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Contour Matching Using Epipolar Geometry
April 2000 (vol. 22 no. 4)
pp. 358-370

Abstract—Matching features computed in images is an important process in multiview image analysis. When the motion between two images is large, the matching problem becomes very difficult. In this paper, we propose a contour matching algorithm based on geometric constraints. With the assumption that the contours are obtained from images taken from a moving camera with static scenes, we apply the epipolar constraint between two sets of contours and compute the corresponding points on the contours. From the initial epipolar constraints obtained from corner point matching, candidate contours are selected according to the epipolar geometry, contour end point constraints, and contour distance measures. In order to reduce the possibility of false matches, the number of match points on a contour is also used as a selection measure. The initial epipolar constraint is refined from the matched sets of contours. The algorithm can be applied to a pair or two pairs of images. All of the processes are fully automatic and successfully implemented and tested with various real images.

[1] R.I. Hartley, “In Defense of the 8-Point Algorithm,” Proc. Fifth Int'l Conf. Computer Vision, pp. 1,064-1,070, June 1995.
[2] B. Boufama and R. Mohr, “Epipole and Fundamental Matrix Estimation Using the Virtual Parallax Property,” Proc. Fifth Int'l Conf. Computer Vision, pp. 1,030-1,036, June 1995.
[3] O. Faugeras, "What can be seen in three dimensions with an uncalibrated stereo rig?" Second European Conf. Computer Vision, pp. 563-578, 1992.
[4] R. Tsai and T. Huang, “Uniqueness and Estimation of Three-Dimensional Motion Parameters of Rigid Objects with Curved Surface,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, no. 1, pp. 13-26, Jan. 1984.
[5] M.E. Spetsakis and J. Aloimonos, “Structure from Motion Using Line Correspondences,” Int'l J. Computer Vision, vol. 4, pp. 171-183, 1990.
[6] Y. Liu and T. Huang, “A Linear Algorithm for Determining Motion and Structure from Line Correspondences,” Computer Vision, Graphics, and Image Processing, vol. 44, no. 1, pp. 35-57, 1988.
[7] R. Hartley, “Lines and Points in Three Views—An Integrated Approach,” Proc. ARPA Image Understanding Workshop, 1994.
[8] Z. Zhang and O.D. Faugeras, 3D Dynamic Scene Analysis: A Stereo Based Approach. Berlin Heidelberg: Springer, 1992.
[9] Z. Zhang, “Estimating Motion and Structure from Correspondences of Line Segments between Two Perspective Images,” Research Report 2,340, INRIA Sophia, 1994.
[10] B.K. Ray and K.S. Ray, “Corner Detection Using Iterative Gaussian Smoothing with Constant Window Size,” Pattern Recognition, vol. 28, no. 11, pp. 1,765-1,781, 1995.
[11] R. Deriche and G. Giraudon, “Accurate Corner Detection: An Analytical Study,” Proc. IEEE Int'l Conf. Computer Vision, pp. 66-70, 1990.
[12] Z. Zhang, R. Deriche, O. Faugeras, and Q.T. Luong, “A Rubust Technique for Matching Two Uncalibrated Images through the Recovery of the Unknown Epipolar Geometry,” Artificial Intelligence J., vol. 78, pp. 87-119, 1995.
[13] R. Deriche and O. Faugeras, “2D-Curves Matching Using High Curvatures Points: Applications to Stereovision,” Proc. 10th Int'l Conf. Pattern Recognition, vol. 1, pp. 240-242, 1990.
[14] C. Tomasi and T. Kanade, “Shape and Motion from Image Streams: A Factorization Method—3. Detection and Tracking of Point Features,” Technical Report CMU-CS-91-132, Carnegie Mellon Univ, Pittsburgh, Apr. 1991.
[15] G. Xu and Z. Zhang, Epipolar Geometry in Stereo, Motion, and Object Recognition: A Unified Approach. Kluwer Academic Publishers, 1996.
[16] G. Xu, “A Unified Approach to Image Matching and Segmentation in Stereo, Motion, and Object Recognition via Recovery of Epipolar Geometry,” VIDERE: J. Computer Vision Research, vol. 1, no. 1, pp. 21-56, 1996.
[17] R. Close, S. Tamura, and H. Naito, “Estimation of Motion from Sequential Images Using Integral Constraints,” Pattern Recognition, vol. 28, no. 1, pp. 1-9, 1995.
[18] R.N. Strickland and Z. Mao, “Contour Motion Estimation Using Relaxation Matching with a Smoothness Constraint on the Velocity Field,” Computer Vision, Graphics, and Image Processing: Image Understanding, vol. 60, no. 2, pp. 157-167, 1994.
[19] J.S. Park and J.H. Han, “Contour Motion Estimation from Image Sequences Using Curvature Information,” Pattern Recognition, vol. 31, no. 1, pp. 31-39, 1998.
[20] H.C. Longuet-Higgins, “A Computer Algorithm for Reconstructing a Scene from Two Projections,” Nature, pp. 133-135, Sept. 1981.
[21] R.I. Hartley, In Defense of the 8-Point Algorithm IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 6, pp. 580-593, June 1997.

Index Terms:
Contour matching, epipolar geometry, contour motion.
Citation:
Joon Hee Han, Jong Seung Park, "Contour Matching Using Epipolar Geometry," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 4, pp. 358-370, April 2000, doi:10.1109/34.845378
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