The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.06 - November/December (2010 vol.16)
pp: 1560-1568
ABSTRACT
We introduce a flexible technique for interactive exploration of vector field data through classification derived from userspecified feature templates. Our method is founded on the observation that, while similar features within the vector field may bespatially disparate, they share similar neighborhood characteristics. Users generate feature-based visualizations by interactivelyhighlighting well-accepted and domain specific representative feature points. Feature exploration begins with the computation ofattributes that describe the neighborhood of each sample within the input vector field. Compilation of these attributes forms a representation of the vector field samples in the attribute space. We project the attribute points onto the canonical 2D plane to enableinteractive exploration of the vector field using a painting interface. The projection encodes the similarities between vector field pointswithin the distances computed between their associated attribute points. The proposed method is performed at interactive rates forenhanced user experience and is completely flexible as showcased by the simultaneous identification of diverse feature types.
INDEX TERMS
vector field, data clustering, feature classification, high-dimensional data, user interaction
CITATION
Joel Daniels II, Erik W. Anderson, Luis Gustavo Nonato, Cláudio T. Silva, "Interactive Vector Field Feature Identification", IEEE Transactions on Visualization & Computer Graphics, vol.16, no. 6, pp. 1560-1568, November/December 2010, doi:10.1109/TVCG.2010.170
REFERENCES
[1] R. J. Adrian, Particle-imaging techniques for experimental fluid mechanics. Annual Review of Fluid Mechanics, 23: 261–304, 1991.
[2] D. Banks and B. Singer, A predictor-corrector technique for visualizing unsteady flow. IEEE Transactions on Visualization and Computer Graphics, 1 (2): 151–163, 1995.
[3] D. Bauer and R. Peikert, Vortex tracking in scale-space. IEEE TCVG Symposium on Data Visualization, 22: 233–240, 2002.
[4] S. Bryson and C. Levit, The virtual windtunnel: an environment for the exploration of three-dimensional unsteady flows. IEEE Transactions on Visualization and Computer Graphics, pages 17–24, 1991.
[5] A. Buja, J. A. McDonald, J. Michalak, and W. Stuetzle, Interactive data visualization using focusing and linking. IEEE Transactions on Visualization and Computer Graphics, pages 156–163, 1991.
[6] B. Cabral and L. C. Leedom, Imaging vector fields using line integral convolution. ACM SIGGRAPH, pages 263–270, 1993.
[7] R. Cucitore, M. Quadrio, and A. Baron, On the effectiveness and limitations of local criteria for the identification of a vortex. European Journal of Mechanics. B, Fluids, 18 (2): 261–282, 1999.
[8] N. Cuntz, A. Kolb, R. Strzodka, and D. Weiskopf, Particle level set advection for the interactive visualization of unsteady 3d flow. IEEE Euro-Graphics Symposium on Visualization, 27 (3), 2008.
[9] T. Davis and W. Dynamic, supernodes in sparse cholesky up-date/downdate and triangular solves. ACM Transactions Mathematics Software, 35 (4): 1–23, 2009.
[10] A. Defant, Physical Oceanography. Pergamon Press, New York, 1961.
[11] M. H. Everts, H. Bekker, J. B. Roerdink, and T. Isenberg, Depth-dependent halos: Illustrative rendering of dense line data. IEEE Transactions on Visualization and Computer Graphics, 15: 1299–1306, 2009.
[12] N. I. Gould, J. A. Scott, and Y. Hu, A numerical evaluation of sparse direct solvers for the solution of large sparse symmetric linear systems of equations. ACM Transactions on Mathematics Software, 33 (2): 10, 2007.
[13] Z. Han and R. D. Reitz, Turbulence modelling of internal combustion engines using κ - models. Combustion Science and Technology, 106 (4–6): 267–295, 1995.
[14] E. Heiberg, T. Ebbers, L. Wigstrom, and M. Karlsson, Three-dimensional flow characterization using vector pattern matching. IEEE Transactions on Visualization and Computer Graphics, 9 (3): 313–319, July 2003.
[15] J. Helman and L. Hesselink, Representation and display of vector field topology in fluid flows. IEEE Computer, 22 (8): 27–36, 1981.
[16] J. P. Hultquist, Constructing stream surfaces in steady 3d vector fields. IEEE Transactions on Visualization and Computer Graphics, pages 171–178, 1992.
[17] H. Janicke, M. Bottinger, and G. Scheuermann, Brushing of attribute clouds for the visualization of multivariate data. IEEE Transactions on Visualization and Computer Graphics, 14 (6): 1459–1466, 2008.
[18] J. Jeong and F. Hussain, On the identification of a vortex. Fluid Mechanics, 285: 69–94, 1995.
[19] B. Jobard, G. Erlebacher, and Y. Hussaini, Hardware-accelerated texture advection for unsteady flow visualization. IEEE Transactions on Visualization and Computer Graphics, 6: 155–162, 2000.
[20] D. Kenwright and R. Haimes, Vortex identification-applications in aerodynamics: a case study. IEEE Transactions on Visualization and Computer Graphics, 3: 413–422, 1997.
[21] H. Krishnan, C. Garth, and K. Joy, Time and streak surfaces for flow visualization in large time-varying data sets. IEEE Transactions on Visualization and Computer Graphics, 15 (6): 1267–1274, 2009.
[22] G.-S. Li, X. Tricoche, and C. Hansen, Physically-based dye advection for flow visualization. Computer Graphics Forum, 27 (3): 727–734, 2008.
[23] H. Li, W. Chen, and I.-F. Shen, Segmentation of discrete vector fields. IEEE Transactions on Visualization and Computer Graphics, 12 (3): 289–300, 2006.
[24] K. Mahrous, J. Bennett, G. Scheuermann, B. Hamann, and K. I. Joy, Topological segmentation in three-dimensional vector fields. IEEE Transactions on Visualization and Computer Graphics, 10 (2): 198–205, 2004.
[25] N. Max, B. Becker, and R. Crawfis, Flow volumes for interactive vector field visualization. IEEE Transactions on Visualization and Computer Graphics, pages 19–24, 1993.
[26] T. McLoughlin, R. Laramee, R. Peikert, F. Post, and M. Chen, Over two decades of integration-based, geometric flow visualization. Computer Graphics Forum, 2010.
[27] T. McLoughlin, R. S. Laramee, R. Peikert, F. H. Post, and M. Chen, Over two decades of integration-based, geometric flow. EuroGraphics, 2009.
[28] N. Mölders and G. Kramm, Influence of wildfire induced land-cover changes on clouds and precipitation in interior alaska - a case study. Atmospheric Research, 84 (2): 142–168, 2007.
[29] H. Pagendarm, B. Henne, and M. Rutten, Detecting vortical phenomena in vector data by medium-scale correlation. IEEE Transactions on Visualization and Computer Graphics, 5: 409–412, 1999.
[30] F. V. Paulovich, L. G. Nonato, R. Minghim, and H. Levkowitz, Least square projection: A fast high-precision multidimensional projection technique and its application to document mapping. IEEE Transactions on Visualization and Computer Graphics, 14 (3): 564–575, 2008.
[31] K. Polthier and E. Preus, Identifying vector field singularities using a discrete hodge decomposition. Visualization and Mathematics III, pages 113–134, 2003.
[32] F. H. Post, B. Vrolijk, H. Hauser, R. S. Laramee, and H. Doleisch, The state of the art in flow visualization: Feature extraction and tracking. EuroGraphics, 22 (4): 1–17, 2003.
[33] S. Rogers, P. Buning, F. Merritt, and S. Follin, Distributed interactive graphics applications in computational fluid dynamics. International Journal of High Performance Computing Applications, 1 (4): 96–105, 1987.
[34] M. Roth and R. Peikert, A higher-order method for finding vortex core lines. IEEE Transactions on Visualization and Computer Graphics, 4: 143–150, 1998.
[35] I. Sadarjoen and F. Post, Detection, quantification, and tracking of vortices using streamline geometry. Computers and Graphics, 24 (3): 333–341, 2000.
[36] I. Sadarjoen, F. Post, B. Ma, D. Banks, and H. Pagendarm, Selective visualization of vortices in hydrodynamic flows. IEEE Transactions on Visualization and Computer Graphics, 4: 151–158, 1998.
[37] G. Salton and C. Buckley, Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24 (5): 513–523, 1988.
[38] M. Schlemmer, M. Heringer, F. Morr, I. Hotz, M.-H. Bertram, C. Garth, W. Kollmann, B. Hamann, and H. Hagen, Moment invariants for the analysis of 2d flow fields. IEEE Transactions on Visualization and Computer Graphics, 13 (6): 1743–1750, 2007.
[39] H.-W. Shen, C. Johnson, and K.-L. Ma, Visualizing vector fields using line integral convolution and dye advection. Symposium on Volume Visualization, 2: 63–70, 1996.
[40] D. Speray and S. Kennon, Volume probes: interactive data exploration on arbitrary grids. Workshop on Volume visualization, pages 5–12, 1990.
[41] J. Tangelder and R. Veltkamp, A survey of content based 3d shape retrieval methods. Shape Modeling International, pages 145–156, 2004.
[42] H. Theisel and H.-P. Seidel, Feature flow fields. IEEE Symposium on Data Visualisation, pages 141–148, 2003.
[43] G. Turk and D. Banks, Image-guided streamline placement. ACM SIG-GRAPH, pages 453–460, 1996.
[44] V. Verma, D. Kao, and A. Pang, A flow-guided streamline seeding strategy. IEEE Transactions on Visualization and Computer Graphics, 6: 163–170, 2000.
[45] R. Westermann, C. Johnson, and T. Ertl, Topology preserving smoothing of vector fields. IEEE Transactions on Visualization and Computer Graphics, 7: 222–229, 2001.
[46] K. Xu, H. Zhang, D. Cohen-Or, and Y. Xiong, Dynamic harmonic fields for surface processing. Computer Graphics, 33 (3): 391–398, 2009.
13 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool