Image Flow: Fundamentals and Algorithms

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.

[1]  D Marr,et al.  Directional selectivity and its use in early visual processing , 1981, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[2]  H. Barlow,et al.  The mechanism of directionally selective units in rabbit's retina. , 1965, The Journal of physiology.

[3]  W. Grimson.  The Implicit Constraints of the Primal Sketch. , 1981 .

[4]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[5]  J. Limb,et al.  Measuring the Speed of Moving Objects from Television Signals , 1975, IEEE Trans. Commun..

[6]  Michael Brady,et al.  Rotationally symmetric operators for surface interpolation , 1983, Comput. Vis. Graph. Image Process..

[7]  Jake K. Aggarwal,et al.  Extraction of moving object descriptions via differencing , 1982, Comput. Graph. Image Process..

[8]  Allen M. Waxman,et al.  Contour Evolution, Neighborhood Deformation, and Global Image Flow: Planar Surfaces in Motion , 1985 .

[9]  S. Ullman.  The interpretation of structure from motion , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[10]  Allen M. Waxman,et al.  Dynamic Stereo: Passive Ranging to Moving Objects from Relative Image Flows , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  W. Eric L. Grimson.  Surface consistency constraints in vision , 1983, Comput. Vis. Graph. Image Process..

[12]  S. Ullman,et al.  A Model for the Spatio-Temporal Organization of X- and Y-Type Ganglion Cells in the Primate Retina , 1980 .

[13]  Jake K. Aggarwal,et al.  Computer Tracking of Objects Moving in Space , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Ciro Cafforio,et al.  Methods for measuring small displacements of television images , 1976, IEEE Trans. Inf. Theory.

[15]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  W Reichardt,et al.  Visual control of orientation behaviour in the fly: Part II. Towards the underlying neural interactions , 1976, Quarterly Reviews of Biophysics.

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

[18]  H. Barrow,et al.  RECOVERING INTRINSIC SCENE CHARACTERISTICS FROM IMAGES , 1978 .

[19]  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.

[20]  R. Courant,et al.  Methods of Mathematical Physics , 1962 .

[21]  Jan J. Koenderink,et al.  Local structure of movement parallax of the plane , 1976 .

[22]  Barin Geoffrey Haskell,et al.  Frame-to-frame coding of television pictures using two-dimensional Fourier transforms (Corresp.) , 1974, IEEE Trans. Inf. Theory.

[23]  A. Netravali,et al.  Transform domain motion estimation , 1979, The Bell System Technical Journal.

[24]  W E Grimson,et al.  A computational theory of visual surface interpolation. , 1982, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[25]  William B. Thompson,et al.  Analysis of Accretion and Deletion at Boundaries in Dynamic Scenes , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Shimon Ullman,et al.  Relaxation and constrained optimization by local processes , 1979 .

[27]  Ramesh C. Jain,et al.  Difference and accumulative difference pictures in dynamic scene analysis , 1984, Image Vis. Comput..

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

[29]  W F Clocksin,et al.  Perception of Surface Slant and Edge Labels from Optical Flow: A Computational Approach , 1980, Perception.

[30]  V. Cantoni.  Image Analysis and Processing II , 1988, Springer US.

[31]  K. Nakayama,et al.  Optical Velocity Patterns, Velocity-Sensitive Neurons, and Space Perception: A Hypothesis , 1974, Perception.

[32]  Allen R. Hanson,et al.  Computer Vision Systems , 1978 .

[33]  Thomas S. Huang,et al.  Estimating three-dimensional motion parameters of a rigid planar patch, II: Singular value decomposition , 1982 .

[34]  S. Ullman,et al.  The interpretation of visual motion , 1977 .

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

[36]  O. Braddick.  A short-range process in apparent motion. , 1974, Vision research.

[37]  Andrew Blake,et al.  Reconstructing a Visible Surface , 1984, AAAI.

[38]  David W. Murray,et al.  3D Solutions to the Aperture Problem , 1984, ECAI.

[39]  Robert J. Schalkoff,et al.  A Model and Tracking Algorithm for a Class of Video Targets , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Ramesh C. Jain,et al.  Separating Non-Stationary from Stationary Scene Components in a Sequence of Real World TV Images , 1977, IJCAI.

[41]  Tomaso A. Poggio.  Early vision: From computational structure to algorithms and parallel hardware , 1985, Comput. Vis. Graph. Image Process..

[42]  Demetri Terzopoulos,et al.  Multilevel computational processes for visual surface reconstruction , 1983, Comput. Vis. Graph. Image Process..

[43]  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.

[44]  Jake K. Aggarwal,et al.  Segmentation through the detection of changes due to motion , 1979 .

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

[46]  Larry S. Davis,et al.  Contour-based motion estimation , 1982, Comput. Vis. Graph. Image Process..

[47]  Ramesh C. Jain,et al.  On the Analysis of Accumulative Difference Pictures from Image Sequences of Real World Scenes , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[48]  J. D. Robbins,et al.  Interframe television coding using gain and displacement compensation , 1980, The Bell System Technical Journal.

[49]  W. F. Clocksin,et al.  Determinig the Orientation of Surfaces from Optical Flow , 1978, AISB/GI.

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

[51]  Demetri Terzopoulos.  Multi-Level Reconstruction of Visual Surfaces: Variational Principles and Finite Element Representations , 1982 .

[52]  Valdis Berzins,et al.  Dynamic Occlusion Analysis in Optical Flow Fields , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[54]  H. Barlow,et al.  Retinal ganglion cells responding selectively to direction and speed of image motion in the rabbit , 1964, The Journal of physiology.