense set of candidate seed points. The metric considers not only Euclidean distance, but also a simple statistical measure of shape and directional similarity. Without explicit feature detection, our method produces streamlines that naturally accentuate regions of geometric interest. In conjunction with this method, we also propose a quantitative error metric for evaluating a streamline representation based on how well it preserves the information from the original vector field. This error metric reconstructs a vector field from points on the streamline representation and computes a difference of the reconstruction from the original vector field.

[1] C. B. Barber, D. P. Dobkin, and H. T. Huhdanpaa, The Quickhull algorithm for convex hulls. ACM Transactions on Mathematical Software, 22 (4): 469–483, Dec. 1996.
[2] M. Bertram, X. Tricoche, and H. Hagen, Adaptive smooth scattered data approximation for large-scale terrain visualization. In Proceedings of the Symposium on Data Visualisation 2003, pages 177–184, 2003.
[3] J. Haber, F. Zeilfelder, O. Davydov, and H. P. Seidel, Smooth approximation and rendering of large scattered data sets. In Proceedings IEEE Visualization 2001, pages 341–348, 2001.
[4] H. Hauser, R. S. Laramee, and H. Doleisch, State-of-the-Art Report 2002 in Flow Visualization. Technical report, VRVis Research Center, www.VRVis.at, Feb. 2002. TR-VRVis-2002-003.
[5] B. Jobard and W. Lefer, Creating evenly spaced streamlines of arbitrary density. In Visualization in Scentific Computing '97, 1997.
[6] B. Jobard and W. Lefer, Multiresolution flow visualization. In WSCG 2001 Conference Proceedings, 2001.
[7] R. S. Laramee, H. Hauser, H. Doleisch, F. H. Post, B. Vrolijk, and D. Weiskopf, The State of the Art in Flow Visualization: Dense and Texture-Based Techniques. Computer Graphics Forum, 23 (2): 203–221, June 2004.
[8] L. Li and H.-W. Shen, Image-based streamline generation and rendering. IEEE Transactions on Visualization and Computer Graphics, 13 (3), 2007.
[9] O. Mattausch, T. Theubl, H. Hauser, and M. E. Groller, Strategies for interactive exploration of 3d flow using evenly-spaced illuminated streamlines. In Proc. of the 19th Spring Conference on Computer Graphics, pages 213–222, 2003.
[10] A. Mebarki, P. Alliez, and O. Devillers, Farthest point seeding for efficient placement of streamlines. In Visualization '05, pages 479–486, Oct. 2005.
[11] G. M. Nielson, Radial hermite operators for scattered point cloud data with normal vectors and applications to implicitizing polygon mesh surfaces for generalized CSG operations and smoothing. In IEEE Visualization, pages 203–210, 2004.
[12] M. Schlemmer, I. Hotz, B. Hamann, F. Morr, and H. Hagen, Priority streamlines: A context-based visualization of flow fields. In Eurographics/IEEE-VGTC Symposium on Visualization, 2007.
[13] G. Turk and D. Banks, Image-guided streamline placement. In Proc. 23rd Ann. Conf. Computer Graphics and Interactive Techniques(SIGGRAPH '96, pages 453–460, 1996.
[14] V. Verma, D. Kao, and A. Pang, A flow-guided streamline seeding strategy. In Visualization '00, pages 163–170, 2000.
[15] X. Ye, D. Kao, and A. Pang, Strategy for seeding 3d streamlines. In Visualization '05, pages 471–478, Oct. 2005.
[16] R. J. M. I. Zhanping Liu and J. Groner, An advanced evenly-spaced streamline placement algorithm. IEEE Transactions on Visualization and Computer Graphics, 12 (5): 965–973, Sept. 2006.

Index Terms:
Adaptive streamlines, vector field reconstruction, shape matching
Citation:
Yuan Chen, Jonathan Cohen, Julian Krolik, "Similarity-Guided Streamline Placement with Error Evaluation," IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1448-1455, Nov.-Dec. 2007, doi:10.1109/TVCG.2007.70595
Usage of this product signifies your acceptance of the Terms of Use.