Pattern Analysis and Applications

, Volume 21, Issue 1, pp 35–44 | Cite as

An efficient fundamental matrix estimation method for wide baseline images

  • Chun-Bao Xiao
  • Da-Zheng Feng
  • Ming-Dong Yuan
Theoretical Advances


Fundamental matrix estimation for wide baseline images is significantly difficult due to the fact that the proportion of inliers in putative correspondences is generally very low. Traditional robust fundamental matrix estimation methods, such as RANSAC, will encounter the problems of computational inefficiency and low accuracy when outlier ratio is high. In this paper, a novel robust estimation method called inlier set sample optimization is proposed to solve these problems. First, a one-class support vector machine-based preselection algorithm is performed to efficiently select a candidate inlier set from putative SIFT correspondences according to distribution consistency of features in location, scale and orientation. Then, the quasi-optimal inlier set is refined iteratively by maximizing a soft decision objective function. Finally, fundamental matrix is estimated with the optimal inlier set. Experimental results show that the proposed method is superior to several state-of-the-art robust methods in speed, accuracy and stability and is applicable to wide baseline images with large differences.


Robust estimation Fundamental matrix Wide baseline image Inlier selection One-class support vector machine Soft decision 


  1. 1.
    Hartley R, Zisserman A (2003) Multiple view geometry in computer vision, 2nd edn. Cambridge University Press, CambridgeMATHGoogle Scholar
  2. 2.
    Armangué X, Salvi J (2003) Overall view regarding fundamental matrix estimation. Image Vis Comput 21:205–220CrossRefGoogle Scholar
  3. 3.
    Zia MZ, Stark M, Schiele B, Schindler K (2013) Detailed 3d representations for object recognition and modelling. IEEE Trans Pattern Anal Mach Intell 35:2608–2623CrossRefGoogle Scholar
  4. 4.
    Naikal N, Yang AY, Sastry SS (2011) Informative feature selection for object recognition via sparse PCA. In: International conference on computer vision, pp 818–825Google Scholar
  5. 5.
    Wu B, Zhang Y, Zhu Q (2011) A triangulation-based hierarchical image matching method for wide-baseline images. Photogramm Eng Remote Sens 77:695–708CrossRefGoogle Scholar
  6. 6.
    Marcon M, Frigerio E, Sarti A, Tubaro S (2012) 3D wide baseline correspondences using depth-maps. Signal Process Image Commun 27:849–855CrossRefGoogle Scholar
  7. 7.
    Pizarro D, Bartoli A (2012) Feature-based deformable surface detection with self-occlusion reasoning. Int J Comput Vis 97:54–70CrossRefMATHGoogle Scholar
  8. 8.
    Tola E, Lepetit V, Fua P (2010) Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans Pattern Anal Mach Intell 32:815–830CrossRefGoogle Scholar
  9. 9.
    Kim D, Paik J (2010) Gait recognition using active shape model and motion prediction. IET Comput Vis 4:25–36CrossRefGoogle Scholar
  10. 10.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110CrossRefGoogle Scholar
  11. 11.
    Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110:346–359CrossRefGoogle Scholar
  12. 12.
    Wang K, Xiao P, Feng X, Wu G (2011) Image feature detection from phase congruency based on two-dimensional Hilbert transform. Pattern Recognit Lett 32:2015–2024CrossRefGoogle Scholar
  13. 13.
    Yang W, Sun C, Zhang L (2011) A multi-manifold discriminant analysis method for image feature extraction. Pattern Recognit 44:1649–1657CrossRefMATHGoogle Scholar
  14. 14.
    Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP 1:331–340Google Scholar
  15. 15.
    Chen HY, Lin YY, Chen BY (2013) Robust feature matching with alternate hough and inverted hough transforms. In: IEEE conference on computer vision and pattern recognition, pp 2762–2769Google Scholar
  16. 16.
    Miksik O, Mikolajczyk K (2012) Evaluation of local detectors and descriptors for fast feature matching. In: International conference on pattern recognition, pp 2681–2684Google Scholar
  17. 17.
    Sharma K, Kim SG, Singh MP (2012) An improved feature matching technique for stereo vision applications with the use of self-organizing map. Int J Precis Eng Manuf 13:1359–1368CrossRefGoogle Scholar
  18. 18.
    Fischler M, Bolles R (1981) Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Commun ACM 24:381–395MathSciNetCrossRefGoogle Scholar
  19. 19.
    Torr PH, Murray DW (1997) The development and comparison of robust methods for estimating the fundamental matrix. Int J Comput Vis 24:271–300CrossRefGoogle Scholar
  20. 20.
    Torr PH, Zisserman A (2000) MLESAC: a new robust estimator with application to estimating image geometry. Comput Vis Image Underst 78:138–156CrossRefGoogle Scholar
  21. 21.
    Torr PHS (2002) Bayesian model estimation and selection for epipolar geometry and generic manifold fitting. Int J Comput Vis 50:35–61CrossRefMATHGoogle Scholar
  22. 22.
    Chum O, Matas J, Kittler J (2003) Locally optimized RANSAC. In: Michaelis B, Krell G (eds) Pattern recognition. Springer, Berlin, pp 236–243Google Scholar
  23. 23.
    Shi XB, Liu F, Wang Y et al (2011) A Fundamental matrix estimation algorithm based on point weighting strategy. In: Proceedings of international conference on virtual reality and visualization. IEEE computer society press, Washington DC, pp 24–29Google Scholar
  24. 24.
    Tordoff BJ, Murray DW (2005) Guided-MLESAC: faster image transform estimation by using matching priors. IEEE Trans Pattern Anal Mach Intell 27:1523–1535CrossRefGoogle Scholar
  25. 25.
    Chum O, Matas J (2005) Matching with PROSAC-progressive sample consensus. In: IEEE conference on CVPR, vol 1, pp. 220–226Google Scholar
  26. 26.
    Xu M, Lu J (2012) Distributed RANSAC for the robust estimation of three-dimensional reconstruction. IET Comput Vis 6:324–333MathSciNetCrossRefGoogle Scholar
  27. 27.
    Adam A, Rivlin E, Shimshoni I (2001) ROR: rejection of outliers by rotations. IEEE Trans Pattern Anal Mach Intell 23:78–84CrossRefGoogle Scholar
  28. 28.
    Zhang D, Wang Y, Tao W (2012) Epipolar geometry estimation for wide baseline stereo. Int J Precis Eng Manuf 2:38–45Google Scholar
  29. 29.
    Zhang K, Li X, Zhang J (2014) A robust point-matching algorithm for remote sensing image registration. Geosci Remote Sens Lett 11:469–473CrossRefGoogle Scholar
  30. 30.
    Zhou HB, Zhang D, Chen C et al (2011) Discarding wide baseline mismatches with global and local transformation consistency. Electron Lett 47:25–26CrossRefGoogle Scholar
  31. 31.
    Choi S, Kim T, Yu W (2009) Performance evaluation of RANSAC family. In: Proceedings of the British machine vision conference (BMVC), pp 1–12Google Scholar
  32. 32.
    Moisan L, Stival B (2004) A probabilistic criterion to detect rigid point matches between two images and estimate the fundamental matrix. Int J Comput Vis 57:201–218CrossRefGoogle Scholar
  33. 33.
    Schölkopf B, Platt JC, Shawe-Taylor J et al (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13:1443–1471CrossRefMATHGoogle Scholar
  34. 34.
    Zhang Z (1998) Determining the epipolar geometry and its uncertainty: a review. Int J Comput Vis 27:161–195CrossRefGoogle Scholar
  35. 35.
    Zhao F, Wang H, Chai X, Ge S (2009) A fast and effective outlier detection method for matching uncalibrated images. In: International conference on image processing, pp. 2097–2100Google Scholar
  36. 36.
    Mills S (2013) Relative orientation and scale for improved feature matching. In: International conference on image processing, pp 3484–3488Google Scholar
  37. 37.
    Ni K, Jin H, Dellaert F (2009) GroupSAC: efficient consensus in the presence of groupings. In: International conference on computer vision, pp 2193–2200Google Scholar
  38. 38.
    Hempstalk K, Frank E, Witten IH (2008) One-class classification by combining density and class probability estimation. In: Machine learning and knowledge discovery in databases. Springer, Berlin, pp. 505–519Google Scholar
  39. 39.
    Bartkowiak AM (2011) Anomaly, novelty, one-class classification: a comprehensive introduction. Int J Comput Syst Ind Manag Appl 3:061–071CrossRefGoogle Scholar
  40. 40.
    Fathy ME, Hussein AS, Tolba MF (2011) Fundamental matrix estimation: a study of error criteria. Pattern Recognit Lett 32:383–391CrossRefGoogle Scholar
  41. 41.
    Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol TIST 2:1–27CrossRefGoogle Scholar
  42. 42.
    Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Chun-Bao Xiao
    • 1
    • 2
    • 3
  • Da-Zheng Feng
    • 2
  • Ming-Dong Yuan
    • 2
  1. 1.School of Computer Science and TechnologyXidian UniversityXi’anChina
  2. 2.National Laboratory of Radar Signal ProcessingXidian UniversityXi’anChina
  3. 3.Information Engineering CollegeHenan University of Science and TechnologyLuoyangChina

Personalised recommendations