Beauchemin, S.S.[Steven S.],
Barron, J.L.[John L.],
The Computation of Optical-Flow,
Surveys(27), No. 3, September 1995, pp. 433-467.
WWW Version.
Survey, Optic Flow.
BibRef
9509
Gibson, J.J.,
The Perception of the Visual World,
Boston:
Houghton Mifflin1955. ??
BibRef
5500
BookBasic perception book where optical flow is formally introduced.
BibRef
Gibson, J.J.,
Optical Motion and Transformations as Stimuli for Visual Perceptions,
PsychR(64), No. 5, 1957, pp. 288-295.
BibRef
5700
Gibson, J.J.,
What Gives Rise to the Perception of Motion?,
PsychR(75), No. 4, 1968, pp. 335-346.
BibRef
6800
Limb, J.O., and
Murphy, J.A.,
Estimating Velocity of Moving Images in Television Signals,
CGIP(4), No. 4, December 1975, pp. 311-327.
WWW Version.
BibRef
7512
And:
Measuring the Speed of Moving Objects from Television Signals,
Commun(23), No. 4, April 1975, pp. 474-478.
Early gradient based method for computation directly from image
measurements. The basic results here are that velocity estimates
work only for single moving objects.
Essentially the subpixel interpolation of the correlation peak in
the matchpoint neighborhood.
See also Source-Receiver Encoding of Television Signals.
BibRef
Cafforio, C., and
Rocca, F.,
Methods for Measuring Small Displacements of Television Images,
IT(22), No. 5 September, 1976, pp. 573-579.
BibRef
7609
Cafforio, C.,
Rocca, F.,
Tracking Moving Objects in Television Images,
SP(1), 1979, pp. 133-140.
BibRef
7900
Cafforio, C.,
Remarks on the Differential Method for the Estimation of
Movement in Television Images,
SP(4), 1982, pp. 45-52.
BibRef
8200
Cafforio, C., and
Rocca, F.,
The Differential Method for Image Motion Estimation,
ISPDSA83(104-124).
BibRef
8300
Horn, B.K.P., and
Schunck, B.G.,
Determining Optical Flow,
AI(17), No. 1-3, August 1981, pp. 185-203.
WWW Version.
BibRef
8108
Earlier:
DARPA81(144-156).
BibRef
And:
MIT AI Memo-572, April 1980.
WWW Version.
Optical Flow.
The standard reference for original optical flow equation
computations. The exact formulations are not quite right since
they only work in special cases.
BibRef
Schunck, B.G.,
Horn, B.K.P.,
Constraints on Optical Flow Computation,
PRIP81(205-210).
BibRef
8100
Horn, B.K.P., and
Schunck, B.G.,
Determining Optical Flow: A Retrospective,
AI(59), No. 1-2, January 1993, pp. 81-87.
WWW Version.
BibRef
9301
Willick, D., and
Yang, Y.H.,
Experimental Evaluation of Motion Constraint Equations,
CVGIP(54), No. 2, September 1991, pp. 206-214.
WWW Version. Evaluate
See also Determining Optical Flow.
See also Motion Constraint Equation for Optical Flow, The. and
See also On a Constraint Equation for the Estimation of Displacement Rates in Image Sequences. constraint equations and conclude that
the original was the best for optical flow.
BibRef
9109
Longuet-Higgins, H.C., and
Prazdny, K.,
The Interpretation of a Moving Retinal Image,
RoyalP(B-208), 1980, pp. 385-397.
Optical Flow. An early formulation of the flow pattern with rotation and translation.
See also Multiple Interpretations of a Pair of Images of a Surface.
BibRef
8000
Ullman, S.,
The Interpretation of Visual Motion,
Cambridge:
MIT Press1979.
BibRef
7900
Ph.D.Thesis (EE). His thesis as a
BibRef
Book
Relaxation.
A network of points are generated for each image with a relaxation
based matching scheme applied to find the 1-1
mapping between the views.
See also Interpretation of Structure from Motion, The.
BibRef
Ullman, S.,
The Optical Flow of Planar Surfaces,
SV(1), 1986, pp. 263-276.
BibRef
8600
And:
MIT AI Memo-870, December 1985.
BibRef
Ullman, S.,
Against Direct Perception,
MIT AI Memo-574, March 1980.
BibRef
8003
Hildreth, E.C.[Ellen C.],
Computations Underlying the Measurement of Visual Motion,
AI(23), No. 3, August 1984, pp. 309-354.
WWW Version.
BibRef
8408
And:
IU8799-146).
BibRef
And:
MIT AI Memo-761, March 1984.
BibRef
Earlier:
The Measurement of Visual Motion,
Cambridge:
MIT Press1983.
BibRef
Book
BibRef
And:
Add A2:
Ullman, S.[Shimon],
MIT AI Memo-699, December 1982.
BibRef
Hildreth, E.C.,
The Computation of the Velocity Field,
RoyalP(B-221), 1984, pp. 189-220.
BibRef
8400
And:
MIT AI Memo-734, September 1983.
See also Computing the Velocity Field along Contours.
BibRef
Hildreth, E.C.[Ellen C.],
Koch, C.[Christof],
The Analysis of Visual Motion:
From Computational Theory to Neuronal Mechanisms,
MIT AI Memo-919, December 1986.
WWW Version.
BibRef
8612
Mitiche, A., and
Aggarwal, J.K.,
A Computational Analysis of Time-Varying Images,
HPRIP86(311-332).
Survey, Motion.
Motion, Survey.
BibRef
8600
Jacobson, L., and
Wechsler, H.,
Derivation of Optical Flow Using a Spatiotemporal-Frequency Approach,
CVGIP(38), No. 1, April 1987, pp. 29-65.
WWW Version.
Survey, Motion.
Motion, Survey. The approach includes Hildreth and Schunck. The paper has a nice
survey of techniques and a lot of equations.
There may be something here if you want optical flow.
BibRef
8704
Jacobson, L., and
Wechsler, H.,
A Theory for Invariant Object Recognition in the Frontoparallel Plane,
PAMI(6), No. 3, May 1984, pp. 325-331.
BibRef
8405
And:
A Paradigm for Invariant Object Recognition of Brightness, Optical Flow and
Binocular Disparity Images,
PRL(1), No. 1, October 1982, pp. 61-68.
BibRef
Nagel, H.H.[Hans-Hellmut],
On the Estimation of Optical Flow:
Relations between Different Approaches and Some New Results,
AI(33), No. 3, November 1987, pp. 299-324.
WWW Version.
Optical Flow.
A unifying approach to optical flow.
BibRef
8711
Werkhoveh, P.,
Toet, A., and
Koenderink, J.J.,
Displacement Estimates Through Adaptive Affinities,
PAMI(12), No. 7, July 1990, pp. 658-663.
IEEE Abstract. IEEE Top Reference.
WWW Version. Replace iterative approach of
See also On the Estimation of Optical Flow: Relations between Different Approaches and Some New Results. with a noniterative scheme.
BibRef
9007
Horn, B.K.P.,
Motion Fields Are Hardly Ever Ambiguous,
IJCV(1), No. 3, October 1987, pp. 239-258.
WWW Version. The cases where a flow field can be ambiguous are difficult to
construct and thus are not a major concern for the solution.
BibRef
8710
Negahdaripour, S.,
Critical Surface Pairs and Triplets,
IJCV(3), No. 4, November 1989, pp. 293-312.
WWW Version. Where can the field have multiple interpretations. At most, for a
curved surface, it is three interpretations.
BibRef
8911
Negahdaripour, S.[Shahriar],
Multiple Interpretations of the Shape and
Motion of Objects from Two Perspective Images,
PAMI(12), No. 11, November 1990, pp. 1025-1039.
IEEE Abstract. IEEE Top Reference.
WWW Version.
BibRef
9011
Earlier:
Ambiguities of a Motion Field,
ICCV87(607-611).
BibRef
And:
MIT AI Memo-940, January 1987.
Cases with ambiguous perspective motion fields are
limited with know flow for all points on the surface.
BibRef
Singh, A., and
Allen, P.K.,
Image-Flow Computation: An Estimation-Theoretic Framework
and a Unified Perspective,
CVGIP(56), No. 2, September 1992, pp. 152-177.
WWW Version.
BibRef
9209
Singh, A.,
Incremental Estimation of Image-Flow Using a Kalman Filter,
JVCIR(3), 1992, pp. 39-57.
BibRef
9200
Earlier:
Motion91(36-43).
BibRef
Singh, A.,
An Estimation-Theoretic Framework for Image-Flow Computation,
ICCV90(168-177).
WWW Version.
BibRef
9000
And:
DARPA90(314-328).
Kalman Filter. Generate the depths from a spatio-temporal sequence.
BibRef
Singh, A.,
Image-Flow Computation:
An Estimation-Theoretic Framework, Unification and Integration,
MVA(4), 1991, pp. 55.
BibRef
9100
Singh, A.,
Robust Computation of Image-Motion and Scene Depth,
CRA91(2730-2737).
BibRef
9100
Singh, A.,
Optic Flow Computation: A Unified Perspective,
IEEE_Press1990.
BibRef
9000
And:
From the thesis:
Image-Flow Computation: Estimation-Theoretic Framework,
Unification and Integration,
Ph.D.Thesis (CS), Columbia, Univ., May 1990.
BibRef
Ioka, M.,
Kurokawa, M.,
Estimation Of Motion Vectors And Their Application To Scene Retrieval,
MVA(7), No. 3, 1994, pp. 199-208.
BibRef
9400
Fitzpatrick, J.M.[J. Michael],
The Existence of Geometrical Density-Image Transformations
Corresponding to Object Motion,
CVGIP(44), No. 2, November 1988, pp. 155-174.
WWW Version.
Geometrical image
transformation is identical to change in density image produced by motion of
the object. (Primarily medical imagery.)
BibRef
8811
Fitzpatrick, J.M.[J. Michael],
A Method for Calculating Velocity in Time Dependent Images
Based on the Continuity Equation,
CVPR85(78-81). (Vanderbilt Univ.)
CT or X-ray data is preferred, more equations than results.
BibRef
8500
Sozou, P.D., and
Loizou, G.,
New Perspectives on Optical-Flow,
PR(26), No. 8, August 1993, pp. 1125-1136.
WWW Version.
BibRef
9308
Arnspang, J.,
Motion Constraint Equations Based on Constant Image Irradiance,
IVC(11), No. 9, November 1993, pp. 577-587.
WWW Version.
BibRef
9311
Malladi, R.,
Sethian, J.A.,
Image-Processing: Flows under Min/Max Curvature and Mean-Curvature,
GMIP(58), No. 2, March 1996, pp. 127-141.
Level Set Methods.
BibRef
9603
Malladi, R.,
Sethian, J.A.,
Flows under Min/Max Curvature Flow and Mean Curvature:
Applications in Image Processing,
ECCV96(I:251-262).
WWW Version. Image enhancement, noise suppression.
BibRef
9600
Heikkonen, J.,
A Computer Vision Approach to Air-Flow Analysis,
PRL(17), No. 4, April 4 1996, pp. 369-385.
9605
BibRef
Ma, J.,
Lu, X.,
Wu, C.,
A Motion Constraint Equation under Space-Varying or
Time-Varying Illumination,
PRL(5), 1987, pp. 203-205.
BibRef
8700
Zanker, J.M.,
Second-Order Motion Perception in the Peripheral Visual-Field,
JOSA-A(14), No. 7, July 1997, pp. 1385-1392.
9708
BibRef
Brodský, T.,
Fermüller, C.,
Aloimonos, Y.,
Directions of Motion Fields Are Hardly Ever Ambiguous,
IJCV(26), No. 1, January 1998, pp. 5-24.
WWW Version.
9804
BibRef
Earlier:
ECCV96(II:119-128).
WWW Version.
BibRef
And:
UMDTR3501, 1995.
WWW Version.
WWW Version.
BibRef
Gros, B.L.,
Blake, R.,
Hiris, E.,
Anisotropies in Visual Motion Perception: A Fresh Look,
JOSA-A(15), No. 8, August 1998, pp. 2003-2011.
9808
BibRef
Åström, K.[Kalle],
Heyden, A.[Anders],
Continuous Time Matching Constraints for Image Streams,
IJCV(28), No. 1, June 1998, pp. 85-96.
WWW Version.
9807
Multilinear constraints for optical flow.
See also Simplifications of Multilinear Forms for Sequences of Images.
BibRef
Mitiche, A.,
Mansouri, A.R.,
On convergence of the Horn and Schunck optical-flow estimation method,
IP(13), No. 6, June 2004, pp. 848-852.
WWW Version.
0406 See also Determining Optical Flow. Analyze the equations to prove convergence via both the
Jacobi and the Gauss-Seidel methods.
BibRef
Bayerl, P.[Pierre],
Neumann, H.[Heiko],
Disambiguating Visual Motion by Form-Motion Interaction:
A Computational Model,
IJCV(72), No. 1, April 2007, pp. 27-45.
WWW Version.
0001
BibRef
Earlier:
Neural Mechanisms of Visual Flow Integration and Segregation:
Insights from the Pinna-Brelstaff Illusion and Variations of It,
BMCV02(301 ff.).
HTML Version.
0303Computational model of neural mechanisms for visual flow.
BibRef
Beck, C.[Cornelia],
Gottbehuet, T.[Thomas],
Neumann, H.[Heiko],
Integration of Multiple Temporal and Spatial Scales for Robust Optic
Flow Estimation in a Biologically Inspired Algorithm,
CAIP07(53-60).
WWW Version.
0708
BibRef
Beck, C.[Cornelia],
Bayerl, P.[Pierre],
Neumann, H.[Heiko],
Optic Flow Integration at Multiple Spatial Frequencies:
Neural Mechanism and Algorithm,
ISVC06(I: 741-750).
WWW Version.
0611
BibRef
Bayerl, P.[Pierre],
Neumann, H.[Heiko],
A Fast Biologically Inspired Algorithm for Recurrent Motion Estimation,
PAMI(29), No. 2, February 2007, pp. 246-260.
WWW Version.
0701Sparse coding framework to implement the method.
BibRef
Giaccone, P.R.,
Jones, G.A.,
Spatio-Temporal Approaches to Computation of Optical Flow,
BMVC97(xx-yy).
HTML Version.
0209
BibRef
Randriantsoa, A.,
Berthoumieu, Y.,
Optical Flow Estimation Using Forward-backward Constraint Equation,
ICIP00(Vol II: 578-581).
IEEE Abstract. IEEE Top Reference.
0008
BibRef
Iu, S.L.[Siu-Leong],
Lin, Y.T.[Yun-Ting],
Re-examining the Optical Flow Constraint:
A New Optical Flow Algorithm with Outlier rejection,
ICIP99(III:727-731).
IEEE Abstract. IEEE Top Reference.
BibRef
9900
Moons, T.,
Pauwels, E.J.,
Van Gool, L.J., and
Oosterlinck, A.,
Towards a General Framework for Feature Extraction,
CVPR92(865-868).
IEEE Abstract. IEEE Top Reference. Merge optical flow and recognition.
BibRef
9200
Chapter on Optical Flow Field Computations and Use continues in
Optical Flow Field Computation and Analysis .