On the Determination of Epipoles Using Cross-Ratios

We study the problem of computing the position of the epipoles in a pair of uncalibrated images. The approach, which is based on the invariance of the cross-ratio by theepipolar transformation, exploits algebraic constraints obtained from point correspondences and provides a solution in which only the epipoles are involved. This is in opposition to the methods based on the computation of the fundamental matrix. These notions are first presented as well as the newepipolar ordering constraint. Three families of methods are successively considered: the first uses statistics on closed-form solutions provided by the so-calledSturm method, the second uses intersect plane cubics through deterministic procedures, and the third is based on nonlinear minimizations of a difference of cross-ratios. We discuss the shortcomings of each and show, using numerous experimental comparisons, that there is a trade-off between elegance and robustness to noise. The cross-ratio based methods do not turn out to be a generally viable alternative to the method based on the fundamental matrix.

[1]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[2]  S. Maybank Properties of essential matrices , 1990, Int. J. Imaging Syst. Technol..

[3]  O. Faugeras,et al.  Motion from point matches: Multiplicity of solutions , 1989, [1989] Proceedings. Workshop on Visual Motion.

[4]  Rachid Deriche,et al.  Robotique, Image Et Vision on Determining the Fundamental Matrix: Analysis of Different Methods and Experimental Results on Determining the Fundamental Matrix: Analysis of Diierent Methods and Experimental Results Programme 4: Robotique, Image Et Vision , 1993 .

[5]  R. Hartley Cheirality Invariants , 1993 .

[6]  Richard I. Hartley,et al.  Estimation of Relative Camera Positions for Uncalibrated Cameras , 1992, ECCV.

[7]  Søren I. Olsen,et al.  Epipolar Line Estimation , 1992, ECCV.

[8]  Quang-Tuan Luong Matrice fondamentale et autocalibration en vision par ordinateur , 1992 .

[9]  Andrew Zisserman,et al.  Geometric invariance in computer vision , 1992 .

[10]  Thomas S. Huang,et al.  Theory of Reconstruction from Image Motion , 1992 .

[11]  Olivier D. Faugeras,et al.  Some Properties of the E Matrix in Two-View Motion Estimation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Philip H. S. Torr,et al.  Outlier detection and motion segmentation , 1993, Other Conferences.

[13]  Thierry Viéville,et al.  Canonical Representations for the Geometries of Multiple Projective Views , 1996, Comput. Vis. Image Underst..

[14]  O. Hesse Die cubische Gleichung, von welcher die Lösung des Problems der Homographie von M. Chasles abhängt. , .

[15]  O. D. Faugeras,et al.  Camera Self-Calibration: Theory and Experiments , 1992, ECCV.

[16]  Olivier D. Faugeras,et al.  Relative 3D positioning and 3D convex hull computation from a weakly calibrated stereo pair , 1993, 1993 (4th) International Conference on Computer Vision.

[17]  Olivier D. Faugeras,et al.  A Stability Analysis of the Fundamental Matrix , 1994, ECCV.

[18]  Richard I. Hartley,et al.  In defence of the 8-point algorithm , 1995, Proceedings of IEEE International Conference on Computer Vision.

[19]  Long Quan,et al.  Invariants of 6 Points from 3 Uncalibrated Images , 1994, ECCV.

[20]  H. C. Longuet-Higgins,et al.  A computer algorithm for reconstructing a scene from two projections , 1981, Nature.

[21]  Rud Sturm,et al.  Das Problem der Projectivität und seine Anwendung auf die Flächen zweiten Grades , 1869 .