Number of Solutions for Motion and Structure from Multiple Frame Correspondence

Much of the dynamic computer vision literature deals with the determination of motion and structure by observing two frames captured at two instants of time. Motion prediction and understanding can be improved significantly, particularly in the presence of noise, by analyzing an image sequence containing more than two frames. In this paper, we assume knowledge of correspondence of points on the surface of an object which is moving with constant motion, i.e., constant translation and constant rotation around an unknown center. We give a new formulation of the problem and prove that the following results hold in general for the number of solutions to motion and structure values (i.e., values of translation, rotation, and depth):(a) For three point correspondences over three views, there are at most two solutions, only one of which has all positive depth values;(b) For two point correspondences over four views, there is a unique solution;(c) For one point correspondence over five views, there can be up to ten solutions;(d) For one point correspondence over six views, there is a unique solution.The method of solution for each of the above formulations requires the solving of a system of multivariate polynomials, whose coefficients are functions of the observed data. In order to determine the number of solutions to these systems, we use theorems from algebraic geometry which imply that under a few mild conditions, the number of solutions at one set of data points provides an upper bound on the number of solutions for almost all sets of data points.Thus a bound on the number of solutions is obtained when a single system is solved by a method such as homotopy continuation, which we use here.

[1]  Layne T. Watson,et al.  Finding all isolated solutions to polynomial systems using HOMPACK , 1989, TOMS.

[2]  Thomas S. Huang,et al.  Motion and structure from feature correspondences: a review , 1994, Proc. IEEE.

[3]  I. Shafarevich Basic algebraic geometry , 1974 .

[4]  Hormoz Shariat,et al.  Motion Estimation with More than Two Frames , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Narendra Ahuja,et al.  3-D MOTION FROM IMAGE SEQUENCES: MODELING, UNDERSTANDING AND PREDICTION. , 1986 .

[6]  A. Morgan,et al.  Coefficient-parameter polynomial continuation , 1989 .

[7]  Bruno Buchberger,et al.  A criterion for detecting unnecessary reductions in the construction of Groebner bases , 1979, EUROSAM.

[8]  D. D. Hoffman,et al.  The computation of structure from fixed-axis motion: rigid structures , 1986, Biological Cybernetics.

[9]  A. Morgan,et al.  A homotopy for solving general polynomial systems that respects m-homogeneous structures , 1987 .

[10]  Thomas S. Huang,et al.  Algebraic methods in 3‐d motion estimation from two‐view point correspondences , 1989, Int. J. Imaging Syst. Technol..

[11]  Allen M. Waxman,et al.  Closed-form solutions to image flow equations for 3D structure and motion , 1988, International Journal of Computer Vision.

[12]  Robert J. Holt,et al.  Camera calibration problem: Some new results , 1991, CVGIP Image Underst..

[13]  J. Limb,et al.  Estimating the Velocity of Moving Images in Television Signals , 1975 .

[14]  Robert J. Holt,et al.  Motion from Optic Flow: Multiplicity of Solutions , 1993, J. Vis. Commun. Image Represent..

[15]  M. Subbarad,et al.  Interpretation of Visual Motion: A Computational Study , 1988 .

[16]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[17]  Armand Borel Linear Algebraic Groups , 1991 .

[18]  J. Salz,et al.  Algorithms for estimation of three-dimensional motion , 1985, AT&T Technical Journal.

[19]  John Canny,et al.  The complexity of robot motion planning , 1988 .

[20]  Jake K. Aggarwal,et al.  On the computation of motion from sequences of images-A review , 1988, Proc. IEEE.

[21]  Alan M. Wood,et al.  Motion analysis , 1986 .

[22]  J. D. Robbins,et al.  Motion-compensated television coding: Part I , 1979, The Bell System Technical Journal.