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A theory of the motion fields of curves

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Abstract

This article reports a study of the motion field generated by moving 3-D curves that are observed by a camera. We first discuss the relationship between optical flow and motion field and show that the assumptions made in the computation of the optical flow are a bit difficult to defend.

We then go ahead to study the motion field of a general curve. We first study the general case of a curve moving nonrigidly and introduce the notion of isometric motion. In order to do this, we introduce the notion of spatiotemporal surface and study its differential properties up to the second order. We show that, contrary to what is commonly believed, the full motion field of the curve (i.e., the component tangent to the curve) cannot be recovered from this surface. We also give the equations that characterize the spatio-temporal surface completely up to a rigid transformation. Those equations are the expressions of the first and second fundamental forms and the Gauss and Codazzi-Mainardi equations. We then relate those differential expressions computed on the spatio-temporal surface to quantities that can be computed from the images intensities. The actual values depend upon the choice of the edge detector.

We then show that the hypothesis of a rigid 3-D motion allows in general to recover the structure and the motion of the curve, in fact without explicitly computing the tangential motion field, at the cost of introducing the three-dimensional accelerations. We first study the motion field generated by the simplest kind of rigid 3-D curves, namely lines. This study is illuminating in that it paves the way for the study of general rigid curves and because of the useful results which are obtained. We then extend the results obtained in the case of lines to the case of general curves and show that at each point of the image curve two equations can be written relating the kinematic screw of the moving 3-D curve and its time derivative to quantities defined in the study of the general nonrigid motion that can be measured from the spatio-temporal surface and therefore from the image. This shows that the structure and the motion of the curve can be recovered from six image points only, without establishing any point correspondences.

Finally we study the cooperation between motion and stereo in the framework of this theory. The use of two cameras instead of one allows us to get rid of the three-dimensional accelerations and the relations between the two spatio-temporal surfaces of the same rigidly moving 3-D curve can be used to help disambiguate stereo correspondences.

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References

  • Ayache, N., and Faugeras, O.D., 1989. Maintaining representations of the environment of a mobile robot,IEEE Trans. Robot. Autom., 5(6): 804–819; also INRIA report 789.

    Google Scholar 

  • Ayache, N., and Lustman, F., 1987. Fast and reliable passive trinocular stereovision,Proc. 1st Intern. Conf. Comput. Vis., June, London, pp. 422–427.

  • Baker, H.H., and Bolles, R.C., 1989. Generalizing epipolar-plane image analysis on the spatiotemporal surface,Intern. J. Comput. Vis. 3(1): 33–49.

    Google Scholar 

  • Bergholm, F., 1989. On the content of information in edges and optical flow, Ph.D. thesis, Royal Institute of Technology, Stockholm, May. Department of Numerical Analysis and Computing Science.

  • Bouthemy, P., 1986. A method of integrating motion information along contours including segmentation,Proc. 8th Intern. Conf. Patt. Recog., Paris, pp. 651–653. IEEE Computer Society Press, October.

  • Bouthemy, P., 1989. A maximum likelihood framework for determining moving edges.IEEE Trans. Patt. Anal. Mach. Intell., 11(5): 499–511.

    Google Scholar 

  • Canny, J. 1986. A computational approach to edge detection.IEEE Trans. Patt. Anal. Mach. Intell, 8(6): 679–698.

    Google Scholar 

  • Cartan, E., 1988.Leçons sur la Géométrie des Espaces de Riemann, Jacques Gabay, 1988. Original edition, Gauthiers-Villars.

  • Deriche, R., 1987. Using Canny's criteria to derive a recursively implemented optimal edge detector,Intern. J. Comput. Vis., 1: 167–187, April.

    Google Scholar 

  • Deriche, R., and Faugeras, O.D., 1990. Tracking line segments,Image Vis. Comput., 8(4): 261–270. (A shorter version appeared in the Proceedings of the 1st ECCV.)

    Google Scholar 

  • D'Hayer, J., 1986. Determining motion of image curves from local pattern changes,Comput. Vis. Graph. Image Process., 34: 166–188.

    Google Scholar 

  • DoCarmo, M.P., 1976.Differential Geometry of Curves and Surfaces. Prentice-Hall: Englewood Cliffs, NJ.

    Google Scholar 

  • Faugeras, O.D., 1990a. On the motion of 3-D curves and its relationship to optical flow. O.D. Faugeras, ed.,Proceed. 1st ECCV, pp. 107–117. Springer Verlag: New York, April.

    Google Scholar 

  • Faugeras, O.D., 1990b. On the motion of 3-D curves and its relationship to optical flow, Tech. Rpt. 1183, INRIA, March.

  • Faugeras, O.D., 1991. Sur le mouvement des courbes tridimension-nelles et sa relation avec le flot optique.Compt. rend. l'Acad. Sci. Paris, pp. 1279–1285, t. 312, Série II.

  • Faugeras, O.D., Deriche, N.-E., and Navab, N., 1989. From optical flow of lines to 3D motion and structure,Proc. IEEERSJ Intern. Workshop Intell. Robots Syst., pp. 646–649, Tsukuba, Japan.

  • Faugeras, O.D., Lustman, F., and Toscani, G., 1987. Motion and structure from point and line matches,Proc. 1st Intern. Conf. Comput. Vis., London, pp. 25–34, June.

  • Gibson, J.J., 1950.The Perception of the Visual World. Houghton-Mifflin: Boston.

    Google Scholar 

  • Gong, S., 1989. Curve motion constraint equation and its applications,Proc. Workshop on Visual Motion, pp. 73–80, Irvine, CA.

  • Hildreth, E.C., 1983. The detection of intensity changes by computer and biological vision systems.Comput. Vis. Graph. Image Process., 22: 1–27.

    Google Scholar 

  • Hildreth, E.C., 1984.The Measurement of Visual Motion. MIT Press: Cambridge, MA.

    Google Scholar 

  • Horn, B.K.P., 1986.Robot Vision. MIT Press: Cambridge, MA.

    Google Scholar 

  • Horn, B.K.P., and Schunk, B.G., 1981. Determining optical flow,Artificial Intelligence, 17: 185–203.

    Google Scholar 

  • Koenderink, J.J., 1986. Optic flow,Vision Research, 26(1): 161–180.

    Google Scholar 

  • Koenderink, J.J., and van Doorn, A.J., 1975. Invariant properties of the motion parallax field due to the movement of rigid bodies relative to an observer,Optica Acta, 22: 717–723.

    Google Scholar 

  • Koenderink, J.J., and van Doorn, A.J., 1978. How an ambulant observer can construct a model of the environment from the geometrical structure of the visual inflow. In G. Hauske and E. Butenandt, editors,Kybernetik 1978, Oldenburg, Muenchen.

  • Longuet-Higgins, H.C., and Prazdny, K., 1980. The interpretation of moving retinal images.Proc. Roy. Soc. London, B 208: 385–387.

    Google Scholar 

  • Maybank, S.J., 1990. Rigid velocities compatible with five image velocity vectors,Image Vis. Comput., 8(1): 18–23, February.

    Google Scholar 

  • Meer, P., and Weiss, I., 1989. Smoothed differential filters for images. Technical Report CAR-TR-424, University of Maryland, Computer Vision Laboratory, Center for Automation Research, February.

  • Nagel, H.-H., 1983. Displacement vectors derived from second order intensity variations in image sequences,Comput. Vis., Graph., Image Process., 21: 85–117.

    Google Scholar 

  • Robert, L., and Faugeras, O.D., 1991. Curve-based stereo: Figural continuity and curvature,Proc. Conf. Comput. Vis. Patt. Recog., June, Maui, Hawaii, pp. 57–62.

  • Spivak, M., 1979.A Comprehensive Introduction to Differential Geometry, vols. 1–3. Publish or Perish, Inc.: Berkeley, 2nd edition.

    Google Scholar 

  • Toscani, G., Deriche, N.-E., and Faugeras, O.D., 1988. 3D motion estimation using a token tracker,Proc. IAPR Workshop on Computer Vision (Special Hardware and Industrial Applications), Tokyo, Japan, pp. 275–261, October.

  • Vieville, T., and Faugeras, O.D., 1992. Robust and fast computation of unbiased intensity derivatives in images. In G. Sandini, ed.,Proc. 2nd Europ. Conf. Comput. Vis., pp. 203–211, Italy, Springer-Verlag: New York.

    Google Scholar 

  • Weiss, I., 1991. High order differentiation filters that work. Tech. Rpt. CAR-TR-545, University of Maryland, Computer Vision Laboratory, Center for Automation Research, March.

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Faugeras, O., Papadopoulo, T. A theory of the motion fields of curves. Int J Comput Vision 10, 125–156 (1993). https://doi.org/10.1007/BF01420734

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