skip to main content
article

Video matching

Authors Info & Claims
Published:01 August 2004Publication History
Skip Abstract Section

Abstract

This paper describes a method for bringing two videos (recorded at different times) into spatiotemporal alignment, then comparing and combining corresponding pixels for applications such as background subtraction, compositing, and increasing dynamic range. We align a pair of videos by searching for frames that best match according to a robust image registration process. This process uses locally weighted regression to interpolate and extrapolate high-likelihood image correspondences, allowing new correspondences to be discovered and refined. Image regions that cannot be matched are detected and ignored, providing robustness to changes in scene content and lighting, which allows a variety of new applications.

Skip Supplemental Material Section

Supplemental Material

References

  1. AGARWALA, A., DONTCHEVA, M., AGRAWALA, M., DRUCKER, S., COLBURN, A., CURLESS, B., SALESIN, D., AND COHEN, M. 2004. Interactive digital photomontage. ACM Trans. Graph., In press.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. ATKESON, C. G., MOORE, A. W., AND SCHAAL, S. 1997. Locally weighted learning. Artificial Intelligence Review 11, 1-5, 11--73.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. BEAUCHEMIN, S. S., AND BARRON, J. L. 1995. The computation of optical flow. ACM Computing Surveys 27, 3, 433--467.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. BIRCHFIELD, S., AND TOMASI, C. 1998. A pixel dissimilarity measure that is insensitive to image sampling. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 4, 401--406.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. BLACK, M. J., AND ANANDAN, P. 1996. The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding 63, 1, 75--104.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. BROWN, M., AND LOWE, D. G. 2003. Recognising panoramas. In ICCV, 1218--1225.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. CASPI, Y., AND IRANI, M. 2000. A step towards sequence to sequence alignment. In CVPR, 682--689.]]Google ScholarGoogle Scholar
  8. CHUANG, Y.-Y., AGARWALA, A., CURLESS, B., SALESIN, D. H., AND SZELISKI, R. 2002. Video matting of complex scenes. ACM Trans. Graph. 21, 3, 243--248.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. DAVISON, A. J., DEUTSCHER, J., AND REID, I. D. 2001. Markerless motion capture of complex full-body movement for character animation. In Eurographics Workshop on Animation and Simulation, 3--14.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. DEBEVEC, P. E., AND MALIK, J. 1997. Recovering high dynamic range radiance maps from photographs. In SIGGRAPH, 369--378.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. DEMPSTER, A. P., LAIRD, N. M., AND RUBIN, D. B. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B 39, 1, 1--38.]]Google ScholarGoogle ScholarCross RefCross Ref
  12. FERRARI, V., TUYTELAARS, T., AND VAN GOOL, L. 2001. Real-time affine region tracking and coplanar grouping. In CVPR, 226--233.]]Google ScholarGoogle Scholar
  13. FISCHLER, M. A., AND BOLLES, R. C. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 6, 381--395.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. HARRIS, C., AND STEPHENS, M. 1988. A combined corner and edge detector. In 4th Alvey Vision Conference, 147--151.]]Google ScholarGoogle ScholarCross RefCross Ref
  15. HARTLEY, R., AND ZISSERMAN, A. 2000. Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge, UK.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. KANAZAWA, Y., AND KANATANI, K. 2002. Robust image matching under a large disparity. In Workshop on Science of Computer Vision, 46--52.]]Google ScholarGoogle Scholar
  17. KANG, S. B., UYTTENDAELE, M., WINDER, S., AND SZELISKI, R. 2003. High dynamic range video. ACM Trans. Graph. 22, 3, 319--325.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. KUTULAKOS, K. N. 2000. Approximate N-view stereo. In ECCV, 67--83.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. LUCAS, B., AND KANADE, T. 1981. An iterative image registration technique with an application to stereo vision. In Int. Joint Conf. Artificial Intelligence, 674--679.]]Google ScholarGoogle Scholar
  20. NOBLE, A. 1989. Descriptions of Image Surfaces. PhD thesis, Oxford University, Oxford, UK.]]Google ScholarGoogle Scholar
  21. RAO, C., GRITAI, A., AND SHAH, M. 2003. View-invariant alignment and matching of video sequences. In ICCV, 939--945.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. SAND, P., AND TELLER, S. 2004. Video matching. Tech. Rep. LCS TR 947, MIT.]]Google ScholarGoogle Scholar
  23. SAWHNEY, H. S., GUO, Y., HANNA, K., KUMAR, R., ADKINS, S., AND ZHOU, S. 2001. Hybrid stereo camera: an IBR approach for synthesis of very high resolution stereoscopic image sequences. In SIGGRAPH, 451--460.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. SCHARSTEIN, D., AND SZELISKI, R. 2002. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision 47, 1-3, 7--42.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. SCHÖDL, A., SZELISKI, R., SALESIN, D. H., AND ESSA, I. 2000. Video textures. In SIGGRAPH, 489--498.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. SHI, J., AND TOMASI, C. 1994. Good features to track. In CVPR, 593--600.]]Google ScholarGoogle Scholar
  27. SMITH, P., SINCLAIR, D., CIPOLLA, R., AND WOOD, K. 1998. Effective corner matching. In British Machine Vision Conference, 545--556.]]Google ScholarGoogle ScholarCross RefCross Ref
  28. SZELISKI, R., AND SCHARSTEIN, D. 2002. Symmetric sub-pixel stereo matching. In ECCV, 525--540.]] Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Video matching

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in

          Full Access

          • Published in

            cover image ACM Transactions on Graphics
            ACM Transactions on Graphics  Volume 23, Issue 3
            August 2004
            684 pages
            ISSN:0730-0301
            EISSN:1557-7368
            DOI:10.1145/1015706
            Issue’s Table of Contents

            Copyright © 2004 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 1 August 2004
            Published in tog Volume 23, Issue 3

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • article

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader