Camera Motion Estimation Through Planar Deformation Determination
Authors: 
C. Jonchery, F. Dibos, G. KoepflerAuthors Info & Claims
C. Jonchery, F. Dibos, G. KoepflerAuthors Info & ClaimsPages 73 - 87
Abstract
In this paper, we propose a global method for estimating the motion of a camera which films a static scene. Our approach is direct, fast and robust, and deals with adjacent frames of a sequence. It is based on a quadratic approximation of the deformation between two images, in the case of a scene with constant depth in the camera coordinate system. This condition is very restrictive but we show that, provided translation and depth inverse variations are small enough, the error on optical flow involved by the approximation of depths by a constant is small. In this context, we propose a new model of camera motion which allows to separate the image deformation in a similarity and a "purely" projective application, due to change of optical axis direction. This model leads to a quadratic approximation of image deformation that we estimate with an M-estimator; we can immediately deduce camera motion parameters.
References
[1]
Azarbayejani, A., Pentland, A.P.: Recursive estimation of motion, structure and focal length. IEEE Trans. Pattern Anal. Mach. Intell. 17(6), 562-575 (1995).
[2]
Yao, A., Calway, A.: Robust estimation of 3-d camera motion for uncalibrated augmented reality. Dept. of Computer Science, University of Bristol, CSTR-02-001 (2002).
[3]
Longuet-Higgins, H.C.: A computer algorithm for reconstructing a scene from two projections. Nature 293(10), 133-135 (1981).
[4]
Faugeras, O.: Three-Dimensional Computer Vision, a Geometric Viewpoint. MIT Press, Cumberland (1993).
[5]
Faugeras, O.,Maybank, S.: Motion from point matches: multiplicity of solutions. Int. J. Comput. Vis. 4(3), 225-246 (1990).
[6]
Huang, T., Faugeras, O.: Some properties of the Ematrix in two-view motion estimation. IEEE Trans. Pattern Anal. Mach. Intell. 11(12), 1310-1312 (1989).
[7]
Faugeras, O., Luong, Q.T., Papadopoulo, T.: The Geometry of Multiple Images. MIT Press, Cumberland (2000).
[8]
Bruss, A.R., Horn, B.K.: Passive navigation. Comput. Graph. Image Process. 21, 3-20 (1983).
[9]
Heeger, D., Jepson, A.: Subspace methods for recovering rigid motion I: algorithm and implementation. Int. J. Comput. Vis. 7(2), 95-117 (1992).
[10]
Ma, Y., Koseckà, J., Sastry, S.: Linear differential algorithm for motion recovery: a geometric approach. Int. J. Comput. Vis. 36(1), 71-89 (2000).
[11]
Brooks, M.J., Chojnacki, W., Baumela, L.: Determining the egomotion of an uncalibrated camera from instantaneous optical flow. J. Opt. Soc. Am. A 14(10), 2670-2677 (1997).
[12]
Tomasi, C., Shi, J.: Direction of heading from image deformations. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 422-427 (1993).
[13]
Lawn, J., Cipolla, R.: Robust egomotion estimation from affine motion parallax. In: Proc. 3rd European Conf on Computer Vision, pp. 205-210. Stockholm, Sweden (1994).
[14]
Tian, T.Y., Tomasi, C., Heeger, D.J.: Comparison of approaches to egomotion computation. In: Proc. of Conf. on Computer Vision and Pattern Recognition, pp. 315-320 (1996).
[15]
Irani, M., Rousso, B., Peleg, S.: Recovery of ego-motion using region alignement. IEEE Trans. Pattern Anal. Mach. Intell. 19(3), 268-272 (1997).
[16]
Horn, B.K., Weldon, E.J.: Direct methods for recovering motion. Int. J. Comput. Vis. 2, 51-76 (1988).
[17]
Bergen, J.R., Anandan, P., Hanna, K.J., Hingorani, R.: Hierarchical model-based motion estimation. In: Proc. of European Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 237-252 (1992).
[18]
Negahdaripour, S., Horn, B.K.P.: Direct passive navigation. IEEE Trans. Pattern Anal. Mach. Intell. 9(1), 168-176 (1987).
[19]
Dibos, F., Koepfler, G., Monasse, P.: Image Alignment. Geometric Level Set Methods in Imaging, Vision and Graphics. Springer, Berlin (2003).
[20]
Odobez, J.M., Bouthemy, P.: Robust Multiresolution Estimation of Parametric Motion Models. J. Vis. Commun. Image Represent. 6(4), 348-365 (1995).
Index Terms
- Camera Motion Estimation Through Planar Deformation Determination
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Copyright © Copyright © 2008 Springer Science+Business Media, LLC.
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Kluwer Academic Publishers
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Published: 01 September 2008
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