Skip to main content
Log in

SC-RANSAC: Spatial consistency on RANSAC

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The goal of robust parameter estimation is developing a model which can properly fit to data. Parameter estimation of a geometric model, in presence of noise and error, is an important step in many image processing and computer vision applications. As the random sample consensus (RANSAC) algorithm is one of the most well-known algorithms in this field, there have been several attempts to improve its performance. In this paper, after giving a short review on existing methods, a robust and efficient method that detects the gross outliers to increase the inlier to outlier ratio in a reduced set of corresponding image points is proposed. It has a new hypothesis and verification scheme which utilizes spatial relations between extracted corresponding points in two images. It can also be considered as a preprocessing step for RANSAC to improve the accuracy as well as the runtime of RANSAC in estimating the parameters of a geometric model (such as fundamental and homography matrices). Obviously, like almost all previous works for enhancing RANSAC’s runtime, the proposed method does not use heavy and compilicated processes. Performance analysis is performed on a variety of standard challenging datasets for estimating the homography and fundamental matrix (as an applicable case used in the literature, especially in the state-of-the-art methods). The performance is also compared quantitatively to RANSAC, PROSAC, and SCRAMSAC robust estimators to demonstrate its superiority. Experimental results show that the proposed method removes about 50% of outliers in most cases and hence extremely reduces the required runtime of RANSAC, while improving its accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. http://www.robots.ox.ac.uk/~vgg/research/affine/

  2. The code was kindly provided by David Lowe, UBC, Vancouver, Canada. http://www.cs.ubc.ca/~lowe/keypoints/

References

  1. Barclay A, Kaufmann H (2014) Ft-ransac: Towards robust multi-modal homography estimation. In: 2014 8th IAPR workshop on Pattern recognition in remote sensing (PRRS). IEEE, pp 1–4

  2. Bellavia F http://www.math.unipa.it/fbellavia/

  3. Bellavia F, Tegolo D (2011) Noransac for fundamental matrix estimation. In: Proceedings of the British Machine Vision Conference, pp 98–1

  4. Botterill T, Mills S, Green RD (2009) New conditional sampling strategies for speeded-up ransac. In: BMVC, pp 1–11

  5. Capel DP (2005) An effective bail-out test for ransac consensus scoring. In: BMVC

  6. Chin T-J, Wang H, Suter D (2009) Robust fitting of multiple structures: The statistical learning approach. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 413–420

  7. Chin T-J, Yu J, Suter D (2010) Accelerated hypothesis generation for multi-structure robust fitting. In: Daniilidis K, Maragos P, Paragios N (eds) Computer vision – ECCV 2010. ECCV 2010. Lecture notes in computer science, vol 6315. Springer, Berlin, Heidelberg

    Chapter  Google Scholar 

  8. Chin T-J, Yu J, Suter D (2012) Accelerated hypothesis generation for multistructure data via preference analysis. IEEE Trans Pattern Anal Mach Intell 34 (4):625–638

    Article  Google Scholar 

  9. Choi J, Medioni G (2009) Starsac: Stable random sample consensus for parameter estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE, pp 675–682

  10. Choi S, Kim T, Yu W (1997) Performance evaluation of ransac family. J Comput Vision 24(3):271– 300

    Article  Google Scholar 

  11. Chum O, Matas J (2002) Randomized ransac with td, d test. In: Proceedings of the British machine vision conference, vol 2, pp 448–457

  12. Chum O, Matas J (2005) Matching with prosac ” progressive sample consensus. In: Proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05) - Volume 1 - Volume 01, ser. CVPR ’05. IEEE Computer Society, Washington, pp 220–226. https://doi.org/10.1109/CVPR.2005.221

  13. Chum O, Matas J, Kittler J (2003) Locally optimized ransac. In: Joint pattern recognition symposium. Springer, pp 236–243

  14. Chum O, Werner T, Matas J (2005) Two-view geometry estimation unaffected by a dominant plane. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1. IEEE, pp 772–779

  15. Ciobanu L, Côrte-Real L (2011) Iterative filtering of sift keypoint matches for multi-view registration in distributed video coding. Multimedia Tools Appl 55 (3):557–578

    Article  Google Scholar 

  16. Frahm J-M, Pollefeys M (2006) Ransac for (quasi-) degenerate data (qdegsac). In: 2006 IEEE computer society conference on computer vision and pattern recognition, vol 1. IEEE, pp 453–460

  17. Kang Z, Zhang L, Wang B, Li Z, Jia F (2014) An optimized baysac algorithm for efficient fitting of primitives in point clouds. IEEE Geosci Remote Sens Lett 11(6):1096–1100

    Article  Google Scholar 

  18. Konouchine A, Gaganov V, Veznevets V (2005) Amlesac: A new maximum likelihood robust estimator. In: Proceedings of the international conference on computer graphics and vision (GrapiCon)

  19. Kovesi P http://www.peterkovesi.com/matlabfns/index.html

  20. Lebeda K, Matas J, Chum O (2012) Fixing the locally optimized ransac–full experimental evaluation. In: British machine vision conference, pp 1–11

  21. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  22. Matas J, Chum O (2005) Randomized ransac with sequential probability ratio test. In: 10th IEEE international conference on computer vision, 2005. ICCV 2005, vol 2. IEEE, pp 1727–1732

  23. Meler A, Decrouez M, Crowley JL (2010) Betasac: A new conditional sampling for ransac. In: British machine vision conference 2010

  24. Mittal S, Anand S, Meer P (2011) Generalized projection based m-estimator: Theory and applications. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2689– 2696

  25. Monji-Azad S, Kasaei S, Eftekhari-Moghadam A-M (2014) An efficient augmented reality method for sports scene visualization from single moving camera. In: 2014 22nd Iranian conference on electrical engineering (ICEE). IEEE, pp 1064–1069

  26. Nasuto D, Craddock JBR Napsac: High noise, high dimensional robust estimation-it’s in the bag

  27. Ni K, Jin H, Dellaert F (2009) Groupsac: Efficient consensus in the presence of groupings. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 2193–2200

  28. Otte S, Schwanecke U, Zell A (2014) Antsac: A generic ransac variant using principles of ant colony algorithms. In: 2014 22nd international conference on pattern recognition (ICPR). IEEE, pp 3558–3563

  29. Raguram R, Frahm J-M, Pollefeys M (2008) A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus. Computer Vision–ECCV 2008:500–513

    Google Scholar 

  30. Raguram R, Chum O, Pollefeys M, Matas J, Frahm J-M (2013) Usac: a universal framework for random sample consensus. IEEE Trans Pattern Anal Machine Intell 35(8):2022–2038

    Article  Google Scholar 

  31. Rodehorst V, Hellwich O (2006) Genetic algorithm sample consensus (gasac)-a parallel strategy for robust parameter estimation. In: Conference on computer vision and pattern recognition workshop, 2006. CVPRW’06. IEEE, pp 103–103

  32. Sattler T, Leibe B, Kobbelt L (2009) Scramsac: Improving ransac’s efficiency with a spatial consistency filter. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 2090–2097

  33. Subbarao R, Meer P (2006) Subspace estimation using projection based m-estimators over grassmann manifolds. Computer Vision–ECCV 2006:301–312

    Google Scholar 

  34. Torr PH (2002) Bayesian model estimation and selection for epipolar geometry and generic manifold fitting. Int J Comput Vis 50(1):35–61

    Article  Google Scholar 

  35. Torr PH, Murray DW (1993) Outlier detection and motion segmentation. In: Optical tools for manufacturing and advanced automation. International Society for Optics and Photonics, pp 432–443

  36. Torr PH, Zisserman A (2000) Mlesac: a new robust estimator with application to estimating image geometry. Comput Vis Image Underst 78(1):138–156

    Article  Google Scholar 

  37. Torr PHS, Davidson C (2003) Impsac: Synthesis of importance sampling and random sample consensus. IEEE Trans Pattern Anal Mach Intell 25(3):354–364

    Article  Google Scholar 

  38. Trivedi P, Agarwal T, Muthunagai K (2013) Mc-ransac: A pre-processing model for ransac using monte carlo method implemented on a gpu. In: 2013 international conference on advances in computing communications and informatics (ICACCI). IEEE, pp 1380–1383

  39. Tuytelaars T, Van Gool LG (2000) Wide baseline stereo matching based on local, affinely invariant regions. In: BMVC, vol 412

  40. Wang X, Zhang H, Liu S (2013) Reliable ransac using a novel preprocessing model. In: Computational and mathematical methods in medicine, vol 2013

    Google Scholar 

  41. Yan W, Tian Z, Duan X Canonical correlation analysis applied to remove sift mismatching

  42. Zhang Z (1998) Determining the epipolar geometry and its uncertainty: a review. Int J Comput Vis 27(2):161–195

    Article  Google Scholar 

  43. Zhang Z, Deriche R, Faugeras O, Luong Q-T (1995) A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artif Intell 78(1-2):87–119

    Article  Google Scholar 

  44. Zitnick CL, Ramnath K (2011) Edge foci interest points. In: 2011 IEEE international conference on computer vision (ICCV). IEEE, pp 359–366

Download references

Acknowledgements

The authors would like to thank Mr. Afshin Bozorgpour, and Mrs. Sara Monji-azad for their valuable comments and suggestions to improve the work. Special thanks to Fabio Bellavia for providing us with the codes of PROSAC and SCRAMSAC. This work has been partly supported by a grant from Iran national science foundation (INSF).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shohreh Kasaei.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fotouhi, M., Hekmatian, H., Kashani-Nezhad, M.A. et al. SC-RANSAC: Spatial consistency on RANSAC. Multimed Tools Appl 78, 9429–9461 (2019). https://doi.org/10.1007/s11042-018-6475-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6475-6

Keywords

Navigation