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Efficient image features selection and weighting for fundamental matrix estimation

Efficient image features selection and weighting for fundamental matrix estimation

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In computer vision, it is a challenge to compute the relationship of multiple views from scene images. The view relationship can be obtained from the fundamental matrix. Thus, it is very important to compute an accurate fundamental matrix from unevenly distributed features in complex scene images. This study proposes a robust method to estimate the fundamental matrix from corresponding images. First, the authors introduce how to find matched features from scene images efficiently. The epipolar geometry can restrict the point correspondences to the polar line, but cannot cope with the false points lying on the line. To eliminate such mismatches, the authors present an affine constraint which can also merge the uniform regions produced by mean-shift segmentation. Second, inspired by the success of random sample consensus, the authors moderately improve the weighting function based on M-estimator to increase the accuracy of the fundamental matrix estimation. Experimental results on simulated data and real images show these works are efficient for estimating fundamental matrix. The authors also evaluated the accuracy of their method on computing the external parameters of two cameras. The result shows that this method obtains comparable performance to the more sophisticated calibration method.

References

    1. 1)
      • 1. Carrera, G., Angeli, A., Davison, A.J.: ‘SLAM-based automatic extrinsic calibration of a multi-camera rig’. Proc. of the IEEE Int. Conf. on Robotics and Automation, Shanghai, China, May 2011, pp. 26522659.
    2. 2)
    3. 3)
      • 3. Warren, M., McKinnon, D., Upcroft, B.: ‘Online calibration of stereo rigs for long-term autonomy’. Proc. of the IEEE Int. Conf. on Robotics and Automation, Karlsruhe, Germany, May 2013, pp. 36923698.
    4. 4)
    5. 5)
      • 5. Hartley, R.I., Zisserman, A.: ‘Multiple view geometry in computer vision’ (Cambridge University Press, 2004, 2nd edn.).
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • 11. Bugarin, F., Bartoli, A., Henrion, D., et al: ‘Rank-constrained fundamental matrix estimation by polynomial global optimization versus the eight-point algorithm’ (HAL:hal-00723015, 2012).
    12. 12)
    13. 13)
    14. 14)
      • 14. Torr, P., Fitzgibbon, A.: ‘Invariant fitting of two view geometry or ‘in defiance of the 8 point algorithm’’. Proc. of the British Machine Vision Conf., Norwich, UK, September 2002, pp. 8392.
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
    22. 22)
      • 22. Kanatani, K., Sugaya, Y.: ‘High accuracy computation of rank-constrained fundamental matrix’. Proc. of the British Machine Vision Conf., Coventry, UK, 2007, pp. 282291.
    23. 23)
      • 23. Migita, T., Shakunaga, T.: ‘Evaluation of epipole estimation methods with/without rank-2 constraint across algebraic/geometric error functions’. Proc. of the IEEE Conf. Computer Vision and Pattern Recognition, Minneapolis, MN, June 2007, pp. 17.
    24. 24)
    25. 25)
      • 25. Faugeras, O., Luong, Q.: ‘The geometry of multiple images’ (MIT Press, 2001).
    26. 26)
      • 26. Huang, J., Lai, S., Cheng, C.: ‘Robust fundamental matrix estimation with accurate outlier detection’, J. Inf. Sci. Eng., 2007, 23, (4), pp. 12131225.
    27. 27)
    28. 28)
    29. 29)
    30. 30)
    31. 31)
    32. 32)
    33. 33)
    34. 34)
      • 34. Bleyer, M., Rother, C., Kohli, P., et al: ‘Object stereo– joint stereo matching and object segmentation’. Proc. of the IEEE Int. Conf. on Computer Vision and Pattern Recognition, Providence, RI, June 2011, pp. 30813088.
    35. 35)
    36. 36)
      • 36. Bleyer, M., Rother, C., Kohli, P.: ‘Surface stereo with soft segmentation’. Proc. of the IEEE Int. Conf. on Computer Vision and Pattern Recognition, San Francisco, CA, June 2010, pp. 15701577.
    37. 37)
      • 37. Vogel, C., Schindler, K., Roth, S.: ‘Piecewise rigid scene flow’. Proc. of the IEEE Int. Conf. on Computer Vision, Sydney, NSW, December 2013, pp. 13771384.
    38. 38)
      • 38. Zhang, Z.: ‘Flexible camera calibration by viewing a plane from unknown orientations’. Proc. of the IEEE Int. Conf. on Computer Vision, Kerkyra, Greece, September 1999, pp. 666673.
    39. 39)
      • 39. Harris, C., Stephens, M.: ‘A combined corner and edge detector’. Proc. of the Alvey Vision Conf., 1988, pp. 147151.
    40. 40)
    41. 41)
    42. 42)
    43. 43)
      • 43. Beis, J., Lowe, D.: ‘Shape indexing using approximate nearest-neighbor search in high-dimensional spaces’. Proc. of the IEEE Conf. Computer Vision and Pattern Recognition, Washington, DC, 1997, p. 1000.
    44. 44)
    45. 45)
      • 45. Ren, X., Malik, J.: ‘Learning a classification model for segmentation’. Proc. of the IEEE Int. Conf. on Computer Vision, Nice, France, October 2003, pp. 1017.
    46. 46)
      • 46. Sumengen, B.: ‘Variational image segmentation and curve evolution on natural images’. PhD thesis, University of California, 2004.
    47. 47)
    48. 48)
      • 48. Zheng, Y., Sugimoto, S., Okutomi, M.: ‘A branch and contract algorithm for globally optimal fundamental matrix estimation’. Proc. of the IEEE Conf. Computer Vision and Pattern Recognition, Providence, RI, June 2011, pp. 29532960.
    49. 49)
      • 49. Sonka, M., Hlavac, V., Boyle, R.: ‘Image processing, analysis and machine vision’ (Thomson Press, 2002, 2nd edn.).
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