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Fusing Features in Direct Volume Rendered Images

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Advances in Visual Computing (ISVC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4291))

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Abstract

In this paper, we propose a novel framework which can fuse multiple user selected features in different direct volume rendered images into a comprehensive image according to users’ preference. The framework relies on three techniques, i.e., user voting, genetic algorithm, and image similarity. In this framework, we transform the fusing problem to an optimization problem with a novel energy function which is based on user voting and image similarity. The optimization problem can then be solved by the genetic algorithm. Experimental results on some real volume data demonstrate the effectiveness of our framework.

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References

  1. Baudisch, P., Gutwin, C.: Multiblending: displaying overlapping windows simultaneously without the drawbacks of alpha blending. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 367–374 (2004)

    Google Scholar 

  2. Tzeng, F.Y., Lum, E.B., Ma, K.L.: An intelligent system approach to higher-dimensional classification of volume data. IEEE Transactions on Visualization and Computer Graphics 11, 273–284 (2005)

    Article  Google Scholar 

  3. Weiskopf, D., Engel, K., Ertl, T.: Interactive clipping techniques for texture-based volume visualization and volume shading. IEEE Transactions on Visualization and Computer Graphics 9, 298–312 (2003)

    Article  Google Scholar 

  4. Viola, I., Kanitsar, A., Gröller, M.E.: Importance-driven feature enhancement in volume visualization. IEEE Transactions on Visualization and Computer Graphics 11, 408–418 (2005)

    Article  Google Scholar 

  5. Rheingans, P., Ebert, D.: Volume illustration: Nonphotorealistic rendering of volume models. IEEE Transactions on Visualization and Computer Graphics 7, 253–264 (2001)

    Article  Google Scholar 

  6. Wang, L., Zhao, Y., Mueller, K., Kaufman, A.E.: The magic volume lens: An interactive focus+context technique for volume rendering. In: Proceedings of IEEE Visualization, pp. 367–374 (2005)

    Google Scholar 

  7. Kindlmann, G.: Transfer functions in direct volume rendering: Design, interface, interaction. In: Course notes of ACM SIGGRAPH (2002)

    Google Scholar 

  8. Marks, J., Andalman, B., Beardsley, P.A., Freeman, W., Gibson, S., Hodgins, J., Kang, T., Mirtich, B., Pfister, H., Ruml, W., Ryall, K., Seims, J., Shieber, S.: Design galleries: a general approach to setting parameters for computer graphics and animation. In: Proceedings of ACM SIGGRAPH, pp. 389–400 (1997)

    Google Scholar 

  9. Kindlmann, G., Durkin, J.W.: Semi-automatic generation of transfer functions for direct volume rendering. In: Proceedings of Volume Visualization Symposium, pp. 79–86 (1998)

    Google Scholar 

  10. Ma, K.L.: Image graps - a novel interface for visual data exploration. In: Proceedings of IEEE Visualization, pp. 81–88 (1999)

    Google Scholar 

  11. König, A.H., Gröller, E.M.: Mastering transfer function specification by using volumepro technology. In: Proceedings of Spring Conference on Computer Graphics, pp. 279–286 (2001)

    Google Scholar 

  12. Sims, K.: Artificial evolution for computer graphics. In: Proceedings of ACM SIGGRAPH, pp. 319–328 (1991)

    Google Scholar 

  13. He, T., Hong, L., Kaufman, A., Pfister, H.: Generation of transfer functions with stochastic search techniques. In: Proceedings of IEEE Visualization, pp. 227–234 (1996)

    Google Scholar 

  14. House, D., Bair, A., Ware, C.: On the optimization of visualizations of complex phenomena. In: Proceedings of IEEE Visualization, pp. 87–94 (2005)

    Google Scholar 

  15. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)

    Google Scholar 

  16. Kitware (The visualization toolkit), http://www.vtk.org

  17. Jong, K.A.D.: An analysis of the behavior of a class of genetic adaptive systems. PhD thesis (1975)

    Google Scholar 

  18. Wong, M.L., Wong, T.T., Fok, K.L.: Parallel evolutionary algorithms on graphics processing unit. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 2286–2293 (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Hong Kong University of Science and Technology,  

    Yingcai Wu, Huamin Qu, Hong Zhou & Ming-Yuen Chan

Authors
  1. Yingcai Wu
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  2. Huamin Qu
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  3. Hong Zhou
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  4. Ming-Yuen Chan
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Intel, 95052, Santa Clara, CA, USA

    Ara Nefian

  7. University of California, 430, Computer Science Building, 92697-3425, Irvine, CA, USA

    Gopi Meenakshisundaram

  8. Institute for Data Analysis and Visualization, P.O. Box

    Valerio Pascucci

  9. Department of Computer Science and Engineering, Czech Technical University in Prague, Czech

    Jiri Zara

  10. Rockwell Scientific, 1049 Camino Dos Rios, 91360, Thousand Oaks, CA, USA

    Jose Molineros

  11. Computer Graphics Group, Bielefeld University, D-33501, Bielefeld, Germany

    Holger Theisel

  12. Hewlett Packard Labs, Palo Alto, CA, USA

    Tom Malzbender

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© 2006 Springer-Verlag Berlin Heidelberg

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Wu, Y., Qu, H., Zhou, H., Chan, MY. (2006). Fusing Features in Direct Volume Rendered Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_28

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  • DOI: https://doi.org/10.1007/11919476_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48628-2

  • Online ISBN: 978-3-540-48631-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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