Self-Calibration of a Moving Camera from Point Correspondences and Fundamental Matrices

International Journal of Computer Vision

Volumn 22, Issue 3, 1997, Pages 261-289

  Luong, Q T ^a   Faugeras, O D ^b  

^a SRI INTERNATIONAL   (United States)

^b INRIA   (France)

Author keywords

Camera calibration; Euclidean geometry; Kruppa equations; Projective geometry

Indexed keywords

ALGORITHMS; CALIBRATION; CAMERAS; COMPUTATIONAL GEOMETRY; COMPUTER SIMULATION; IMAGE RECONSTRUCTION; MATRIX ALGEBRA; PARAMETER ESTIMATION; THREE DIMENSIONAL COMPUTER GRAPHICS;

CAMERA CALIBRATION; EUCLIDEAN GEOMETRY; KRUPPA EQUATIONS; POINT CORRESPONDENCES; PROJECTIVE GEOMETRY;

COMPUTER VISION;

EID: 0031099678     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007982716991     Document Type: Article

References (52)

1. Ayache, N. 1990. Stereovision and Sensor Fusion. MIT Press.
2. Basu, A. 1993. Active calibration: Alternative strategy and analysis. In Proc. of the Conf. on Computer Vision and Pattern Recognition, New-York, pp. 495-500.
3. Brand, P., Mohr, R., and Bobet, P. 1993. Distorsions optiques: Correction dans un modele projectif. Technical Report RR-1933, INRIA.
4. Coxeter, H.S.M. 1987. Projective Geometry. Springer Verlag, second edition.
5. Crowley, J., Bobet, P., and Schmid, C. 1993. Maintaining stereo calibration by tracking image points. In Proc. of the Conf. on Computer Vision and Pattern Recognition, New-York, pp. 483-488.
6. Deriche, R. and Blaszka, T. 1993. Recovering and characterizing image features using an efficient model based approach. In Proc. International Conference on Computer Vision and Pattern Recognition.
7. Deriche, R., Vaillant, R., and Faugeras, O. 1992. From Noisy Edges Points to 3D Reconstruction of a Scene: A Robust Approach and Its Uncertainty Analysis, Vol. 2, pp. 71-79. World Scientific. Series in Machine Perception and Artificial Intelligence.
8. Dron, L. 1993. Dynamic camera self-calibration from controled motion sequences. In Proc. of the conf. on Computer Vision and Pattern Recognition, New-York, pp. 501-506.
9. Du, F. and Brady, M. 1993. Self-calibration of the intrinsic parameters of cameras for active vision systems. In Proc. of the conf. on Computer Vision and Pattern Recognition, New-York, pp. 477-482.
10. Fang, J.Q. and Huang, T.S. 1984. Some experiments on estimating the 3D motion parameters of a rigid body from two consecutive image frames. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:545-554.
11. Faugeras, O.D. 1993. Three-Dimensional Computer Vision: A Geometric Viewpoint. MIT Press.
12. Faugeras, O.D. and Toscani, G. 1986. The calibration problem for stereo. In Proceedings of CVPR'86, pp. 15-20.
13. Faugeras, O.D., Lustman, F., and Toscani, G. 1987. Motion and Structure from point and line matches. In Proc. International Conference on Computer Vision, pp. 25-34.
14. Faugeras, O.D. and Maybank, S.J. 1990. Motion from point matches: Multiplicity of solutions. The International Journal of Computer Vision, 4(3):225-246, also INRIA Tech. Report 1157.
15. Faugeras, O.D., Luong, Q.-T., and Maybank, S.J. 1992. Camera self-calibration: Theory and experiments. In Proc. European Conference on Computer Vision, Santa-Margerita, Italy, pp. 321-334.
16. Garner, L.E. 1981. An Outline of Projective Geometry. Elsevier: North Holland.
17. Golub, G.H. and Van Loan, C.F. 1989. Matrix Computations. The John Hopkins University Press.
18. Hartley, R.I. 1992. Estimation of relative camera positions for uncalibrated cameras. In Proc. European Conference on Computer Vision, pp. 579-587.
19. Hartley, R.I. 1994a. An algorithm for self calibration from several views. In Proc. Conference on Computer Vision and Pattern Recognition, Seattle, WA, pp. 908-912.
20. Hartley, R.I. 1994b. Self-calibration from multiple views with a rotating camera. In Proc. European Conference on Computer Vision, Stockholm, Sweden, pp. 471-478.
21. Horn, B.K.P. 1990. Relative orientation. The International Journal of Computer Vision, 4(1):59-78.
22. Huang, T.S. and Faugeras, O.D. 1989. Some properties of the E-matrix in two view motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11:1310-1312.
23. Jazwinsky, A.M. 1970. Stochastic Processes and Filtering Theory. Academic Press: London.
24. Kanatani, K. 1991. Computational projective geometry. Computer Vision, Graphics, and Image Processing. Image Understanding, 54(3).
25. Kanatani, K. 1992. Geometric Computation for Machine Vision. Oxford university press.
26. Kruppa, E. 1913. Zur Ermittlung eines Objektes aus zwei Perspektiven mit innerer Orientierung. Sitz.-Ber. Akad. Wiss., Wien, math. naturw. Kl., Abt. IIa., 122:1939-1948.
27. Kumar, R. and Hanson, A. 1990. Sensibility of the pose refinement problem to accurate estimation of camera parameters. In Proceedings of the International Conference on Computer Vision, Osaka, Japan, pp. 365-369.
28. Kumar, R.V.R., Tirumalai, A., and Jain, R.C. 1989. A non-linear optimization algorithm for the estimation of structure and motion parameters. In Proc. International Conference on Computer Vision and Pattern Recognition, pp. 136-143.
29. Longuet-Higgins, H.C. 1981. A computer algorithm for reconstructing a scene from two projections. Nature, 293:133-135.
30. Luong, Q.-T. 1992. Matrice fondamentale et auto-calibration en vision par ordinateur. Ph.D. thesis, Universite de Paris-Sud, Orsay.
31. Luong, Q.-T. and Faugeras, O.D. 1992. Self-calibration of a camera using multiples images. In Proc. International Conference on Pattern Recognition, Den Hag, The Netherlands, pp. 9-12.
32. Luong, Q.-T. and Viéville, T. 1996. Canonic representations for the geometries of multiple projective views. Computer Vision and Image Understanding, 64(2): 193-229.
33. Luong, Q.-T. and Faugeras, O.D. 1994. An optimization framework for efficient self-calibration and motion determination. In Proc. International Conference on Pattern Recognition, Jerusalem, Israel, pp. A-248-A-252.
34. Luong, Q.-T. and Faugeras, O.D. 1994. A stability analysis of the fundamental matrix. In Proc. European Conference on Computer Vision, Stockholm, Sweden, pp. 577-588.
35. Luong, Q.-T. and Faugeras, O.D. 1996. The fundamental matrix: Theory, algorithms, and stability analysis. Intl. Journal of Computer Vision 7(1):43-76.
36. Luong, Q.-T., Deriche, R., Faugeras, O.D., and Papadopoulo, T. 1993. On determining the fundamental matrix: Analysis of different methods and experimental results. Technical Report RR-1894, INRIA.
37. Maybank, S.J. 1990. The projective geometry of ambiguous surfaces. Proc. of the Royal Society London A, 332:1-47.
38. Maybank, S.J. and Faugeras, O.D. 1992. A theory of self-calibration of a moving camera. The International Journal of Computer Vision, 8(2):123-151.
39. Maybeck, P.S. 1979. Stochastic Models, Estimation and Control. Academic Press: London.
40. Mundy, J.L. and Zisserman, A. (Eds.) 1992. Geometric Invariance in Computer Vision. MIT Press.
41. Robert, L. 1993. Reconstruction de courbes et de surfaces par vision stéréoscopique. Applications a la robotique mobile. Ph.D. thesis, Ecole Polytechnique.
42. Semple, J.G. and Kneebone, G.T. 1979. Algebraic Projective Geometry. Clarendon Press: Oxford, 1952 (Reprinted).
43. Spetsakis, M.E. and Aloimonos, J. 1988. Optimal computing of structure from motion using point correspondances in two frames. In Proc. International Conference on Computer Vision, pp. 449-453.
44. Trivedi, H.P. 1988. Can multiple views make up for lack of camera registration? Image and Vision Computing, 6(1):29-32.
45. Tsai, R.Y. 1989. Synopsis of Recent Progress on Camera Calibration for 3D Machine Vision. In Oussama Khatib, John J. Craig, and Tomás Lozano-Pérez (Eds.), The Robotics Review. MIT Press: pp. 147-159.
46. Tsai, R.Y. and Huang, T.S. 1984. Uniqueness and estimation of three-dimensional motion parameters of rigid objects wirth curved surfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:13-27.
47. Ullman, S. 1979. The Interpretation of Visual Motion. MIT Press.
48. Vieville, T. 1994. Auto-calibration of visual sensor parameters on a robotic head. Image and Vision Computing, 12.
49. Wampler, C.W., Morgan, A.P., and Sommese, A.J. 1988. Numerical continuation methods for solving polynomial systems arising in kinematics. Technical Report GMR-6372, General Motors Research Labs.
50. Weng, J., Ahuja, N., and Huang, T.S. 1989. Optimal motion and structure estimation. In Proc. International Conference on Computer Vision and Pattern Recognition, pp. 144-152.
51. Zhang, Z. and Faugeras, O.D. 1992. 3D Dynamic Scene Analysis. Springer-Verlag.
52. Zhang, Z., Deriche, R., Faugeras, O., and Luong, Q.-T. 1995. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence Journal 78:87-119.