Abstract
We focus in this chapter on the problem of adding computer-generated objects in video sequences. A two-stage robust statistical method is used for computing the pose from model-image correspondences of tracked curves. This method is able to give a correct estimate of the pose even when tracking errors occur. However, if we want to add virtual objects in a scene area which does not contain (or contains few) model features, the reprojection error in this area is likely to be large. In order to improve the accuracy of the viewpoint, we use 2D keypoints that can be easily matched in two consecutive images. As the relationship between two matched points is a function of the camera motion, the viewpoint can be improved by minimizing a cost function which encompasses the reprojection error as well as the matching error between two frames. The reliability of the system is shown on the encrustation of a virtual car in a sequence of the Stanislas square.
The interested reader can look at the video sequences of our results1.
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References
M.-O. Berger. How to track efficiently piecewise curved contours with a view to reconstructing 3D objects. In Proceedings of the 12th International Conference on Pattern Recognition, Jerusalem (Israel), volume 1, pages 32–36, 1994.
M.-O. Berger. Resolving occlusion in augmented reality: a contour-based approach without 3D reconstruction. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Puerto Rico, PR (USA), pages 91–96, June 1997.
M.-O. Berger, C. Chevrier, and G. Simon. Compositing computer and video image sequences: Robust algorithms for the reconstruction of the camera parameters. Computer Graphics Forum, Conference Issue Eurographics’96, Poitiers, France, 15(3):23–32, August 1996.
D. Dementhon and L. Davis. Model based object pose in 25 lines of code. International Journal of Computer Vision, 15:123–141, 1995.
G. Ertl, H. Müller-Seelich, and B. Tabatabai. MOVE-X: a system for combining video films and computer animation. In Eurographics, pages 305–313, 1991.
O. Faugeras. Three-Dimensional Computer Vision: A Geometric Viewpoint. Artificial Intelligence. MIT Press, 1993.
O. D. Faugeras and G. Toscani. The Calibration Problem for Stereo. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL (USA), pages 15–20, 1986.
R. M. Haralick, H. Joo, C. N. Lee, X. Zhuang, V.G. Vaidya, and M. B. Kim. Pose estimation from corresponding point data. IEEE Transactions on Systems, Man, and Cybernetics, 19(6), 1989.
C. Harris and M. Stephens. A combined corner and edge detector. In Proceedings of 4th Alvey Conference, Cambridge, August 1988.
R. Kumar and A. Hanson. Robust methods for estimating pose and a sensitivity analysis. CVGIP: Image Understanding, 60(3):313–342, 1994.
Q.-T. Luong, R. Deriche, O. Faugeras, and T. Papadopoulo. On determining the fundamental matrix: Analysis of different methods and experimental results. Technical Report 1894, INRIA, 1993.
W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling. Numerical Recipes in C, The Art of Scientific Computing. Cambridge University Press, 1988.
C. Schmid and R. Mohr. Local grayvalue invariants for image retrieval. IEEE Transactions on RAMI, 19(5):530–535, August 1997.
G. Simon and M.-O. Berger. A two-stage robust statistical method for temporal registration from features of various type. In Proceedings of 6th International Conference on Computer Vision, Bombay (India), pages 261–266, January 1998.
A. State, G. Hirota, D. Chen, W. Garett, and M. Livingston. Superior augmented reality registration by integrating landmark tracking and magnetic tracking. In Computer Graphics (Proceedings Siggraph New Orleans), pages 429–438, 1996.
C. Tomasi and T. Kanade. Shape and motion from image streams under orthography: A factorization method. International Journal of Computer Vision, 9(2):137–154, 1992.
M. Uenohara and T. Kanade. Vision based object registration for real time image overlay. Journal of Computers in Biology and Medicine, 25(2):249–260, 1996.
Z. Zhang, R. Deriche, O. Faugeras, and Q. Luong. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence, 78:87–119, October 1995.
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Simon, G., Lepetit, V., Berger, MO. (2000). Registration Methods for Harmonious Integration of Real World and Computer Generated Objects. In: Leonardis, A., Solina, F., Bajcsy, R. (eds) Confluence of Computer Vision and Computer Graphics. NATO Science Series, vol 84. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-4321-9_16
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DOI: https://doi.org/10.1007/978-94-011-4321-9_16
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