Advertisement

Automatic Blending of Multiple Perspective Views for Aesthetic Composition

  • Kairi Mashio
  • Kenichi Yoshida
  • Shigeo Takahashi
  • Masato Okada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6133)

Abstract

Hand-drawn pictures differ from ordinary perspective images in that the entire scene is composed of local feature regions each of which is projected individually as seen from its own vista point. This type of projection, called nonperspective projection, has served as one of the common media for our visual communication while its automatic generation process still needs more research. This paper presents an approach to automatically generating aesthetic nonperspective images by simulating the deformation principles seen in such hand-drawn pictures. The proposed approach first locates the optimal viewpoint for each feature region by maximizing the associated viewpoint entropy value. These optimal viewpoints are then incorporated into the 3D field of camera parameters, which is represented by regular grid samples in the 3D scene space. Finally, the camera parameters are smoothed out in order to eliminate any unexpected discontinuities between neighboring feature regions, by taking advantage of image restoration techniques. Several nonperspective images are generated to demonstrate the applicability of the proposed approach.

Keywords

Monte Carlo Sampling Camera Parameter Grid Sample Representative Object Mesh Vertex 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawala, M., Zorin, D., Munzner, T.: Artistic multiprojection rendering. In: Eurographics Rendering Workshop 2000, pp. 125–136 (2000)Google Scholar
  2. 2.
    Kurzion, Y., Yagel, R.: Interactive space deformation with hardware-assisted rendering. IEEE Computer Graphics and Applications 17(5), 66–77 (1997)CrossRefGoogle Scholar
  3. 3.
    Singh, K.: A fresh perspective. In: Proceedings of Graphics Interface 2002, pp. 17–24 (2002)Google Scholar
  4. 4.
    Coleman, P., Singh, K., Barrett, L., Sudarsanam, N., Grimm, C.: 3D scene-space widgets for non-linear projection. In: Proceedings of GRAPHITE 2005, pp. 221–228 (2005)Google Scholar
  5. 5.
    Rademacher, P.: View-dependent geometry. In: Proceedings of SIGGRAPH 1999, pp. 439–446 (1999)Google Scholar
  6. 6.
    Martín, D., García, S., Torres, J.C.: Observer dependent deformations in illustration. In: Proceedings of the 1st International Symposium on Non-Photorealistic Animation and Rendering (NPAR 2000), pp. 75–82 (2000)Google Scholar
  7. 7.
    Takahashi, S., Ohta, N., Nakamura, H., Takeshima, Y., Fujishiro, I.: Modeling surperspective projection of landscapes for geographical guide-map generation. Computer Graphics Forum 21(3), 259–268 (2002)CrossRefGoogle Scholar
  8. 8.
    Takahashi, S., Yoshida, K., Shimada, K., Nishita, T.: Occlusion-free animation of driving routes for car navigation systems. IEEE Transactions on Visualization and Computer Graphics 12(5), 1141–1148 (2006)CrossRefGoogle Scholar
  9. 9.
    Vázquez, P.-P., Feixas, M., Sbert, M., Heidrich, W.: Viewpoint selection using view entropy. In: Proceedings of Vision Modeling and Visualization Conference (VMV 2001), pp. 273–280 (2001)Google Scholar
  10. 10.
    Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. ACM Transactions on Graphics 24(3), 659–666 (2005)CrossRefGoogle Scholar
  11. 11.
    Vázquez, P.P., Feixas, M., Sbert, M., Llobet, A.: Realtime automatic selection of good molecular views. Computers and Graphics 30(1), 98–110 (2006)CrossRefGoogle Scholar
  12. 12.
    Sokolov, D., Plemenos, D., Tamine, K.: Viewpoint quality and global scene exploration strategies. In: Proceedings of International Conference on Computer Graphics Theory and Applications (GRAPP 2006), pp. 184–191 (2006)Google Scholar
  13. 13.
    Mühler, K., Neugebauer, M., Tietjen, C., Preim, B.: Viewpoint selection for intervention planning. In: Proceedings of Eurographics/IEEE-VGTC Symposium on Visualization, pp. 267–274 (2007)Google Scholar
  14. 14.
    Elmqvist, N., Tsigas, P.: A taxonomy of 3D occlusion management for visualization. IEEE Transactions on Visualization and Computer Graphics 14(5), 1095–1109 (2008)CrossRefGoogle Scholar
  15. 15.
    Geman, D.: Random fields and inverse problems in imaging. In: Y. Vardi, M. (ed.) CAV 1998. LNCS, vol. 1427, pp. 113–193. Springer, Heidelberg (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kairi Mashio
    • 1
  • Kenichi Yoshida
    • 1
  • Shigeo Takahashi
    • 1
  • Masato Okada
    • 1
    • 2
  1. 1.The University of Tokyo, JapanJapan
  2. 2.RIKEN, JapanSaitamaJapan

Personalised recommendations