A computational approach to motion perception

In this paper it is shown that the computation of the optical flow from a sequence of timevarying images is not, in general, an underconstrained problem. A local algorithm for the computation of the optical flow which uses second order derivatives of the image brightness pattern, and that avoids the aperture problem, is presented. The obtained optical flow is very similar to the true motion field — which is the vector field associated with moving features on the image plane — and can be used to recover 3D motion information. Experimental results on sequences of real images, together with estimates of relevant motion parameters, like time-to-crash for translation and angular velocity for rotation, are presented and discussed. Due to the remarkable accuracy which can be achieved in estimating motion parameters, the proposed method is likely to be very useful in a number of computer vision applications.

[1]  R. Hetherington.  The Perception of the Visual World , 1952 .

[2]  M. Hirsch,et al.  Differential Equations, Dynamical Systems, and Linear Algebra , 1974 .

[3]  Claude L. Fennema,et al.  Velocity determination in scenes containing several moving objects , 1979 .

[4]  S. Ullman.  The Interpretation of Visual Motion , 1979 .

[5]  H. C. Longuet-Higgins,et al.  The interpretation of a moving retinal image , 1980, Proceedings of the Royal Society of London. Series B. Biological Sciences.

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

[7]  R. Haralick,et al.  The Facet Approach to Optic Flow , 1983 .

[8]  Hans-Hellmut Nagel,et al.  Displacement vectors derived from second-order intensity variations in image sequences , 1983, Comput. Vis. Graph. Image Process..

[9]  S. Ullman.  Recent Computational Studies in the Interpretation of Structure from Motion , 1983 .

[10]  Thomas S. Huang,et al.  Uniqueness and Estimation of Three-Dimensional Motion Parameters of Rigid Objects with Curved Surfaces , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  E. Hildreth.  The computation of the velocity field , 1984, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[12]  Ellen C. Hildreth,et al.  Measurement of Visual Motion , 1984 .

[13]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[14]  Kenichi Kanatani.  Structure from Motion Without Correspondence: General Principle , 1985, IJCAI.

[15]  Hans-Hellmut Nagel,et al.  Toward the derivation of three-dimensional descriptions from image sequences for nonconvex moving objects , 1986, Comput. Vis. Graph. Image Process..

[16]  Hans-Hellmut Nagel,et al.  On the Estimation of Optical Flow: Relations between Different Approaches and Some New Results , 1987, Artif. Intell..

[17]  A. Waxman.  An image flow paradigm , 1987 .

[18]  David J. Heeger,et al.  Optical flow from spatialtemporal filters , 1987 .

[19]  Alessandro Verri,et al.  Against Quantitative Optical Flow , 1987 .

[20]  M. Bertero,et al.  Ill-posed problems in early vision , 1988, Proc. IEEE.

[21]  Wilfried Enkelmann,et al.  Investigations of multigrid algorithms for the estimation of optical flow fields in image sequences , 1988, Comput. Vis. Graph. Image Process..

[22]  W. Reichardt,et al.  Movement Detectors of the Correlation Type Provide Sufficient Information for Local Computation of the 2-D Velocity Field , 1988 .

[23]  Hans-Hellmut Nagel,et al.  Image Sequences - Ten (Octal) Years - from phenomenology towards a Theoretical Foundation , 1988, Int. J. Pattern Recognit. Artif. Intell..

[24]  M. Egelhaaf,et al.  Movement detectors provide sufficient information for local computation of 2-D velocity field , 1988, Naturwissenschaften.