Advertisement

Combining Geometric and Appearance Priors for Robust Homography Estimation

  • Eduard Serradell
  • Mustafa Özuysal
  • Vincent Lepetit
  • Pascal Fua
  • Francesc Moreno-Noguer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)

Abstract

The homography between pairs of images are typically computed from the correspondence of keypoints, which are established by using image descriptors. When these descriptors are not reliable, either because of repetitive patterns or large amounts of clutter, additional priors need to be considered. The Blind PnP algorithm makes use of geometric priors to guide the search for matches while computing camera pose. Inspired by this, we propose a novel approach for homography estimation that combines geometric priors with appearance priors of ambiguous descriptors. More specifically, for each point we retain its best candidates according to appearance. We then prune the set of potential matches by iteratively shrinking the regions of the image that are consistent with the geometric prior. We can then successfully compute homographies between pairs of images containing highly repetitive patterns and even under oblique viewing conditions.

Keywords

Gaussian Mixture Model Model Point Candidate Selection Repetitive Pattern Potential Match 
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.

Supplementary material

978-3-642-15558-1_5_MOESM1_ESM.avi (2.8 mb)
Electronic Supplementary Material (2,847 KB)
978-3-642-15558-1_5_MOESM2_ESM.avi (2.5 mb)
Electronic Supplementary Material (2,601 KB)
978-3-642-15558-1_5_MOESM3_ESM.avi (1.8 mb)
Electronic Supplementary Material (1,801 KB)
978-3-642-15558-1_5_MOESM4_ESM.avi (1.9 mb)
Electronic Supplementary Material (1,942 KB)
978-3-642-15558-1_5_MOESM5_ESM.avi (1.7 mb)
Electronic Supplementary Material (1,791 KB)
978-3-642-15558-1_5_MOESM6_ESM.avi (1.5 mb)
Electronic Supplementary Material (1,580 KB)

References

  1. 1.
    Szeliski, R.: Image Alignment and Stitching: A Tutorial. Found. Trends. Comput. Graph. Vis. 2, 1–104 (2006)CrossRefGoogle Scholar
  2. 2.
    Scherrer, C., Pilet, J., Lepetit, V., Fua, P.: Souvenirs du Monde des Montagnes. Leonardo, special issue on ACM SIGGRAPH, 350–355 (2009)Google Scholar
  3. 3.
    Wagner, D., Reitmayr, G., Mulloni, A., Drummond, T., Schmalstieg, D.: Pose Tracking from Natural Features on Mobile Phones. In: ISMAR (2008)Google Scholar
  4. 4.
    Moreno-Noguer, F., Lepetit, V., Fua, P.: Pose Priors for Simultaneously Solving Alignment and Correspondence. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 405–418. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Chum, O., Matas, J.: Matching with PROSAC - Progressive Sample Consensus. In: CVPR, pp. 220–226 (2005)Google Scholar
  6. 6.
    Lowe, D.: Distinctive Image Features From Scale-Invariant Keypoints. IJCV (2004)Google Scholar
  7. 7.
    Fischler, M., Bolles, R.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. ACM Comm., 381–395 (1981)Google Scholar
  8. 8.
    Ayache, N., Faugeras, O.D.: Hyper: A New Approach for the Recognition and Positioning to Two-Dimensional Objects. PAMI, 44–54 (1986)Google Scholar
  9. 9.
    Grimson, W.E.L.: The Combinatorics of Heuristic Search Termination for Object Recognition in Cluttered Environments. PAMI, 920–935 (1991)Google Scholar
  10. 10.
    Lamdan, Y., Wolfson, H.J.: Geometric Hashing: A General and Efficient Model-Based Recognition Scheme. In: ICCV, pp. 238–249 (1988)Google Scholar
  11. 11.
    Burns, J.B., Weiss, R.S., Riseman, E.M.: View Variation of Point-Set and Line- Segment Features. PAMI, 51–68 (1993)Google Scholar
  12. 12.
    Beis, J.S., Lowe, D.G.: Indexing Without Invariants in 3d Object Recognition. PAMI, 1000–1015 (1999)Google Scholar
  13. 13.
    Olson, C.F.: Efficient Pose Clustering Using a Randomized Algorithm. IJCV (1997)Google Scholar
  14. 14.
    Stockman, G.: Object Recognition and Localization Via Pose Clustering. Comput. Vision Graph. Image Process. 40, 361–387 (1987)CrossRefGoogle Scholar
  15. 15.
    Tordoff, B., Murray, D.W.: Guided Sampling and Consensus for Motion Estimation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 82–98. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Raguram, R., Frahm, J., 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
  17. 17.
    David, P., DeMenthon, D., Duraiswami, R., Samet, H.: Softposit: Simultaneous Pose and Correspondence Determination. IJCV, 259–284 (2004)Google Scholar
  18. 18.
    Ozuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast Keypoint Recognition Using Random Ferns. PAMI, 448–461 (2010)Google Scholar
  19. 19.
    Lowe, D.: Object Recognition From Local Scale-Invariant Features. In: ICCV, pp. 1150–1157 (1999)Google Scholar
  20. 20.
    Bay, H., Tuytelaars, T., Gool, L.: Surf: Speeded Up Robust Features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Davison, A.J.: Active Search for Real-Time Vision. In: ICCV, pp. 66–73 (2005)Google Scholar
  22. 22.
    Chili, M., Davison, A.: Active Matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 72–85. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  23. 23.
    Abdel-Aziz, Y.I., Karara, H.M.: Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry. In: Proc. ASP/UI Symp. Close-Range Photogrammetry, pp. 1–18 (1971)Google Scholar
  24. 24.
    Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.: A Comparison of Affine Region Detectors. IJCV (2005)Google Scholar
  25. 25.
    Stewart, C.: Robust Parameter Estimation in Computer Vision. SIAM Rev (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Eduard Serradell
    • 1
  • Mustafa Özuysal
    • 2
  • Vincent Lepetit
    • 2
  • Pascal Fua
    • 2
  • Francesc Moreno-Noguer
    • 1
  1. 1.Institut de Robòtica i Informàtica IndustrialCSIC-UPCBarcelonaSpain
  2. 2.Computer Vision LaboratoryEPFLLausanneSwitzerland

Personalised recommendations