Skip to main content

High-Resolution Video from Series of Still Photographs

  • Conference paper
Advances in Visual Computing (ISVC 2006)

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

Included in the following conference series:

  • 1716 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Horn, B., Schunck, B.: Determining Optical Flow. Artificial Intelligence 17, 185–203 (1981)

    Article  Google Scholar 

  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. Shi, J., Tomasi, C.: Good features to track. In: IEEE CVPR 1994, pp. 593–600 (1994)

    Google Scholar 

  4. Bouguet, J.Y.: Pyramidal Implementation of the Lucas Kanade Feature Tracker. Intel Corporation, Microprocessor Research Labs (2000),

    Google Scholar 

  5. Sand, P., Teller, S.: Video matching. ACM Transactions on Graphics 23(3), 592–599 (2004)

    Article  Google Scholar 

  6. Beier, T., Neely, S.: Feature-based image metamorphosis. In: Proc. ACM SIGGRAPH, July 1992, pp. 35–42 (1992)

    Google Scholar 

  7. Wolberg, G.: Skeleton Based Image Warping. Visual Computer 5(1), 95–108 (1989)

    Article  Google Scholar 

  8. Seitz, S.M., Dyer, C.R.: View morphing. In: Proc. ACM SIGGRAPH 1996, pp. 21–30 (1996)

    Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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. Liu, C., Torralba, A., Freeman, W.T., Durand, F., Adelson, E.H.: Motion magnification. ACM Trans. Graphics 24(3), 519–526 (2005)

    Article  Google Scholar 

  14. Li, Y., Sun, J., Tang, C.K., Shum, H.Y.: Lazy snapping. ACM Transactions on Graphics 23(3), 303–308 (2004)

    Article  Google Scholar 

  15. Rother, C., Kolmogorov, V., Blake, A.: ”GrabCut”: interactive foreground extraction using iterated graph cuts. ACM Trans. on Graphics 23(3), 309–314 (2004)

    Article  Google Scholar 

  16. Li, Y., Sun, J., Shum, H.Y.: Video object cut and paste. ACM Transactions on Graphics 24(3), 595–600 (2005)

    Article  Google Scholar 

  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. Sun, J., Jia, J., Tang, C.K., Shum, H.Y.: Poisson matting. ACM Transactions on Graphics 23(3), 315–321 (2004)

    Article  Google Scholar 

  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. OpenCV: OpenCV: The Open Computer Vision Library. Sourceforge.net/projects/opencvlibrary

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Jin, G., Hahn, J.K. (2006). High-Resolution Video from Series of Still Photographs. 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_90

Download citation

  • DOI: https://doi.org/10.1007/11919476_90

  • 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)

Publish with us

Policies and ethics