Advertisement

High-Resolution Video from Series of Still Photographs

  • Ge Jin
  • James K. Hahn
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)

Abstract

In this paper, we explored the problem of creating a high-resolution video from a series of still photographs. Instead of enhancing the resolution from the video stream, we consider the problem of generating a high-resolution video as an image synthesis problem. Using the continuous shot in the digital camera, we can get a series of still photographs at 2 to 3 frames pre second. The main challenge in our approach is to synthesize the in between frames from two consecutive still images. The image synthesis approach varies based on the scene motion and image characteristics. We have applied optical flow, image segmentation, image filtering and skeleton based image warping techniques to generate high-resolution video.

Keywords

Video Synthesis Optical Flow Image Segmentation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Horn, B., Schunck, B.: Determining Optical Flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  2. 2.
    Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proc. of International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)Google Scholar
  3. 3.
    Shi, J., Tomasi, C.: Good features to track. In: IEEE CVPR 1994, pp. 593–600 (1994)Google Scholar
  4. 4.
    Bouguet, J.Y.: Pyramidal Implementation of the Lucas Kanade Feature Tracker. Intel Corporation, Microprocessor Research Labs (2000),Google Scholar
  5. 5.
    Sand, P., Teller, S.: Video matching. ACM Transactions on Graphics 23(3), 592–599 (2004)CrossRefGoogle Scholar
  6. 6.
    Beier, T., Neely, S.: Feature-based image metamorphosis. In: Proc. ACM SIGGRAPH, July 1992, pp. 35–42 (1992)Google Scholar
  7. 7.
    Wolberg, G.: Skeleton Based Image Warping. Visual Computer 5(1), 95–108 (1989)CrossRefGoogle Scholar
  8. 8.
    Seitz, S.M., Dyer, C.R.: View morphing. In: Proc. ACM SIGGRAPH 1996, pp. 21–30 (1996)Google Scholar
  9. 9.
    Horry, Y., Anjoy, K., Arai, K.: Tour into the picture: using a spidery mesh interface to make animation from a single image. In: Proc. ACM SIGGRAPH, August 1997, pp. 225–232 (1997)Google Scholar
  10. 10.
    Chen, S.E.: Quicktime VR - an image-based approach to virtual environment navigation. In: Proc. of ACM SIGGRAPH 1995, pp. 29–38 (1995)Google Scholar
  11. 11.
    Chuang, Y.Y., Goldman, D.B., Zheng, K.C., Curless, B., Salesin, D.H., Szeliski, R.: Animating pictures with stochastic motion textures. ACM Transactions on Graphics 24(3), 853–860 (2005)CrossRefGoogle Scholar
  12. 12.
    Brostow, G.J., Essa, I.: Image-based motion blur for stop motion animation. In: Proc. of ACM SIGGRAPH 2001, pp. 561–566 (2001)Google Scholar
  13. 13.
    Liu, C., Torralba, A., Freeman, W.T., Durand, F., Adelson, E.H.: Motion magnification. ACM Trans. Graphics 24(3), 519–526 (2005)CrossRefGoogle Scholar
  14. 14.
    Li, Y., Sun, J., Tang, C.K., Shum, H.Y.: Lazy snapping. ACM Transactions on Graphics 23(3), 303–308 (2004)CrossRefGoogle Scholar
  15. 15.
    Rother, C., Kolmogorov, V., Blake, A.: ”GrabCut”: interactive foreground extraction using iterated graph cuts. ACM Trans. on Graphics 23(3), 309–314 (2004)CrossRefGoogle Scholar
  16. 16.
    Li, Y., Sun, J., Shum, H.Y.: Video object cut and paste. ACM Transactions on Graphics 24(3), 595–600 (2005)CrossRefGoogle Scholar
  17. 17.
    Chuang, Y.Y., Agrawala, M., Curless, B., Salesin, D.H., Szeliski, R.: Video matting of complex scenes. In: Proceedings of ACM SIGGRAPH, pp. 243–248 (2002)Google Scholar
  18. 18.
    Sun, J., Jia, J., Tang, C.K., Shum, H.Y.: Poisson matting. ACM Transactions on Graphics 23(3), 315–321 (2004)CrossRefGoogle Scholar
  19. 19.
    Vezhnevets, V., Konouchine, V.: ”Grow-Cut” - Interactive Multi-Label N-D Image Segmentation. In: Int. conf. on the Computer Graphics and Vision (Graphicon 2005), pp. 150–156 (2005)Google Scholar
  20. 20.
    OpenCV: OpenCV: The Open Computer Vision Library. Sourceforge.net/projects/opencvlibraryGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ge Jin
    • 1
  • James K. Hahn
    • 1
  1. 1.Department of Computer ScienceThe George Washington UniversityWashington DCUSA

Personalised recommendations