Enkelmann, W.,
Kories, R.,
Nagel, H.H., and
Zimmermann, G.,
An Experimental Investigation of Estimation Approaches
for Optical Flow Fields,
MU88(189-226).
Discussion of 2 optical flow techniques, feature extraction and match and
differential or gradient approaches. The conclusion is that for both
methods, certain features provide dominant contributions to the optical flow
computation.
BibRef
8800
Prager, J.M.[John M.], and
Arbib, M.A.[Michael A.],
Computing the Optic Flow: The MATCH Algorithm and Prediction,
CVGIP(24), No. 3, December 1983, pp. 271-304.
WWW Version. Using the smoothness of motion and almost rigid body constraints,
the vector field (optic flow field) is computed. The algorithm
also assumes that feature points are dense on the objects.
Seems to produce results. The work is from 1980.
See also Extracting and Labeling Boundary Segments in Natural Scenes.
BibRef
8312
Buxton, B.F.,
Buxton, H.,
Monocular Depth Perception from Optical Flow by Space Time
Signal Processing,
RoyalP(B-218), 1983, pp. 27-47.
BibRef
8300
Bergmann, H.C.,
Analysis of Different Displacement Estimation Algorithms
for Digital Television Signals,
ISPDSA83(215-234).
BibRef
8300
Adiv, G.[Gilad],
Determining 3-D Motion and Structure from
Optical Flow Generated by Several Moving Objects,
PAMI(7), No. 4, July 1985, pp. 384-401.
BibRef
8507
Earlier:
DARPA84(113-129).
BibRef
Earlier:
Recovering 2-D Motion Parameters in
Scenes Containing Multiple Moving Objects,
DARPA83(285-292).
BibRef
And:
COINSTR 83-11, May, 1983.
BibRef
Earlier:
Recovering Motion Parameters in
Scenes Containing Multiple Moving Objects,
CVPR83(399-400).
Hough. Use of the generalized Hough transform to get the several moving objects.
Segment objects based on 2D motion parameters.
See also Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Flow Field.
BibRef
Williams, L.R., and
Hanson, A.R.,
Translating Optical Flow into Token Matches and Depth from Looming,
ICCV88(441-448).
IEEE Abstract. IEEE Top Reference.
BibRef
8800
And:
COINSTR 88-68, UMass.
BibRef
And:
Translating Optical Flow into Token Matches,
DARPA88(970-980).
BibRef
And:
Depth from Looming Structure,
DARPA88(1047-1051).
Using optical flow to aid the matching of line tokens in motion pairs.
BibRef
Castelow, D.A.,
Murray, D.W.,
Scott, G.L.,
Buxton, B.F.,
Matching Canny Edgels to Compute the Principal Components of
Optic Flow,
IVC(6), No. 2, May 1988, pp. 129-136.
WWW Version.
BibRef
8805
Scott, G.L.,
'Four-Line' Method of Locally Estimating Optic Flow,
IVC(5), No. 2, May 1987, pp. 67-72.
WWW Version.
BibRef
8705
Earlier:
Smoothing the Optic Flow Field Under Perspective Projection,
CVPR86(504-509).
Treating the flow field as 3D
vectors (the projection is what we see) seems to help.
BibRef
Thompson, W.B.[William B.], and
Kearney, J.K.[Joseph K.],
DIDA: Dynamic Image Disparity Analysis,
TRFinal report for July 1981 to December 1982. AFWAL-TR-83-10A35.
The report on an analysis of the needs for disparity analysis
systems to be useful. Mostly obvious conclusions (as expected in a
report of this type to a government lab) but they implemented
several alternative disparity systems. The major point is that
various applications have different requirements for accuracy,
speed, and density of coverage; and that different methods can give
you some of these. The best seems to be the gradient based methods
with proper post processing to account for the variations and
constrain the output.
BibRef
8107
Thompson, W.B.,
Madarasz, R.L., and
Kearney, J.K.,
Temporal Constraints for Estimating Optic Flow,
PRIP82(252-255).
Relaxation. Computing optic flow using the constraints that disparity changes
slowly over space and over time. Temporal continuity is used in the
relaxation process (by remembering the past disparity) to restrict
future matches and resolve ambiguities. This leads to a substantial
speed up successive frames.
BibRef
8200
Thompson, W.B., and
Barnard, S.T.,
Lower-level Estimates and Interpretation of Visual Motion,
Computer(14), No. 8, August 1981, pp. 20-28.
Optical Flow, Gradient Based. How to use the low-level information in motion analysis - gradients
of motion, analysis of feature points (tracking-matching) and a
little on 3-D analysis. Optimization versus Hough transform for
solutions.
BibRef
8108
Fuh, C.S., and
Maragos, P.,
Motion Displacement Extimation Using an Affine Model for Image Matching,
OptEng(30), No. 7, July 1991, pp. 881-887.
BibRef
9107
Earlier:
Region-Based Optical Flow Estimation,
CVPR89(130-135).
IEEE Abstract. IEEE Top Reference. Determine the flow of primitive
tokens (regions) by matching. Then smooth the field. Basic.
BibRef
Fuh, C.S.,
Maragos, P.,
Vinlent, M.,
Visual Motion Correspondence by Region-Based Approaches,
ACCV93(784-789).
Region features only.
BibRef
9300
Duncan, J.H., and
Chou, T.C.,
On the Detection of Motion and the Computation of Optical Flow,
PAMI(14), No. 3, March 1992, pp. 346-352.
IEEE Abstract. IEEE Top Reference.
WWW Version.
BibRef
9203
Earlier:
Temporal Edges: The Detection of Motion and the Computation of
Optical Flow,
ICCV88(374-382).
IEEE Abstract. IEEE Top Reference. Use of spatio-temporal filter approach for optical flow.
BibRef
Cornilleau-Pérès, V.[Valérie],
Droulez, J.[Jacques],
Velocity-Based Correspondence in Stereokinetic Images,
CVGIP(58), No. 2, September 1993, pp. 137-146.
WWW Version.
BibRef
9309
Earlier:
Stereo correspondence from optic flow,
ECCV90(326-330).
WWW Version.
9004
BibRef
Shapiro, V.,
Backalov, I.,
Kavardjikov, V.,
Motion Analysis Via Interframe Point Correspondence Establishment,
IVC(13), No. 2, March 1995, pp. 111-118.
WWW Version.
BibRef
9503
Huang, Y.,
Zhuang, X.H.,
Yang, C.S.,
Two Block-Based Motion Compensation Methods for Video Coding,
CirSysVideo(6), No. 1, February 1996, pp. 123-126.
IEEE Top Reference.
BibRef
9602
Huang, Y.[Yan],
Zhuang, X.H.[Xin-Hua],
An Adaptively Refined Block Matching Algorithm for
Motion Compensated Video Coding,
CirSysVideo(5), No. 1, February 1995, pp. 56-59.
IEEE Top Reference.
BibRef
9502
Earlier:
Motion-partitioned adaptive block matching for video compression,
ICIP95(I: 554-557).
WWW Version.
9510
BibRef
Wu, Q.X.,
A Correlation-Relaxation-Labeling Framework for Computing Optical Flow:
Template Matching from a New Perspective,
PAMI(17), No. 9, September 1995, pp. 843-853.
IEEE Abstract. IEEE Top Reference.
WWW Version.
Template Matching.
Clouds. Low contrast, large displacements, and non-rigid motions.
BibRef
9509
Chaudhury, K.,
Mehrotra, R.,
A Trajectory-Based Computational Model for Optical-Flow Estimation,
RA(11), No. 5, October 1995, pp. 733-741.
BibRef
9510
Bannehr, L.,
Rohn, M.,
Warnecke, G.,
A Functional Analytic Method to Derive Displacement Vector-Fields
from Satellite Image Sequences,
JRS(17), No. 2, January 20 1996, pp. 383-392.
BibRef
9601
Hang, H.M.,
Leonardi, R.,
Haskell, B.G.,
Schmidt, R.L.,
Bheda, H.,
Othmer, J.,
Digital HDTV Compression Using Parallel
Motion-Compensated Transform Coders,
CirSysVideo(1), No. 2, June 1991, pp. 210-221.
IEEE Top Reference.
BibRef
9106
Reusens, E.,
Joint Optimization of Representation Model and Frame Segmentation for
Generic Video Compression,
SP(46), No. 1, September 1995, pp. 105-117.
BibRef
9509
Pan, J.N.,
Shi, Y.Q.,
Shu, C.Q.,
Correlation Feedback Technique in Optical Flow Determination,
IP(7), No. 7, July 1998, pp. 1061-1067.
WWW Version.
9807 See also Unified Optical-Flow Field Approach To Motion Analysis from a Sequence of Stereo Images.
BibRef
Chen, J.L.[Jau-Ling],
Chen, P.Y.[Pei-Yin],
An efficient gray search algorithm for the estimation of motion vectors,
SMC-C(31), No. 2, May 2001, pp. 242-248.
IEEE Top Reference.
0109
BibRef
Jou, J.M.,
Chen, P.Y.,
Sun, J.M.,
The Gray Prediction Search Algorithm for Block Motion Estimation,
CirSysVideo(9), No. 6, September 1999, pp. 843.
IEEE Top Reference.
BibRef
9909
Imiya, A.[Atsushi],
Iwawaki, K.[Keisuke],
Voting method for the detection of subpixel flow field,
PRL(24), No. 1-3, January 2003, pp. 197-214.
HTML Version.
0211
See also Voting method for planarity and motion detection.
BibRef
Sun, C.M.[Chang-Ming],
Fast optical flow using 3D shortest path techniques,
IVC(20), No. 13-14, December 2002, pp. 981-991.
WWW Version.
0212
BibRef
Earlier:
Fast Optical Flow Using Cross Correlation and Shortest-Path Techniques,
DICTA99(143-148).
Shortest-Path Techniques, fast correlation
See also Circular shortest path in images. See also Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques.
BibRef
Tordoff, B.J.[Ben J.],
Murray, D.W.,
Guided-MLESAC: Faster Image Transform Estimation by Using Matching
Priors,
PAMI(27), No. 10, October 2005, pp. 1523-1535.
WWW Version.
0509
BibRef
Earlier:
Guided Sampling and Consensus for Motion Estimation,
ECCV02(I: 82 ff.).
HTML Version.
HTML Version.
0205Maximum-likelihood estimation by random sampling consensus
Build on MLESAC (
See also MLESAC: A New Robust Estimator with Application to Estimating Image Geometry. ).
BibRef
Wills, J.[Josh],
Agarwal, S.[Sameer],
Belongie, S.[Serge],
A Feature-based Approach for Dense Segmentation and Estimation of Large
Disparity Motion,
IJCV(68), No. 2, June 2006, pp. 125-143.
WWW Version.
0606
BibRef
Wills, J.[Josh],
Belongie, S.[Serge],
A Feature-Based Approach for Determining Dense Long Range
Correspondences,
ECCV04(Vol III: 170-182).
WWW Version.
0405two stage process in which a planar model is used to get an
approximation for the segmentation and the gross motion, and then a
spline is used to refine the fit.
BibRef
Roth, S.[Stefan],
Black, M.J.[Michael J.],
On the Spatial Statistics of Optical Flow,
IJCV(74), No. 1, August 2007, pp. 33-50.
WWW Version.
PDF Version.
0705
BibRef
Earlier:
ICCV05(I: 42-49).
WWW Version.
0510
Award, Marr Prize, HM. Training fields from range images and moving camera.
BibRef
Roth, S.[Stefan],
Black, M.J.[Michael J.],
Specular Flow and the Recovery of Surface Structure,
CVPR06(II: 1869-1876).
WWW Version.
0606
BibRef
Tandjung, S.S.,
Gunawan, T.,
Chong, M.,
Motion Estimation Using Adaptive Blocksize Observation Model and
Efficient Multiscale Regularization,
ICIP00(Vol II: 566-569).
IEEE Abstract. IEEE Top Reference.
0008
BibRef
Ben-Ari, R.[Rami],
Sochen, N.A.[Nir A.],
A General Framework and New Alignment Criterion for Dense Optical Flow,
CVPR06(I: 529-536).
WWW Version.
0606 See also Geometric Approach for Regularization of the Data Term in Stereo-Vision, A.
BibRef
Chapter on Optical Flow Field Computations and Use continues in
Optical Flow -- Matching Using Areas .