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Layer-based video registration

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Abstract.

Registration of a mission video sequence with a reference image without any metadata (camera location, viewing angles, and reference DEMs) is still a challenging problem. This paper presents a layer-based approach to registering a video sequence to a reference image of a 3D scene containing multiple layers. First, the robust layers from a mission video sequence are extracted and a layer mosaic is generated for each layer, where the relative transformation parameters between consecutive frames are estimated. Then, we formulate the image-registration problem as a region-partitioning problem, where the overlapping regions between two images are partitioned into supporting and nonsupporting (or outlier) regions, and the corresponding motion parameters are also determined for the supporting regions. In this approach, we first estimate a set of sparse, robust correspondences between the first frame and reference image. Starting from corresponding seed patches, the aligned areas are expanded to the complete overlapping areas for each layer using a graph-cut algorithm with level set, where the first frame is registered to the reference image. Then, using the transformation parameters estimated from the mosaic, we initially align the remaining frames in the video to the reference image. Finally, using the same partitioning framework, the registration is further refined by adjusting the aligned areas and removing outliers. Several examples are demonstrated in the experiments to show that our approach is effective and robust.

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References

  1. Ayer S, Sawhney H (1995) Layered representation of motion video using robust maximum-likelihood estimation of mixture models and MDL encoding. In: International conference on computer vision

  2. Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222-1239

    Google Scholar 

  3. Brown L (1992) A survey of image registration techniques. ACM Comput Surv 24(4):325-376

    Google Scholar 

  4. Ferrari V, Tuytellars T, Van Gool L (2003) Wide-baseline multiple-view correspondences. In: IEEE conference on computer vision and pattern recognition

  5. Horn B, Schunck B (1981) Determining optical flow. Artif Intell 17:185-203

    Article  Google Scholar 

  6. Ke Q, Kanade T (2002) A robust subspace approach to layer extraction. In: IEEE workshop on motion and video computing

  7. Keller Y, Averbuch A (2003) Implicit similarity: a new approach to multi-sensor image registration. In: IEEE conference on computer vision and pattern recognition

  8. Khan S, Shah M (2001) Object based segmentation of video using color, motion and spatial information. In: IEEE conference on computer vision and pattern recognition

  9. Kolmogorov V, Zabih R (2002) What energy functions can be minimized via graph cuts? In: European conference on computer vision

  10. Osher S, Fedkiw R (2003) Level set methods and dynamic implicit surfaces. Springer, Berlin Heidelberg New York

  11. Sawhney H, Hsu S, Kumar R (1998) Robust video mosaicing through topology inference and local to global alignment. In: European conference on computer vision

  12. Sethian J (1999) Level set methods and fast marching methods. Cambridge University Press, Cambridge, UK

  13. Sheikh Y, Shah M (2004) Aligning ‘dissimilar’ images directly. In: Asian conference on computer vision

  14. Sheikh Y, Khan S, Shah M, Cannata R (2003) Geodetic alignment of aerial video frames. In: Video registration, video computing series. Kluwer, Dordrecht

  15. Shen D, Davatzitos C (2002) HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE Trans Med Imag 21:1421-1439

    Google Scholar 

  16. Shah M, Kumar R (eds) (2003) Video registration. Kluwer, Dordrecht

  17. Szeliski R (1996) Video mosaics for virtual environments. IEEE Comput Graph Appl 16:22-30

    Google Scholar 

  18. Tomasi C, Manduchi r (1998) Bilateral filtering for gray and color images. In: International conference on computer vision

  19. Wills J, Agarwal S, Belongie S (2003) What went where. In: IEEE conference on computer vision and pattern recognition

  20. Wildes R, Hirvonen D, Hsu S, Kumar R, Lehman W, Matei B, Zhao W (2001) Video georegistration: algorithm and quantitative evaluation. In: International conference on computer vision

  21. Xiao J, Shah M (2003) Two-frame wide baseline matching. In: International conference on computer vision

  22. Xiao J, Shah M (2004) Motion layer extraction in the presence of occlusion using graph cut. In: IEEE conference on computer vision and pattern recognition

  23. Zheng Q, Chellappa R (1993) A computational vision approach to image registration. IEEE Trans Image Process 2(3):311-326

    Google Scholar 

  24. Zitova B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21:977-1000

    Google Scholar 

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Correspondence to Jiangjian Xiao.

Additional information

Received: 16 September 2004, Accepted: 23 September 2004, Published online: 19 January 2005

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Xiao, J., Shah, M. Layer-based video registration. Machine Vision and Applications 16, 75–84 (2005). https://doi.org/10.1007/s00138-004-0162-5

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  • DOI: https://doi.org/10.1007/s00138-004-0162-5

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