17.2 Optical Flow Field Computation and Analysis

Chapter Contents (Back)
Matching, Optical Flow. Optical Flow. See also Computation for Vector Fields, Flow Fields. See also Moving Image Coding, Compression: Using Vector Fields, Flow Fields.

17.2.1 Point Matching for Optical Flow Computation

Chapter Contents (Back)
Matching, Points. Optical Flow, Features.

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.], Arbib, M.A.[Michael A.],
Computing the Optic Flow: The MATCH Algorithm and Prediction,
CVGIP(24), No. 3, December 1983, pp. 271-304.
WWW Link. 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 DOI 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 Link. Relaxation algorithm at edges. BibRef 8805

Scott, G.L.,
'Four-Line' Method of Locally Estimating Optic Flow,
IVC(5), No. 2, May 1987, pp. 67-72.
WWW Link. 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 DOI 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 DOI BibRef 9203
Earlier:
Temporal Edges: The Detection of Motion and the Computation of Optical Flow,
ICCV88(374-382).
IEEE DOI 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 Link. BibRef 9309
Earlier:
Stereo correspondence from optic flow,
ECCV90(326-330).
Springer DOI 9004
Binocular flow as input for stereo correspondence. BibRef

Shapiro, V.[Vladimir], Backalov, I.[Ivailo], Kavardjiko, V.[Vassil],
Motion Analysis Via Interframe Point Correspondence Establishment,
IVC(13), No. 2, March 1995, pp. 111-118.
WWW Link. Global consistent point matches 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).
IEEE DOI 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 DOI 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.
IEEE DOI 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.
Elsevier DOI 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 Link. 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.
IEEE DOI 0509
BibRef
Earlier:
Guided Sampling and Consensus for Motion Estimation,
ECCV02(I: 82 ff.).
Springer DOI
HTML Version. 0205
Maximum-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.J.[Serge J.],
A Feature-based Approach for Dense Segmentation and Estimation of Large Disparity Motion,
IJCV(68), No. 2, June 2006, pp. 125-143.
Springer DOI 0606
BibRef

Wills, J.[Josh], Belongie, S.J.[Serge J.],
A Feature-Based Approach for Determining Dense Long Range Correspondences,
ECCV04(Vol III: 170-182).
Springer DOI 0405
two 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.
Springer DOI
PDF File. 0705
BibRef
Earlier: ICCV05(I: 42-49).
IEEE DOI 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).
IEEE DOI 0606
BibRef

Ben-Ari, R.[Rami], Sochen, N.A.[Nir A.],
A Geometric Framework and a New Criterion in Optical Flow Modeling,
JMIV(33), No. 2, February 2009, pp. xx-yy.
Springer DOI 0903
BibRef
Earlier:
A General Framework and New Alignment Criterion for Dense Optical Flow,
CVPR06(I: 529-536).
IEEE DOI 0606
See also Geometric Approach for Regularization of the Data Term in Stereo-Vision, A. BibRef

Keren, D.[Daniel],
A Probabilistic Method for Point Matching in the Presence of Noise and Degeneracy,
JMIV(33), No. 3, March 2009, pp. xx-yy.
Springer DOI 0903
Bayesian method to eliminate noise. BibRef

Yang, G.[Gaobo], Chen, W.W.[Wei-Wei], Wang, X.J.[Xiao Jing], Zhang, Z.Y.[Zhao-Yang],
Dense Estimation of Optical Flow Field Within the Mpeg-2 Compressed Domain,
IJIG(9), No. 3, July 2009, pp. 435-448.
DOI Link 0911
BibRef

Yang, G.[Gaobo], Chen, W.W.[Wei-Wei], Zhou, Q.[Qiya], Zhang, Z.Y.[Zhao-Yang],
Optical flow approximation based motion object extraction for MPEG-2 video stream,
RealTimeIP(4), No. 4, November 2009, pp. xx-yy.
Springer DOI 0911
BibRef

Brox, T.[Thomas], Malik, J.[Jitendra],
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation,
PAMI(33), No. 3, March 2011, pp. 500-513.
IEEE DOI 1102
Integrate correspondences from descriptor matching into a variational approach to gain the benefits of both. BibRef

Brox, T.[Thomas], Bregler, C.[Christoph], Malik, J.[Jitendra],
Large displacement optical flow,
CVPR09(41-48).
IEEE DOI 0906
BibRef

Chen, W.[Wei], Mied, R.P.[Richard P.],
Optical flow estimation for motion-compensated compression,
IVC(31), No. 3, March 2013, pp. 275-289.
Elsevier DOI 1303
Optical flow; Optical flow determination; Optical flow estimation; Displacement estimation; Velocity estimation; Motion compensation; Motion-compensated prediction; Motion-compensated interpolation; Motion-Compensated Compression BibRef

Mozerov, M.G.,
Constrained Optical Flow Estimation as a Matching Problem,
IP(22), No. 5, May 2013, pp. 2044-2055.
IEEE DOI 1304
BibRef

Conze, P.H.[Pierre-Henri], Robert, P.[Philippe], Crivelli, T.[Tomás], Morin, L.[Luce],
Multi-reference combinatorial strategy towards longer long-term dense motion estimation,
CVIU(150), No. 1, 2016, pp. 66-80.
Elsevier DOI 1608
BibRef
Earlier: A1, A3, A2, A4:
Dense motion estimation between distant frames: Combinatorial multi-step integration and statistical selection,
ICIP13(3860-3864)
IEEE DOI 1402
Long-term motion estimation. dense point matching BibRef

Fitzner, D.[Daniel], Sester, M.[Monika],
Field Motion Estimation with a Geosensor Network,
IJGI(5), No. 10, 2016, pp. 175.
DOI Link 1610
For determining various geo values. BibRef

Braux-Zin, J.[Jim], Dupont, R.[Romain], Bartoli, A.E.[Adrien E.], Tamaazousti, M.[Mohamed],
EasyFlow: Increasing the Convergence Basin of Variational Image Matching with a Feature-Based Cost,
IET-CV(11), No. 2, March 2017, pp. 122-134.
DOI Link 1703
BibRef


Caetano, C., dos Santos, J.A., Schwartz, W.R.,
Optical Flow Co-occurrence Matrices: A novel spatiotemporal feature descriptor,
ICPR16(1947-1952)
IEEE DOI 1705
Computer vision, Feature extraction, Image motion analysis, Optical distortion, Optical imaging, Optical variables measurement, Spatiotemporal phenomena BibRef

Bai, M.[Min], Luo, W.J.[Wen-Jie], Kundu, K.[Kaustav], Urtasun, R.[Raquel],
Exploiting Semantic Information and Deep Matching for Optical Flow,
ECCV16(VI: 154-170).
Springer DOI 1611
BibRef

Kim, S., Min, D., Sohn, K.,
ANCC flow: Adaptive normalized cross-correlation with evolving guidance aggregation for dense correspondence estimation,
ICIP16(3454-3458)
IEEE DOI 1610
Computational modeling BibRef

Xiang, J., Li, Z., Blaauw, D., Kim, H.S., Chakrabarti, C.,
Low complexity optical flow using neighbor-guided semi-global matching,
ICIP16(4483-4487)
IEEE DOI 1610
Complexity theory BibRef

Bailer, C., Taetz, B., Stricker, D.,
Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation,
ICCV15(4015-4023)
IEEE DOI 1602
Adaptive optics BibRef

Baghaie, A.[Ahmadreza], D'Souza, R.M.[Roshan M.], Yu, Z.Y.[Ze-Yun],
Dense Correspondence and Optical Flow Estimation Using Gabor, Schmid and Steerable Descriptors,
ISVC15(I: 406-415).
Springer DOI 1601
BibRef

Verma, N.K., Gunesh, D.E., Rao, G.S.S.S., Mishra, A.,
High Accuracy Optical Flow Based Future Image Frame Predictor Model,
AIPR15(1-6)
IEEE DOI 1605
edge detection BibRef

Verma, N.K., Mishra, A.,
Large displacement optical flow based image predictor model,
AIPR14(1-7)
IEEE DOI 1504
edge detection BibRef

Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
Sparse Flow: Sparse Matching for Small to Large Displacement Optical Flow,
WACV15(1100-1106)
IEEE DOI 1503
Computer vision BibRef

Michielin, F., Calvagno, G., Sartor, P., Emmerich, T., Unruh, C., Erdler, O.,
A parallel true motion estimation method based on binarized cross correlation,
ICIP14(1198-1202)
IEEE DOI 1502
Computational efficiency BibRef

Yang, H.S.[Hong-Sheng], Lin, W.Y.[Wen-Yan], Lu, J.B.[Jiang-Bo],
DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences,
CVPR14(3406-3413)
IEEE DOI 1409
BibRef

Hontani, H.[Hidekata], Oishi, G.[Go], Kitagawa, T.[Tomohiro],
Local Estimation of High Velocity Optical Flow with Correlation Image Sensor,
ECCV14(III: 235-249).
Springer DOI 1408
BibRef

Qiu, W.C.[Wei-Chao], Wang, X.G.[Xing-Gang], Bai, X.[Xiang], Yuille, A.L.[Alan L.], Tu, Z.W.[Zhuo-Wen],
Scale-Space SIFT flow,
WACV14(1112-1119)
IEEE DOI 1406
Computer vision BibRef

Revaud, J.[Jérôme], Weinzaepfel, P.[Philippe], Harchaoui, Z.[Zaid], Schmid, C.[Cordelia],
DeepMatching: Hierarchical Deformable Dense Matching,
IJCV(120), No. 3, December 2016, pp. 300-323.
Springer DOI 1609
BibRef
Earlier: A1, A2, A3, A4:
EpicFlow: Edge-preserving interpolation of correspondences for optical flow,
CVPR15(1164-1172)
IEEE DOI 1510
BibRef
Earlier: A2, A1, A3, A4:
DeepFlow: Large Displacement Optical Flow with Deep Matching,
ICCV13(1385-1392)
IEEE DOI 1403
deep convolutional networks BibRef

Kim, T.H.[Tae Hyun], Lee, H.S.[Hee Seok], Lee, K.M.[Kyoung Mu],
Optical Flow via Locally Adaptive Fusion of Complementary Data Costs,
ICCV13(3344-3351)
IEEE DOI 1403
BibRef

Kato, T.[Tomoya], Itoh, H.[Hayato], Imiya, A.[Atsushi],
Optical Flow Computation with Locally Quadratic Assumption,
CAIP15(I:223-234).
Springer DOI 1511
BibRef

Itoh, H.[Hayato], Inagaki, S.[Shun], Fan, M.Y.[Ming-Ying], Imiya, A.[Atsushi], Kawamoto, K.[Kazuhiko], Sakai, T.[Tomoya],
Local Affine Optical Flow Computation,
PSIVTWS13(203-215).
Springer DOI 1402
BibRef

Wang, B.T.[Bo-Tao], Zhu, Q.X.[Qing-Xiang], Xiong, H.K.[Hong-Kai],
Optimization of variational methods via motion-based weight selection and keypoint matching,
VCIP12(1-6).
IEEE DOI 1302
BibRef

Garrigues, M.[Matthieu], Manzanera, A.[Antoine],
Real Time Semi-dense Point Tracking,
ICIAR12(I: 245-252).
Springer DOI 1206
BibRef

Ummenhofer, B.[Benjamin],
Large Displacement Optical Flow for Volumetric Image Sequences,
DAGM11(432-437).
Springer DOI 1109
BibRef

Song, H.J.[Hua-Jun], Shen, M.L.[Mei-Li],
Optic Flow Target Tracking Method Based on Corner Detection,
CISP09(1-5).
IEEE DOI 0910
BibRef

Smith, T.M.A.[Timothy M. A.], Redmill, D.W.[David W.], Canagarajah, C.N.[C. Nishan], Bull, D.R.[David R.],
A framework for dense optical flow from multiple sparse hypotheses,
ICIP08(837-840).
IEEE DOI 0810
BibRef

Liu, C.[Ce], Yuen, J.[Jenny], Torralba, A.[Antonio],
SIFT Flow: Dense Correspondence across Scenes and Its Applications,
PAMI(33), No. 1, January 2011, pp. 978-994.
IEEE DOI 1104
Matching dense SIFT features between 2 views BibRef

Freeman, W.T.[William T.], Torralba, A.[Antonio], Yuen, J.[Jenny], Liu, C.[Ce],
SIFT Flow: Dense Correspondence across Scenes and its Applications R,
CSAIL(TR-2010-024). 2010-05-08
WWW Link. 1101
Matching dense SIFT features between 2 views BibRef

Liu, C.[Ce], Yuen, J.[Jenny], Torralba, A.[Antonio], Sivic, J.[Josef], Freeman, W.T.[William T.],
SIFT Flow: Dense Correspondence across Different Scenes,
ECCV08(III: 28-42).
Springer DOI 0810
BibRef

Tandjung, S.S., Soon, S.H.[Seah Hock], Kemao, Q.,
Block Motion Model for Optical Flow with Smoothness Prior Function,
ICARCV06(1-6).
IEEE DOI 0612
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 DOI 0008
BibRef

Chapter on Optical Flow Field Computations and Use continues in
Optical Flow -- Matching Using Areas .


Last update:Sep 18, 2017 at 11:34:11