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An efficient fundamental matrix estimation method for wide baseline images

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Abstract

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.

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References

  1. Hartley R, Zisserman A (2003) Multiple view geometry in computer vision, 2nd edn. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  2. Armangué X, Salvi J (2003) Overall view regarding fundamental matrix estimation. Image Vis Comput 21:205–220

    Article  Google Scholar 

  3. Zia MZ, Stark M, Schiele B, Schindler K (2013) Detailed 3d representations for object recognition and modelling. IEEE Trans Pattern Anal Mach Intell 35:2608–2623

    Article  Google Scholar 

  4. Naikal N, Yang AY, Sastry SS (2011) Informative feature selection for object recognition via sparse PCA. In: International conference on computer vision, pp 818–825

  5. Wu B, Zhang Y, Zhu Q (2011) A triangulation-based hierarchical image matching method for wide-baseline images. Photogramm Eng Remote Sens 77:695–708

    Article  Google Scholar 

  6. Marcon M, Frigerio E, Sarti A, Tubaro S (2012) 3D wide baseline correspondences using depth-maps. Signal Process Image Commun 27:849–855

    Article  Google Scholar 

  7. Pizarro D, Bartoli A (2012) Feature-based deformable surface detection with self-occlusion reasoning. Int J Comput Vis 97:54–70

    Article  MATH  Google Scholar 

  8. Tola E, Lepetit V, Fua P (2010) Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans Pattern Anal Mach Intell 32:815–830

    Article  Google Scholar 

  9. Kim D, Paik J (2010) Gait recognition using active shape model and motion prediction. IET Comput Vis 4:25–36

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-up robust features (SURF). Comput Vis Image Underst 110:346–359

    Article  Google Scholar 

  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 32:2015–2024

    Article  Google Scholar 

  13. Yang W, Sun C, Zhang L (2011) A multi-manifold discriminant analysis method for image feature extraction. Pattern Recognit 44:1649–1657

    Article  MATH  Google Scholar 

  14. Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP 1:331–340

    Google Scholar 

  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 2762–2769

  16. Miksik O, Mikolajczyk K (2012) Evaluation of local detectors and descriptors for fast feature matching. In: International conference on pattern recognition, pp 2681–2684

  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 13:1359–1368

    Article  Google Scholar 

  18. Fischler M, Bolles R (1981) Random sample consensus: a paradigm for model fitting with application to image analysis and automated cartography. Commun ACM 24:381–395

    Article  MathSciNet  Google Scholar 

  19. Torr PH, Murray DW (1997) The development and comparison of robust methods for estimating the fundamental matrix. Int J Comput Vis 24:271–300

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  Google Scholar 

  22. Chum O, Matas J, Kittler J (2003) Locally optimized RANSAC. In: Michaelis B, Krell G (eds) Pattern recognition. Springer, Berlin, pp 236–243

  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 24–29

  24. Tordoff BJ, Murray DW (2005) Guided-MLESAC: faster image transform estimation by using matching priors. IEEE Trans Pattern Anal Mach Intell 27:1523–1535

    Article  Google Scholar 

  25. Chum O, Matas J (2005) Matching with PROSAC-progressive sample consensus. In: IEEE conference on CVPR, vol 1, pp. 220–226

  26. Xu M, Lu J (2012) Distributed RANSAC for the robust estimation of three-dimensional reconstruction. IET Comput Vis 6:324–333

    Article  MathSciNet  Google Scholar 

  27. Adam A, Rivlin E, Shimshoni I (2001) ROR: rejection of outliers by rotations. IEEE Trans Pattern Anal Mach Intell 23:78–84

    Article  Google Scholar 

  28. Zhang D, Wang Y, Tao W (2012) Epipolar geometry estimation for wide baseline stereo. Int J Precis Eng Manuf 2:38–45

    Google Scholar 

  29. Zhang K, Li X, Zhang J (2014) A robust point-matching algorithm for remote sensing image registration. Geosci Remote Sens Lett 11:469–473

    Article  Google Scholar 

  30. Zhou HB, Zhang D, Chen C et al (2011) Discarding wide baseline mismatches with global and local transformation consistency. Electron Lett 47:25–26

    Article  Google Scholar 

  31. Choi S, Kim T, Yu W (2009) Performance evaluation of RANSAC family. In: Proceedings of the British machine vision conference (BMVC), pp 1–12

  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 57:201–218

    Article  Google Scholar 

  33. Schölkopf B, Platt JC, Shawe-Taylor J et al (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13:1443–1471

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  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. 2097–2100

  36. Mills S (2013) Relative orientation and scale for improved feature matching. In: International conference on image processing, pp 3484–3488

  37. Ni K, Jin H, Dellaert F (2009) GroupSAC: efficient consensus in the presence of groupings. In: International conference on computer vision, pp 2193–2200

  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. 505–519

  39. Bartkowiak AM (2011) Anomaly, novelty, one-class classification: a comprehensive introduction. Int J Comput Syst Ind Manag Appl 3:061–071

    Article  Google Scholar 

  40. Fathy ME, Hussein AS, Tolba MF (2011) Fundamental matrix estimation: a study of error criteria. Pattern Recognit Lett 32:383–391

    Article  Google Scholar 

  41. Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol TIST 2:1–27

    Article  Google Scholar 

  42. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, Shaanxi, China

    Chun-Bao Xiao

  2. National Laboratory of Radar Signal Processing, Xidian University, Xi’an, 710071, Shaanxi, China

    Chun-Bao Xiao, Da-Zheng Feng & Ming-Dong Yuan

  3. Information Engineering College, Henan University of Science and Technology, Luoyang, 471023, Henan, China

    Chun-Bao Xiao

Authors
  1. Chun-Bao Xiao
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  2. Da-Zheng Feng
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  3. Ming-Dong Yuan
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Correspondence to Da-Zheng Feng.

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Xiao, CB., Feng, DZ. & Yuan, MD. An efficient fundamental matrix estimation method for wide baseline images. Pattern Anal Applic 21, 35–44 (2018). https://doi.org/10.1007/s10044-016-0561-z

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  • DOI: https://doi.org/10.1007/s10044-016-0561-z

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