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Image Flow: Fundamentals and Algorithms

  • Chapter
Motion Understanding

Part of the book series: The Kluwer International Series in Engineering and Computer Science ((SECS,volume 44))

Abstract

This chapter describes work toward understanding the fundamentals of image flow and presents algorithms for estimating the image flow field. Image flow is the velocity field in the image plane that arises due to the projection of moving patterns in the scene onto the image plane. The motion of patterns in the image plane may be due to the motion of the observer, the motion of objects in the scene, or both. The motion may also be apparent motion where a change in the image between frames gives the illusion of motion. The image flow field can be used to solve important vision problems provided that it can be accurately and reliably computed. Potential applications are discussed in Section 2.1.2.

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References

  • Ames, W.F., (1977) Numerical Methods for Partial Differential Equations, 2nd ed., New York: Academic Press.

    MATH  Google Scholar 

  • Barlow, H.B., Hill, R.M., and Levick, W.R., (1964) ‘Retinal ganglion cells responding selectively to direction and speed of image motion in the rabbit,’ J. of Physiology, vol. 173, pp. 377–407.

    Google Scholar 

  • Barlow, H.B. and Levick, W.R., (1965) ‘The mechanism of directionally selective units in rabbit’s retina,’ J. of Physiology, vol. 178, pp. 477–504.

    Google Scholar 

  • Barrow, H.G. and Tenenbaum, J.M., (1978) ‘Recovering intrinsic scene characteristics from images,’ in Computer Vision Systems, A.R. Hanson and E.M. Riseman (eds.), Academic Press, New York.

    Google Scholar 

  • Batali, J. and Ullman, S., (1979) ‘Motion detection and analysis,’ in Image Understanding, L.S. Baumann (ed.), Science Applications, Arlington, Virginia, pp. 69–75

    Google Scholar 

  • Bers, K.H., Bohner, M., and Gerlach, H., (1980) ‘Object detection in image sequences,’ Proc. Int. Joint Conf. Pattern Recognition, pp. 1317–1319.

    Google Scholar 

  • Blake, A., (1984) ‘Reconstructing a visible surface,’ Proc. Nat. Conf Artificial Intelligence, pp. 23–26.

    Google Scholar 

  • Braddick, O., (1974) ‘A short-range process in apparent motion,’ Vision Research, vol. 14, pp. 519–527.

    Article  Google Scholar 

  • Brady, M., and Horn, B.K.P., (1981) ‘Rotationally symmetric operators for surface interpolation,’ Memo 654, Artificial Intelligence Laboratory, M.I.T.

    Google Scholar 

  • Buckner, E., (1976) ‘Elementary movement detectors in an insect visual system,’ Biological Cybernetics, vol. 24, pp. 85–101.

    Article  Google Scholar 

  • Buxton, B.F., Buxton, H., Murray, D.W., and Williams, N.S., (1983) ‘3D solutions to the aperture problem,’ Tech. Rep., GEC Research Laboratories.

    Google Scholar 

  • Cafforio, C., and Rocca, F., (1976) ‘Methods for measuring small displacements of television images,’ IEEE Trans. on Information Theory, vol. IT-22, pp. 573–579.

    Article  Google Scholar 

  • Canny, J.F., (1983) ‘Finding edges and lines in images,’ Tech. Rep. 720, Artificial Intelligence Laboratory, M.I.T.

    Google Scholar 

  • Clocksin, W.F., (1978) ‘Determining the orientation of surfaces from optical flow,’ Proc. 3rd AISB Conf, pp. 93–102.

    Google Scholar 

  • Clocksin, W.F., (1980) ‘Perception of surface slant and edge labels from optical flow: A computational approach.’ Perception, vol. 9, pp. 253–269.

    Google Scholar 

  • Courant, R. and Hilbert, D., (1937) Methods of Mathematical Physics, vol. 1, Wiley-Interscience, New York.

    Google Scholar 

  • Davis, L.S., Wu, Z., and Sun, H., (1981) ‘Contour-based motion estimation,’ Tech. Rep., Computer Vision Laboratory, University of Maryland.

    Google Scholar 

  • Fennema, C.L., and Thompson, W.B., (1979) ‘Velocity determination in scenes containing several moving objects,’ Computer Graphics and Image Proccessinf, vol. 9, pp. 301–315.

    Article  Google Scholar 

  • Flinchbaugh, B.E., and Chandrasekaran, B., (1981) ‘A theory of spatiotemporal aggregation for vision,’ Artificial Intelligence, vol. 17, pp. 387–407. reprinted in Computer Vision, M. Brady (ed.), North-Holland, Amsterdam.

    Google Scholar 

  • Geman, S., and Geman, D., (1984) ‘Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,’ IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. PAMI-6, pp. 721–741.

    Article  Google Scholar 

  • Grimson, W.E.L., (1981a) From Images to Surfaces: A Computational Study of the Human Early Vision System, M. I. T. Press, Cambridge.

    Google Scholar 

  • Grimson, W.E.L., (198lb) ‘A computational theory of visual surface interpolation,’ Memo 613, Artificial Intelligence Laboratory, M.I.T.

    Google Scholar 

  • Grimson, W.E.L., (1981c) ‘The implicit constraints of the primal sketch,’ Memo 663, Artificial Intelligence Laboratory, M.I.T.

    Google Scholar 

  • Grimson, W.E.L., (1983) ‘Surface consistency constraints in vision,’ Computer Vision, Graphics and Image Processing, vol. 24, pp. 28–51.

    Article  Google Scholar 

  • Haskell, (1974) ‘Frame-to-frame coding of television pictures using two-dimensional Fourier transforms,’ IEEE Trans. on Information Theory, vol. IT-20, pp. 119–120.

    Google Scholar 

  • Hildreth, E.C., (1984) ‘The computation of the velocity field,’ Proc. Royal Society London, B, vol. 221, pp. 189–220.

    Article  Google Scholar 

  • Hildreth, E.C., and Ullman, S., (1982) ‘The measurement of visual motion,’ Memo 699, Artificial Intelligence Laboratory, M.I.T.

    Google Scholar 

  • Hirzinger, G., Landzettel, K., and Snyder, W., (1980) ‘Automated TV tracking of moving objects: The DFVLR tracker and related approaches,’ Proc. Int. Joint Conf Pattern Recognition, pp. 1255–1261.

    Google Scholar 

  • Hoffman, D.D., (1980) ‘Inferring shape from motion fields,’ Memo 592, Artificial Intelligence Laboratory, M.I.T.

    Google Scholar 

  • Horn, B.K.P., and Bruss, A.R., (1983) ‘Passive navigation,’ Computer Vision, Graphics and Image Processing, vol. 21, pp. 3–20.

    Article  Google Scholar 

  • Horn, B.K.P., and Schunck, B.G., (1981) ‘Determining optical flow,’ Artificial Intelligence, vol. 17, reprinted in Computer Vision, M. Brady (ed.), North-Holland, Amsterdam, pp. 185–203.

    Google Scholar 

  • Jain, R., Martin, W.N., and Aggarwal, J.K., (1979) ‘Segmentation through the detection of changes due to motion,’ Computer Graphics and Image Processing, vol. 11, pp. 13–34.

    Article  Google Scholar 

  • Jain, R., Militzer, D., and Nagel, H.-H., (1977) ‘Separating non-stationary from stationary scene components in a sequence of real world TV-images,’ Proc, Int. Joint Conf. Artificial Intelligence, pp. 612–618.

    Google Scholar 

  • Jain, R., and Nagel, H.-H., (1979) ‘On the analysis of accumulative difference pictures from image sequences of real world scenes,’ IEEE Trans. on Pattern Analalysis and Machine Intelligence, vol. PAMI-1, pp. 206–214.

    Article  Google Scholar 

  • Jain, R., (1983) ‘Difference and accumulative difference pictures in dynamic scene analysis,’ Rep. GMR-4548, General Motors Research Laboratories.

    Google Scholar 

  • Jones, R.A., and Rashid, H., (1981) ‘Residual recursive displacement estimation,’ Proc Conf. Pattern Recognition and Image Processing, pp. 508–509.

    Google Scholar 

  • Koenderink, J.J., and van Doom, A.J., (1975) ‘Invariant properties of the motion parallax field due to the movement of rigid bodies relative to an observer,’ Optica Acta, vol. 22, pp. 773–791.

    Google Scholar 

  • Koenderink, J.J., and van Doom, A.J., (1976) ‘Local structure of movement parallax of the plane,’ J. Optical Society America, vol. 66, pp. 717–723.

    Article  Google Scholar 

  • Limb, J.O., and Murphy, J.A., (1975a) ‘Measuring the speed of moving objects from television signals,’ IEEE Trans. on Communication, vol. COM-23, pp. 474–478.

    Google Scholar 

  • Limb, J.O., and Murphy, J.A., (1975b) ‘Estimating the velocity of moving images in television signals,’ Computer Graphics and Image Processing, vol. 4, pp. 311–327.

    Article  Google Scholar 

  • Longuet-Higgins, H.C., and Prazdny, K., (1980) The interpretation of moving retinal image,’ Proc. Royal Society London, vol. B 208, pp. 385–397.

    Article  Google Scholar 

  • Marr, D., and Hildreth, E., (1980) ‘Theory of edge detection,’ Proc. Royal Society London, vol. B 207, pp. 187–217.

    Article  Google Scholar 

  • Marr, D., and Ullman, S., (1981) ‘Directional selectivity and its use in early visual processing,’ Proc. Royal Society London, vol. B 211, pp. 151–180.

    Article  Google Scholar 

  • Mutch, K.M., and Thompson, W.B., (1984) ‘Analysis of accretion and deletion at boundaries in dynamic scenes,’ Tech. Rep. 84–7, Department of Computer Science, University of Minnesota, Minneapolis.

    Google Scholar 

  • Nagel, H.-H., (1980) ‘From digital picture processing to image analysis,’ Proc. Int. Conf. Image Analysis and Processing.

    Google Scholar 

  • Nakayama, K., and Loomis, J., (1974) ‘Optical velocity patterns, velocity-sensitive neurons, and space perception: A hypothesis,’ Perception, vol. 3, pp. 63–80.

    Article  Google Scholar 

  • Netravali, A.N., and Robbins, J.D., (1979) Motion-compensated television coding: Part I,’ Bell System Tech. J., vol. 58, pp. 631–670.

    MATH  Google Scholar 

  • Nevatia, R., (1982) Machine Perception, Prentice-Hall, Englewood Cliffs, NJ.

    Google Scholar 

  • Poggio, T., and Reichardt, W., (1976) ‘Visual control of orientation behavior in the fly,’ Quart. Rev. of Biophysics, vol. 9, pp. 377–438.

    Article  Google Scholar 

  • Poggio, T., (1985) ‘Early vision: From computational structure to algorithms and parallel hardware,’ Computer Vision, Graphics and Image Processing, vol. 31, pp. 139–155.

    Article  Google Scholar 

  • Prazdny, K., (1980) ‘Egomotion and relative depth map from optical flow,’ Biological Cybernetics, vol. 36, pp. 87–102.

    Article  MathSciNet  MATH  Google Scholar 

  • Price, C., Snyder, W., and Rajala, S., (1981) ‘Computer tracking of moving objects using a Fourier-domain filter based on a model of the human visual system,’ Proc, Conf Pattern Recognition and Image Processing, pp. 98–102.

    Google Scholar 

  • Reichardt, W., and Poggio, T., (1979) ‘Figure-ground discrimination by relative movement in the visual system of the fly,’ Biological Cybernetics, vol. 35, pp. 81–100.

    Article  Google Scholar 

  • Richter, J., and Ullman, S., (1980) ‘A model for the spatio-temporal organization of X and Y-type ganglion cells in the primate retina,’ Memo 573, Artificial Intelligence Laboratory, M.I.T.

    Google Scholar 

  • Roach, J.W. and Aggarwal, J.K., (1979) ‘Computer tracking of objects moving in space.’ IEEE Trans. on Pattern Analalysis and Machine Intelligence, vol. PAMI-1, pp. 127–135.

    Article  Google Scholar 

  • Schalkoff, R.J., and McVey, E.S., (1982) ‘A model and tracking algorithm for a class of video targets,’ IEEE Trans. on Pattern Analalysis and Machine Intelligence, vol. PAMI-4, pp. 2–10.

    Article  Google Scholar 

  • Schetzen, M., (1980) The Voltera and Wiener Theories of Nonlinear Systems, John Wiley & Sons, New York.

    Google Scholar 

  • Schunck, B.G., (1983) ‘Motion segmentation and estimation,’ Department of Electrical Engineering and Computer Science, M. I. T., PhD. Dissertation.

    Google Scholar 

  • Schunck, B.G., (1984a) ‘The motion constraint equation for optical flow,’ Proc. Int. Joint Conf. on Pattern Recognition, Montreal, pp. 20–22.

    Google Scholar 

  • Schunck, B.G., (1984b) ‘Motion segmentation and estimation by constraint line clustering,’ Proc. 2nd IEEE Workshop on Computer Vision, Annapolis, Maryland, pp. 58–62.

    Google Scholar 

  • Schunck, B.G., (1984c) ‘Surface-based smoothing of optical flow fields,’ Proc. Conf. on Intelligent Systems and Machines, Oakland University, Rochester, Michigan, pp. 107–111.

    Google Scholar 

  • Schunck, B.G., and Horn, B.K.P., (1981) ‘Constraints on optical flow computation,’ Proc. Conf. Pattern Recognition and Image Processing, pp. 205–210.

    Google Scholar 

  • Snyder, W.E., Rajala, S.A., and Hirzinger, G., (1980) ‘Image modelling: The continuity assumption and tracking,’ Proc. Int. Joint Conf. Pattern Recognition, pp. 1111–1114.

    Google Scholar 

  • Stuller, J.A., and Netravali, A.N., (1979) ‘Transform domain motion estimation,’ Bell System Tech. J., vol. 58, pp. 1673–1702.

    MathSciNet  Google Scholar 

  • Stuller, J.A., Netravali, A.N., and Robbins, J.D., (1980) ‘Interframe television coding using gain and displacement compensation,’ Bell System Tech. J., vol. 59, pp. 1227–1240.

    Google Scholar 

  • Terzopoulos, D., (1982) ‘Multilevel reconstruction of visual surfaces: Variational principles and finite element representations,’ Memo 671, Artificial Intelligence Laboratory, M.I.T.

    Google Scholar 

  • Terzopoulos, D., (1983) ‘Multilevel computational processes for visual surface reconstruction,’ Computer Vision, Graphics and Image Processing, vol. 24, pp. 52–96.

    Article  Google Scholar 

  • Thompson, W.B., Mutch, K.M., and Berzins, V.A., (1984) ‘Dynamic occlusion analysis in optical flow fields,’ Tech. Rep. 84–6, Department of Computer Science, University of Minnesota.

    Google Scholar 

  • Tsai, R.Y., and Huang, T.S., (1982) ‘Uniqueness and estimation of three-dimensional motion parameters of rigid objects with curved surfaces,’ Proc. Conf. Pattern Recognition and Image Processing, pp. 112–118.

    Google Scholar 

  • Tsai, R.Y., Huang, T.S., and Zhu, W.-L., (1982) ‘Estimating three-dimensional motion parameters of a rigid planar patch, II: Singular value decomposition,’ IEEE Trans. on Acoustics, Speech, and Signal Processing, vol. ASSP-30, pp. 525–534.

    Article  Google Scholar 

  • Ullman, S., (1979a) ‘The interpretation of structure from motion,’ Proc. Royal Society London, vol. B 203, pp. 405–426.

    Article  Google Scholar 

  • Ullman, S., (1979b) The Interpretation of Visual Motion, M. I. T. Press, Cambridge.

    Google Scholar 

  • Ullman, S., (1979c) ‘Relaxation and constrained optimization by local processes,’ Computer Graphics and Image Processing, vol. 10, pp. 115–125.

    Article  Google Scholar 

  • Waxman, A., and Ullman, S., (1983) ‘Surface structure and 3-d motion from image flow: A kinematic analysis,’ Tech. Rep., Center for Automation Research, University of Maryland.

    Google Scholar 

  • Waxman, A.M. and Sinha, S.S., (1984) ‘Dynamic stereo: Passive ranging to moving objects from relative image flows,’ Tech. Rep., Computer Vision Laboratory, University of Maryland.

    Google Scholar 

  • Waxman, A.M., (1984) ‘An image flow paradigm,’ Tech. Rep., Computer Computer Vision Laboratory, University of Maryland.

    Google Scholar 

  • Waxman, A.M., and Wohn, K., (1984) ‘Contour evolution, neighborhood deformation and global image flow: Planar surfaces in motion,’ Computer Vision Laboratory, University of Maryland.

    Google Scholar 

  • Yalamanchili, S., Martin, W.N., and Aggarwal, J.K., (1982) ‘Extraction of moving object descriptions via differencing,’ Computer Vision, Graphics and Image Processing, vol. 18, pp. 188–201.

    Article  Google Scholar 

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Authors
  1. Brian G. Schunck
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Editors and Affiliations

  1. University of Virginia, USA

    W. N. Martin

  2. University of Texas at Austin, USA

    J. K. Aggarwal

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© 1988 Kluwer Academic Publishers

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Schunck, B.G. (1988). Image Flow: Fundamentals and Algorithms. In: Martin, W.N., Aggarwal, J.K. (eds) Motion Understanding. The Kluwer International Series in Engineering and Computer Science, vol 44. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1071-6_2

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  • DOI: https://doi.org/10.1007/978-1-4613-1071-6_2

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-8413-0

  • Online ISBN: 978-1-4613-1071-6

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