Bilateral Functions for Global Motion Modeling

  • Wen-Yan Daniel Lin
  • Ming-Ming Cheng
  • Jiangbo Lu
  • Hongsheng Yang
  • Minh N. Do
  • Philip Torr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8692)


This paper proposes modeling motion in a bilateral domain that augments spatial information with the motion itself. We use the bilateral domain to reformulate a piecewise smooth constraint as continuous global modeling constraint. The resultant model can be robustly computed from highly noisy scattered feature points using a global minimization. We demonstrate how the model can reliably obtain large numbers of good quality correspondences over wide baselines, while keeping outliers to a minimum.


Image Pair Outlier Removal Bilateral Model Motion Coherence Feature Correspondence 
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.


  1. 1.
    Morel, J., Yu, G.: Asift: A new framework for fully affine invariant image comparison. SIAM Journal on Imaging Sciences 2(2), 438–469 (2009)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)CrossRefGoogle Scholar
  3. 3.
    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. of the ACM 24, 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Raguram, R., Frahm, J.-M., Pollefeys, M.: A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 500–513. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  6. 6.
    Longuet-Higgins, H.C.: A computer algorithm for reconstructing a scene from two projections. Nature, 133–135 (1981)Google Scholar
  7. 7.
    Brown, M., Lowe, D.: Automatic panoramic image stitching using invariant features. IJCV 1(74), 59–73 (2007)CrossRefGoogle Scholar
  8. 8.
    Serradell, E., Özuysal, M., Lepetit, V., Fua, P., Moreno-Noguer, F.: Combining geometric and appearance priors for robust homography estimation. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 58–72. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  9. 9.
    Sprengel, R., Rohr, K., Stiehl, H.S.: Thin-plate spline approximation for image registration. In: Proc. of Engineering in Medicine and Biology Society (1996)Google Scholar
  10. 10.
    Lin, W.Y., Cheng, M.M., Zheng, S., Lu, J., Crook, N.: Robust non-parametric data fitting for correspondence modeling. In: IEEE ICCV (2013)Google Scholar
  11. 11.
    Yuille, A.L., Grywacz, N.M.: The motion coherence theory. In: IEEE ICCV (1988)Google Scholar
  12. 12.
    Black, M.J., Anandan, P.: The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding (1996)Google Scholar
  13. 13.
    Ye, M., Haralick, R.M., Shapiro, L.G.: Estimating piecewise-smooth optical flow with global matching and graduated optimization. PAMI (2003)Google Scholar
  14. 14.
    Sinha, S.N., Steedly, D., Szeliski, R.: Piecewise planar stereo for image-based rendering. In: ICCV (2009)Google Scholar
  15. 15.
    Myronenko, A., Song, X., Carreira-Perpinan, M.: Non-rigid point set registration: Coherent point drift. In: NIPS (2007)Google Scholar
  16. 16.
    Lin, W.Y., Liu, S., Matsushita, Y., Ng, T.T., Cheong, L.F.: Smoothly varying affine stitching. In: IEEE CVPR (2011)Google Scholar
  17. 17.
    Raguram, R., Frahm, J.M.: Recon: Scale-adaptive robust estimation via residual consensus. In: ICCV (2011)Google Scholar
  18. 18.
    Pham, T.T., Chin, T.J., Yu, J., Suter, D.: The random cluster model for robust geometric fitting. In: CVPR (2012)Google Scholar
  19. 19.
    Weinzaepfel, P., Revaud, J., Harchaoui, Z., Schmid, C.: Deepflow: Large displacement optical flow with deep matching. In: ICCV (2013)Google Scholar
  20. 20.
    Brox, T., Malik, J.: Large displacement optical flow: Descriptor matching in variational motion estimation. IEEE TPAMI (2010)Google Scholar
  21. 21.
    Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence (1981)Google Scholar
  22. 22.
    Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of Imaging Understanding Workshop (1981)Google Scholar
  23. 23.
    Torresani, L., Kolmogorov, V., Rother, C.: Feature correspondence via graph matching: Models and global optimization. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 596–609. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  24. 24.
    Garg, R., Roussos, A., Agapito, L.: Dense variational reconstruction of non-rigid surfaces from monocular video. In: CVPR (2013)Google Scholar
  25. 25.
    Pizarro, D., Bartoli, A.: Feature-based deformable surface detection with self-occlusion. IJCV (2012)Google Scholar
  26. 26.
    Tomasi, C., Manduch, R.: Bilateral filtering for gray and color images. In: IEEE ICCV (1998)Google Scholar
  27. 27.
    Xiao, J., Cheng, H., Sawhney, H.S., Rao, C., Isnardi, M.: Bilateral filtering-based optical flow estimation with occlusion detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 211–224. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  28. 28.
    Barnes, C., Shechtman, E., Goldman, D.B., Finkelstein, A.: The generalized PatchMatch correspondence algorithm. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 29–43. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  29. 29.
    Lhuiller, M., Quan, L.: A quasi-dense approach to surface reconstruction from uncalibrated images. PAMI (2005)Google Scholar
  30. 30.
    HaCohen, Y., Shechtman, E., Goldman, D.B., Lischinski, D.: Non-rigid dense correspondence with applications for image enhancement. ACM TOG (2011)Google Scholar
  31. 31.
    Hartley, R.I.: In defense of the eight-point algorithm. IEEE TPAMI 19(6), 580–593 (1997)CrossRefGoogle Scholar
  32. 32.
    Strecha, C., von Hansen, W., Gool, L.V., Fua, P., Thoennessen, U.: On benchmarking camera calibration and multi-view stereo for high resolution imagery. In: CVPR (2008)Google Scholar
  33. 33.
    Heinly, J., Dunn, E., Frahm, J.-M.: Comparative evaluation of binary features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 759–773. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  34. 34.
    Kovesi, P.D.: MATLAB and Octave functions for computer vision and image processing,
  35. 35.
    Torr, P.H.S., Zisserman, A.: Mlesac: A new robust estimator with application to estimating image geometry. Computer Vision and Image Understanding (2010)Google Scholar
  36. 36.
    Konouchine, A., Gaganov, V., Veznevets, V.: A new maximum likelihood robust estimator. Computer Vision and Image Understanding (2005)Google Scholar
  37. 37.
    Wong, H.S., Chin, T.J., Yu, J., Suter, D.: Dynamic and hierarchical multi-structure geometric model fitting. In: ICCV (2011)Google Scholar
  38. 38.
    Vedaldi, A., Fulkerson, B.: VLFeat: An open and portable library of computer vision algorithms (2008)Google Scholar
  39. 39.
    Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: a factorization method. IJCV (1992)Google Scholar
  40. 40.
    Torr, P., Zisserman, A.: Robust parameterization and computation of the trifocal tensor. Image and Vision Computing 15, 591–605 (1997)CrossRefGoogle Scholar
  41. 41.
    Agrawal, M.: Practical camera auto calibration using semidefinite programming. In: WMVC (2007)Google Scholar
  42. 42.
  43. 43.
    Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV (2001)Google Scholar
  44. 44.
    Yang, H., Lin, W.Y., Lu, J.: Daisy filter flow: A generalized discrete approach to dense correspondences. In: CVPR (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Wen-Yan Daniel Lin
    • 1
  • Ming-Ming Cheng
    • 2
  • Jiangbo Lu
    • 1
  • Hongsheng Yang
    • 3
  • Minh N. Do
    • 4
  • Philip Torr
    • 2
  1. 1.Advanced Digital Sciences CenterSingapore
  2. 2.Oxford UniversityUK
  3. 3.University of North Carolina at Chapel HillUSA
  4. 4.University of Illinois at Urbana-ChampaignUSA

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