Computational Field Visualization

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Johnson, C.R., Livnat, Y., Zhukov, L., David Augustus Hart, and Kindlmann, G.: Computational Field Visualization. Mathematics Unlimited - 2001 and Beyond. Engquist, B., Royal Institute of Technology, Stockholm, Sweden, and Schmid, W., Harvard University, Cambridge, MA, USA (Eds.) Book Chapter, Springer-Verlag, ISBN 978-3-540-66913-5



Since computers were first introduced, scientists, mathematicians, and engi- neers have attempted to harness their power to simulate complex physical phe- nomena. Today, the computer is an almost universal tool used in a wide range of scientific and engineering domains. Scientific visualization clearly plays a central role in the analysis of data generated by scientific and engineering sim- ulations.

The field of computational science and engineering has grown out of the widespread use of computers to numerically simulate physical phenomena as- sociated with many problems in science and engineering. In recent years, the scientific computing community has experienced an explosive growth in both the size and the complexity of numeric computations that can be performed in simulations. One of the significant benefits of this increased computing power is the ability to perform complex three-dimensional simulations. How- ever, such simulations present new challenges for computational scientists, in- cluding the need to effectively analyze and visualize complex three-dimensional data.

Computational field problems; such as computational fluid dynamics (CFD), electromagnetic field simulation, and weather modeling – essentially any prob- lems whose physics can be modeled effectively by ordinary and/or partial dif- ferential equations–constitute the majority of computational science and en- gineering simulations. The output of such simulations might be a single field variable (such as pressure or velocity) or a combination of fields involving a number of scalar fields, vector fields, and/or tensor fields. As such, scien- tific visualization researchers have concentrated on effective ways to visualize large-scale computational fields. Much current and previous visualization re- search has focused on methods and techniques for visualizing a computational field variables (such as the extraction of a single scalar field variable as an isosurface). While single variable visualization often satisfies the needs of the user, it is clear that it would also be useful to be able to effectively visualize multiple fields simultaneously. Take Poisson’s equation, for example. It has variants through just about all of science and engineering ...

Extended Abstract


Used References

1. D. Battke, H. andStalling and H.-C. Hege. Fast line integral convolution for ar- bitrary surfaces in 3d. In H.C. Hege and K. Polthier, editors, Visualization and Mathematics, pages 181–195. Springer, Heidelberg, 1997.

2. B. Cabral and C. Leedom. Imaging vector fields using line integral convolution. In Proceedings of SIGGRAPH 93, pages 263–270. ACM SIGGRAPH, 1993.

3. P. Cignoni, C. Montani, E. Puppo, and R. Scopigno. Optimal isosurface extraction from irregular volume data. In Proceedings of IEEE 1996 Symposium on Volume Visualization. ACM Press, 1996.

4. R. Crawfis and N. Max. Direct volume visualization of three-dimensional vector fields. In Proceedings of 1992 Workshop on Volume Visualization, pages 55–60. IEEE Computer Society Press, Los Alamitos, CA, 1992.

5. R. Crawfis and N. Max. Texture splats for 3d scalar and vector field visualization. In Proceedings of Visualization ’93, pages 261–265. IEEE Computer Society Press, Los Alamitos, CA, 1993.

6. Landau L. D. and Lifshitz L. M. Statistical Physics, volume 5 of Course of Theo- retical Physics. Pergamon Press, 1977.

7. T. Delmarcelle and L. Hesselink. Visualizing second-order tensor fields with hyper streamlines. IEEE Computer Graphics and Applications, pages 25–33, 1993.

8. T. Delmarcelle and L. Hesselink. A unified framework for flow visualization. In R. S. Gallagher, editor, Computer visualization: graphics techniques for scientific and engineering analysis, pages 129–170. CRC Press, 1995.

9. K. Engel, R. Westermann, and T. Ertl. Isosurface extraction techniques for web- based volume visualization. In Visualization ’99, pages 139–146. ACM Press, Oc- tober 1999.

10. R. S. Gallagher. Span filter: An optimization scheme for volume visualization of large finite element models. In Proceedings of Visualization ’91, pages 68–75. IEEE Computer Society Press, Los Alamitos, CA, 1991.

11. M. Giles and R. Haimes. Advanced interactive visualization for CFD. Computing Systems in Engineering, 1(1):51–62, 1990.

12. Kindlmann Gordon and Weinstein David. Hue-balls and lit-tensors for direct vol- ume rendering of diffusion tensor field. In IEEE Visualization, 1999.

13. Levy L. Hesselink L. and Lavin Y. The topology of symmetric, second-order 3-d tensor fields. IEEE Transactions, Visualization and Computer Graphics, 1997.

14. T. Itoh and K. Koyyamada. Isosurface generation by using extreme graphs. In Proceedings of Visualization ’94, pages 77–83. IEEE Computer Society Press, Los Alamitos, CA, 1994.

15. T. Itoh, Y. Yamaguchi, and K. Koyyamada. Volume thining for automatic isosurface propagation. In Visualization ’96, pages 303–310. IEEE Computer Society Press, Los Alamitos, CA, 1996.

16. G.D. Kerlick. Moving iconic objects in scientific visualization. In IEEE Visualiza- tion 90 Proceedings, pages 124–130, 1990.

17. Gordon Kindlmann and James Durkin. Semi-automatic generation of transfer func- tions for direct volume rendering,.

18. Korn Theresa M. Korn Granino A. Mathematical Handbook for Scientists and Engineers. Mgraw-Hill, 1961.

19. David H. Laidlaw, Eric T. Ahrens, David Kremers, Matthew J. Avalos, Carol Read- head, and Russell E. Jacobs. Visualizing diffusion tensor images of the mouse spinal cord. In Proceedings Visualization ’98. IEEE Computer Society Press, 1998. 1998.

20. Barthold Lichtenbelt, Randy Crane, and Shaz Naqvi. Introduction to Volume Ren- dering, chapter 1. Prentice Hall PTR, Upper Saddle River, NJ, 1998. 21. Y. Livnat and C. Hansen. View dependent isosurface extraction. In Visualization ’98, pages 175–180. ACM Press, October 1998.

22. Y. Livnat, H.W. Shen, and C.R. Johnson. A near optimal isosurface extraction algorithm using the span space. IEEE Transaction on Visualization and Computer Graphics, 2(1), March 1996.

23. W.E. Lorensen and H. E. Cline. Marching cubes: A high resolution 3D surface construction algorithm. Computer Graphics, 21(4):163–169, July 1987.

24. J. Marks, B. Andalman, P.A. Beardsley, and H. Pfister et al. Design Galleries: A General Approach to Setting Parameters for Computer Graphics and Animation. In ACM Computer Graphics (SIGGRAPH ’97 Proceedings), pages 389–400, August 1997.

25. Sergey V. Matveyev. Approximation of isosurface in the marching cubes: Ambi- guity problem. In Visualization ’94, pages 288–292, October 1994.

26. N. Max, R. Crawfis, and C. Grant. Visualizing 3d velocity fields near contour surfaces. In Proceedings of Visualization ‘94, pages 248–255. IEEE Computer Society Press, Los Alamitos, CA, 1994.

27. C. Montani, R. Scateni, and R. Scopingo. Discretized marching cubes. In Visual- ization ’94, Los Alamitos, CA, pages 281–287, October 1994.

28. J.S. Painter, P. Bunge, and Y. Livnat. Case study: Mantle convection visualization on the cray t3d. In Visualization ’96, pages 409–412. IEEE Computer Society Press, Los Alamitos, CA, 1996.

29. S. Parker, P. Shirley, Y. Livnat, C. Hansen, and P. Sloan. Interactive ray tracing for isosurface rendering. In Visualization ’98, pages 233–238. ACM Press, October 1998.

30. C. Pierpaoli and P.J. Basser. Toward a Quantitative Assessment of Diffusion Anisotropy. Magnetic Resonance Magazine.

31. R. Shekhar, E. Fayyad, R. Yagel, and J. F. Cornhill. Octree-based decimation of marching cubes surfaces. In Visualization ’96, pages 335–342. IEEE Computer Scoiety Press, October 1996.

32. H Shen, C. D. Hansen, Y Livnat, and C. R. Johnson. Isosurfacing in span space with utmost efficiency (issue).

33. H.W. Shen. Isosurface extraction from time-varying fields using a temporal hierar- chical index tree. In IEEE Visualization ‘98, 1998.

34. H.W. Shen and C.R. Johnson. Sweeping simplices: A fast isosurface extraction al- gorithm for unstructure grids. In Proceedings of Visualization ’95. IEEE Computer Society Press, Los Alamitos, CA, 1995.

35. H.W. Shen, C.R. Johnson, and K.L. Ma. Global and local vector field visualization using enhanced line integral convolution. In Symposium on Volume Visualization, pages 63–70. IEEE Press, 1996.

36. H.W. Shen and D.L. Kao. A new line integral convolution algorithm for visualizing unsteady flows. IEEE Transactions on Visualization and Computer Graphics, 4(2), 1998.

37. D. Silver, N. Zabusky, V. Fernandez, and M. Gao. Ellipsoidal quantification of evolving phenomena. Scientific Visualization of Natural Phenomena, pages 573– 588, 1991.

38. Luthringer R. Gresser J. Minot R. Macher J.P. Soufflet L., Toussaint M. A statisti- cal evaluation of the main interpolation methods applied to 3-dimensional eeg map- ping. Elecrtoencephalography and Clinical Neurophysiology, 79:393–402, 1991.

39. D. Stalling and H.-C. Hege. Fast and resolution independent line integral convolu- tion. In Proceedings of SIGGRAPH 95, pages 249–256. ACM SIGGRAPH, 1995.

40. Aziz M. Ulug and Peter C.M. van Zijl. Orientation-independent diffusion imaging without tensor diagonalization: Anisotropy difinitions based on physical attributes of the diffusion ellipsoid. Journal of Magnetic Resonance Imaging, 9:804–813, 1999.

41. J.J. van Wijk. Spot noise: Texture synthesis for data visualization. Computer Graphics, 25(4):309–318, 1991.

42. D. Weinstein, G. Kindlmann, and E. Lundberg. Tensorlines: Advection-diffusion based propagation through diffusion tensor fields. In IEEE Visualization, pages 249–253, 1999.

43. Gubjartsson H. Kikinis R. Westin C-F., Peled S. and Jolesz F.A. Geometrical dif- fusion measures for mri from tensor basis analysis. In Proceedings of ISMRM, 1997.

44. J. Wilhelms and A. Van Gelder. Octrees for faster isosurface generation. Computer Graphics, 24(5):57–62, November 1990.

45. J. Wilhelms and A. Van Gelder. Octrees for faster isosurface generation. ACM Transactions on Graphics, 11(3):201–227, July 1992.

46. Geoff Wyvill, Craig McPheeters, and Brian Wyvill. Data structure for soft objects. The Visual Computer, 2:227–234, 1986.


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