17.2.4 Optical Flow Field Computation -- Gradient Techniques

Chapter Contents (Back)
Gradient Techniques. Optical Flow, Gradient Based.

Nagel, H.H.[Hans-Hellmut],
Displacement Vectors Derived from Second-Order Intensity Variations in Image Sequences,
CVGIP(21), No. 1, January 1983, pp. 85-117.
WWW Link. Computing the motion of corners by studying the equations for the intensity with respect to time. This gives a closed form solution to the motion problem. Another version is in the Munich paper. This paper shows that the See also Determining Optical Flow. method is a special case of this one. This takes the gradient approaches ( See also Gradient Based Estimation of Disparity. ) to their logical conclusion. BibRef 8301

Nagel, H.H., and Enkelmann, W.,
Investigation of Second Order Greyvalue Variations to Estimate Corner Point Displacements,
ICPR82(768-773). Corner points are computed and a method of computing the displacements is given. This is one step in computing the optic flow. The displacements can be computed directly from the neighborhood averages of the differences (minimize an integral (sum) and force the math through). See also Displacement Vectors Derived from Second-Order Intensity Variations in Image Sequences. for other information. BibRef 8200

Subbarao, M.[Muralidhara],
Interpretation of Image Flow: Rigid Curved Surfaces in Motion,
IJCV(2), No. 1, June 1988, pp. 77-96.
Springer DOI BibRef 8806
Earlier:
Solution and Uniqueness of Image Flow Equations for rigid Curved Surfaces in Motion,
ICCV87(687-692). Similar to the other closed from solution papers. BibRef

Subbarao, M.[Muralidhara],
Interpretation of Image Flow: A Spatio-Temporal Approach,
PAMI(11), No. 3, March 1989, pp. 266-278.
IEEE DOI BibRef 8903
Earlier:
Interpretation of Image Motion Fields: A Spatio-Temporal Approach,
Motion86(157-165). A study of what information is there and how to get it. More equations. BibRef

Subbarao, M.,
Interpretation of Visual Motion: A Computational Study,
Los Altos: Morgan Kaufmann1988. BibRef 8800 Bookfrom his thesis. BibRef

Zhao, W.Z.[Wei-Zhao], Qi, F.H.[Fei-Hu], Yang, T.Y.[Tzay Young],
Dynamic Estimation of Optical Flow Field Using Objective Functions,
IVC(7), No. 4, November 1989, pp. 259-267.
WWW Link. BibRef 8911

Verri, A., Girosi, F., and Torre, V.,
Differential Techniques for Optical Flow,
JOSA-A(7), No 5, May 1990, pp. 912-922. BibRef 9005

de Micheli, E., Torre, V., and Uras, S.,
The Accuracy of the Computation of Optical Flow and the Recovery of Motion Parameters,
PAMI(15), No. 5, May 1993, pp. 434-447.
IEEE DOI See also Computational Approach to Motion Perception, A. Produce vector fields and recover motion parameters (time to collision) from reduced images or a single scanline near the FOE. BibRef 9305

Girosi, F., Verri, A., and Torre, V.,
Constraints for the Computation of Optical Flow,
Motion89(116-124). BibRef 8900

Verri, A., Girosi, F., and Torre, V.,
Mathematical Properties of the 2D Motion Field: From Singular Points to Motion Parameters,
Motion89(190-200). BibRef 8900

Schunck, B.G.[Brian G.],
Robust Estimation of Image Flow,
SPIE(1198), Sensor Fusion II: Human and Machine Strategies, 1989, pp. 116-127. BibRef 8900

Schunck, B.G.[Brian G.],
Image Flow: Fundamentals and Future Research,
CVPR85(560-571). (GM Research Labs) Invited talk. Discusses the current view of image flow analysis, and some of the past problems. BibRef 8500

Schunck, B.G.[Brian G.],
Image Flow Continuity Equations for Motion and Density,
Motion86(89-94). A continuing attempt to understand flow, either in the image values or in feature density. BibRef 8600

Schunck, B.G.[Brian G.],
The Image Flow Constraint Equation,
CVGIP(35), No. 1, July 1986, pp. 20-46.
WWW Link. BibRef 8607
Earlier:
The Motion Constraint Equation for Optical Flow,
ICPR84(20-22). A cleaner discussion than his earlier papers of the equations, with some discussion of boundaries and discontinuities. BibRef

Heeger, D.J.,
Optical Flow Using Spatiotemporal Filters,
IJCV(1), No. 4, January 1988, pp. 279-302).
Springer DOI BibRef 8801
Earlier: ICCV87(181-190). Award, Marr Prize. BibRef
And:
Model for the Extraction of Image Flow,
JOSA-A(2), No. 2, 1987, pp. 1455-1471. BibRef
And:
A Model for the Extraction of Image Flow,
ICCV87(181-190). BibRef
Earlier:
Depth and Flow from Motion Energy,
AAAI-86(657-663). Based on a biological model of motion perception, a set of filters are applied to the image. BibRef

Chen, H.J., Shirai, Y., and Asada, M.,
Obtaining Optical Flow with Multi-Orientation Filters,
CVPR93(736-737).
IEEE DOI BibRef 9300

Weber, J.W.[Joseph W.], Malik, J.[Jitendra],
Robust Computation of Optical-Flow in a Multiscale Differential Framework,
IJCV(14), No. 1, January 1995, pp. 67-81.
Springer DOI BibRef 9501
Earlier: ICCV93(12-20).
IEEE DOI BibRef
And: UCBCSD-92-709, 1992. First use a set of filters and combine the different estimates. BibRef

Adelson, E.H., and Bergen, J.R.[James R.],
Spatiotemporal Energy Models for the Perception of Motion,
JOSA-A(2), No. 2, 1985, pp. 284-299. BibRef 8500
And:
The Extraction of Spatio-Temporal Energy in Human and Machine Vision,
Motion86(151-155). BibRef

Hadani, I., and Barta, E.,
The Hybrid Constraint Equation for Motion Extraction,
IVC(7), No. 3, August 1989, pp. 217-224.
WWW Link. Apply constraint to Fourier transform of the image. BibRef 8908

Colombo, C., del Bimbo, A., Santini, S.,
Optical-Flow by Nonlinear Relaxation,
PR(28), No. 7, July 1995, pp. 977-988. BibRef 9507
And:
WWW Link.
Optical-Flow Through Relaxation in the Velocity Space,
PRL(15), No. 4, April 1994, pp. 373-382. Optical flow from relaxation. BibRef

Taalebinezhaad, M.A.,
Direct Recovery of Motion and Shape in the General Case by Fixation,
PAMI(14), No. 8, August 1992, pp. 847-853.
IEEE DOI BibRef 9208
Earlier: ICCV90(451-455).
IEEE DOI BibRef
And: MIT AI Memo-1187, March 1990. BibRef
And:
Partial Implementation of the Fixation Method on Real Images: Direct Recovery of Motion and Shape in the General Case,
CVPR91(400-405).
IEEE DOI BibRef
And:
FIXATION: A Direct Method for Recovery of Motion and Shape in the General Case,
DARPA90(284-291). Gradient approach to OF computation. BibRef

Taalebinezhaad, M.A.[M. Ali],
Robot Motion Vision by Fixation,
MIT AI-TR-1384, September 1992.
WWW Link. BibRef 9209

Taalebinezhaad, M.A.[M. Ali],
Autonomous Fixation,
CVPR92(744-747).
IEEE DOI BibRef 9200
And:
Autonomous Motion Vision,
ICPR92(I:232-235).
IEEE DOI BibRef
And:
Towards Autonomous Motion Vision,
MIT AI Memo-1334, April 1992.
WWW Link. BibRef

Taalebinezhaad, M.A.[M. Ali],
Visual Tracking,
MIT AI Memo-1382, October 1992.
WWW Link. BibRef 9210

Efstratiadis, S.N., Katsaggelos, A.K.,
Nonstationary AR modeling and constrained recursive estimation of the displacement field,
CirSysVideo(2), No. 4, December 1992, pp. 334-346.
IEEE Top Reference. 0206
BibRef

Brailean, J.C., Katsaggelos, A.K.,
A Recursive Nonstationary Map Displacement Vector Field Estimation Algorithm,
IP(4), No. 4, April 1995, pp. 416-429.
IEEE DOI BibRef 9504
And:
Recursive MAP Displacement Field Estimation and Its Applications,
ICIP96(I: 917-920).
IEEE DOI BibRef
And:
Noise robust spatial gradient estimation for use in displacement estimation,
ICIP95(I: 211-214).
IEEE DOI 9510
BibRef

Tistarelli, M.,
Multiple Constraints to Compute Optical-Flow,
PAMI(18), No. 12, December 1996, pp. 1243-1250.
IEEE DOI 9701
Differential constraints correspond to feature tracking. Considers multiple points and a constant acceleration motion model. BibRef

Tistarelli, M.,
Computation of Optical Flow and Its Derivatives from Local Differential Constraints,
SCV95(19-24).
IEEE DOI U. of Genoa. BibRef 9500

Tistarelli, M.[Massimo],
Computation of Coherent Optical Flow by Using Multiple Constraints,
ICCV95(263-268).
IEEE DOI BibRef 9500
Earlier:
Multiple Constraints for Optical Flow,
ECCV94(A:61-70).
Springer DOI BibRef

Bainbridge-Smith, A., Lane, R.G.,
Determining Optical-Flow Using a Differential Method,
IVC(15), No. 1, January 1997, pp. 11-22.
WWW Link. 9702
Conclusion is the Lucas-Kanade ( See also Iterative Image Registration Technique with an Application to Stereo Vision, An. ) is best generalized second order method. BibRef

Brandt, J.W.,
Improved Accuracy in Gradient Based Optical Flow Estimation,
IJCV(25), No. 1, October 1997, pp. 5-22.
DOI Link 9710
BibRef
Earlier:
Finite-differencing errors in gradient-based optical flow estimation,
ICIP94(II: 775-779).
IEEE DOI 9411
BibRef

Elad, M., Feuer, A.,
Recursive Optical Flow Estimation: Adaptive Filtering Approach,
JVCIR(9), 1998, pp. 119-138. BibRef 9800

Christmas, W.J.,
Filtering Requirements for Gradient-Based Optical Flow Measurement,
IP(9), No. 10, October 2000, pp. 1817-1820.
IEEE DOI 0010
BibRef
Earlier:
Spatial Filtering Requirements for Gradient-based Optical Flow Measurement,
BMVC98(xx-yy). BibRef

Lai, S.H.[Shang-Hong], Vemuri, B.C.[Baba C.],
Reliable and Efficient Computation of Optical Flow,
IJCV(29), No. 2, August-September 1998, pp. 87-105.
DOI Link 0010
BibRef
Earlier:
Robust and Efficient Algorithms for Optical Flow Computation,
SCV95(455-460).
IEEE DOI University of Florida. A gradient-based approach and a SSD approach. See also Efficient hybrid search for visual reconstruction problems. BibRef

Simoncelli, E.P.,
Bayesian Multi-scale Differential Optical Flow,
HCVA99(II: 397-422). Coarse to fine, Kalman Filter.
HTML Version. BibRef 9900
Earlier: WIMSP93(128-129). BibRef
Coarse-to-fine Estimation of Visual Motion,

PS File. BibRef

Nestares, O., Navarro, R.,
Probabilistic estimation of optical flow in multiple band-pass directional channels,
IVC(19), No. 6, April 2001, pp. 339-351.
WWW Link. 0105
BibRef

Haussecker, H.W.[Horst W.], Fleet, D.J.[David J.],
Computing Optical Flow with Physical Models of Brightness Variation,
PAMI(23), No. 6, June 2001, pp. 661-673.
IEEE DOI 0106
BibRef
Earlier: CVPR00(II: 760-767).
IEEE DOI 0005
Do not rely on brightness constancy. Use a model of how it will vary. BibRef

Haussecker, H.W.[Horst W.],
Simultaneous Estimation of Optical Flow and Heat Transport in Infrared Image Sequences,
CVBVS00(85).
IEEE DOI 0006
BibRef

Gautama, T., van Hulle, M.M.[Marc M.], M. (2002).
A Phase-based Approach to the Estimation of the Optical Flow Field Using Spatial Filtering,
TNN(13), No. 5, 2002, pp. 1127-1136. BibRef 0200

Arredondo, M.A., Lebart, K., Lane, D.,
Optical flow using textures,
PRL(25), No. 4, March 2004, pp. 449-457.
WWW Link. 0402
Combine results of texture and intensity. BibRef

Burgi, P.Y.[Pierre-Yves],
Motion estimation based on the direction of intensity gradient,
IVC(22), No. 8, August 2004, pp. 637-653.
WWW Link. 0405
constraint based on distribution of gradient directions. BibRef

Elad, M.[Michael], Teo, P.[Patrick], Hel-Or, Y.[Yacov],
On the Design of Filters for Gradient-Based Motion Estimation,
JMIV(23), No. 3, November 2005, pp. 345-365.
Springer DOI 0510
BibRef
Earlier:
Optimal Filters for Gradient-based Motion Estimation,
ICCV99(559-565).
IEEE DOI BibRef

Lu, Q.H.[Qing-Hua], Zhang, X.M.[Xian-Min],
Robust multiscale algorithms for gradient-based motion estimation,
IJIST(17), No. 6, 2007, pp. 333-340.
DOI Link 0804
BibRef

Wietzke, L.[Lennart], Sommer, G.[Gerald],
The Signal Multi-Vector,
JMIV(37), No. 2, June 2010, pp. xx-yy.
Springer DOI 1003
BibRef
Earlier:
The Conformal Monogenic Signal,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Wietzke, L.[Lennart], Sommer, G.[Gerald], Fleischmann, O.[Oliver],
The geometry of 2D image signals,
CVPR09(1690-1697).
IEEE DOI 0906
BibRef

Wietzke, L.[Lennart], Fleischmann, O.[Oliver], Sedlazeck, A.[Anne], Sommer, G.[Gerald],
Local Structure Analysis by Isotropic Hilbert Transforms,
DAGM10(131-140).
Springer DOI 1009
BibRef

Fleischmann, O.[Oliver], Wietzke, L.[Lennart], Sommer, G.[Gerald],
Image Analysis by Conformal Embedding,
JMIV(40), No. 3, July 2011, pp. 305-325.
WWW Link. 1103
BibRef
Earlier: A2, A1, A3:
2D Image Analysis by Generalized Hilbert Transforms in Conformal Space,
ECCV08(II: 638-649).
Springer DOI 0810
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Wietzke, L.[Lennart], Sommer, G.[Gerald], Schmaltz, C.[Christian], Weickert, J.[Joachim],
Differential Geometry of Monogenic Signal Representations,
RobVis08(454-465).
Springer DOI 0802
BibRef

Zang, D.[Di], Wietzke, L.[Lennart], Schmaltz, C.[Christian], Sommer, G.[Gerald],
Dense Optical Flow Estimation from the Monogenic Curvature Tensor,
SSVM07(239-250).
Springer DOI 0705
BibRef

Koeser, K.[Kevin], Perwass, C.[Christian], Sommer, G.[Gerald],
Dense Optic Flow with a Bayesian Occlusion Model,
SCVMA04(127-139).
Springer DOI 0405
BibRef

Lee, J.H.[Ju Hwan], Park, S.Y.[Sung Yun], Kim, S.J.[Sung Jae], Kim, S.M.[Sung Min],
The Study of Phase-Based Optical Flow Technique Using an Adaptive Bilateral Filter,
IEICE(E95-D), No. 2, February 2012, pp. 658-667.
WWW Link. 1202
BibRef

Xu, L.[Li], Jia, J.Y.[Jia-Ya], Matsushita, Y.[Yasuyuki],
Motion Detail Preserving Optical Flow Estimation,
PAMI(34), No. 9, September 2012, pp. 1744-1757.
IEEE DOI 1208
BibRef
Earlier: CVPR10(1293-1300).
IEEE DOI Video of talk:
WWW Link. 1006
BibRef

Xu, L.[Li], Chen, J.N.[Jia-Ning], Jia, J.Y.[Jia-Ya],
A Segmentation Based Variational Model for Accurate Optical Flow Estimation,
ECCV08(I: 671-684).
Springer DOI 0810
BibRef

Xu, L.[Li], Jia, J.Y.[Jia-Ya],
Two-Phase Kernel Estimation for Robust Motion Deblurring,
ECCV10(I: 157-170).
Springer DOI 1009
BibRef

Xu, L.[Li], Dai, Z.L.[Zhen-Long], Jia, J.Y.[Jia-Ya],
Scale Invariant Optical Flow,
ECCV12(II: 385-399).
Springer DOI 1210
BibRef

Rashwan, H.A.[Hatem A.], Puig, D.[Domenec], Garcia, M.A.[Miguel Angel],
Improving the robustness of variational optical flow through tensor voting,
CVIU(116), No. 9, September 2012, pp. 953-966.
Elsevier DOI 1208
BibRef
Earlier:
On improving the robustness of differential optical flow,
ARTEMIS11(876-881).
IEEE DOI 1201
Variational optical flow; Anisotropic filtering; Tensor voting BibRef

Rashwan, H.A.[Hatem A.], Garcia, M.A.[Miguel Angel], Puig, D.[Domenec],
Variational Optical Flow Estimation Based on Stick Tensor Voting,
IP(22), No. 7, 2013, pp. 2589-2599.
IEEE DOI flow discontinuities; flow field estimation;optimization process; variational optical flow 1307
BibRef

Ren, D.W.[Dong-Wei], Zuo, W.M.[Wang-Meng], Zhao, X.F.[Xiao-Fei], Lin, Z.C.[Zhou-Chen], Zhang, D.[David],
Fast gradient vector flow computation based on augmented Lagrangian method,
PRL(34), No. 2, 15 January 2013, pp. 219-225.
Elsevier DOI 1212
Gradient vector flow; Convex optimization; Augmented Lagrangian method; Fast Fourier transform; Multiresolution method BibRef

Li, J.F.[Jian-Feng], Zuo, W.M.[Wang-Meng], Zhao, X.F.[Xiao-Fei], Zhang, D.[David],
An augmented Lagrangian method for fast gradient vector flow computation,
ICIP11(1525-1528).
IEEE DOI 1201
BibRef

Lee, K.J.[Kyong Joon], Yun, I.D.[Il Dong], Lee, S.U.[Sang Uk],
Adaptive large window correlation for optical flow estimation with discrete optimization,
IVC(31), No. 9, 2013, pp. 631-639.
Elsevier DOI 1307
Window correlation BibRef

Lee, K.J.[Kyong Joon], Yun, I.D.[Il Dong],
Occlusion detecting window matching scheme for optical flow estimation with discrete optimization,
PRL(89), No. 1, 2017, pp. 73-80.
Elsevier DOI 1704
Optical flow BibRef

Lee, K.J.[Kyong Joon], Kwon, D.J.[Dong-Jin], Yun, I.D.[Il Dong], Lee, S.U.[Sang Uk],
Optical flow estimation with adaptive convolution kernel prior on discrete framework,
CVPR10(2504-2511).
IEEE DOI 1006
BibRef

Tu, Z.G.[Zhi-Gang], van der Aa, N.[Nico], van Gemeren, C.[Coert], Veltkamp, R.C.[Remco C.],
A combined post-filtering method to improve accuracy of variational optical flow estimation,
PR(47), No. 5, 2014, pp. 1926-1940.
Elsevier DOI 1402
Optical flow BibRef

Tu, Z.G.[Zhi-Gang], Poppe, R.[Ronald], Veltkamp, R.C.[Remco C.],
Weighted local intensity fusion method for variational optical flow estimation,
PR(50), No. 1, 2016, pp. 223-232.
Elsevier DOI 1512
Optical flow BibRef

Tu, Z.G.[Zhi-Gang], Xie, W.[Wei], Cao, J.[Jun], van Gemeren, C.[Coert], Poppe, R.[Ronald], Veltkamp, R.C.[Remco C.],
Variational method for joint optical flow estimation and edge-aware image restoration,
PR(65), No. 1, 2017, pp. 11-25.
Elsevier DOI 1702
Optical flow BibRef

Li, Y., Zhu, E., Zhao, J., Yin, J., Zhao, X.,
A Fast Simple Optical Flow Computation Approach Based on the 3-D Gradient,
CirSysVideo(24), No. 5, May 2014, pp. 842-853.
IEEE DOI 1405
Kernel BibRef

Mohamed, M.A.[Mahmoud A.], Rashwan, H.A.[Hatem A.], Mertsching, B.[Bärbel], García, M.A.[Miguel Angel], Puig, D.,
Illumination-Robust Optical Flow Using a Local Directional Pattern,
CirSysVideo(24), No. 9, September 2014, pp. 1499-1508.
IEEE DOI 1410
BibRef
Earlier: A2, A1, A4, A3, Only:
Illumination Robust Optical Flow Model Based on Histogram of Oriented Gradients,
GCPR13(354-363).
Springer DOI 1311
feature extraction BibRef

Monzón, N.[Nelson], Salgado, A.[Agustín], Sánchez, J.[Javier],
Regularization Strategies for Discontinuity-Preserving Optical Flow Methods,
IP(25), No. 4, April 2016, pp. 1580-1591.
IEEE DOI 1604
BibRef
Earlier: A1, A3, A2:
Efficient Mechanism for Discontinuity Preserving in Optical Flow Methods,
ICISP14(425-432).
Springer DOI 1406
image motion analysis BibRef

Monzón, N.[Nelson], Salgado, A.[Agustín], Sánchez, J.[Javier],
Robust Discontinuity Preserving Optical Flow Methods,
IPOL(6), 2016, pp. 165-182.
DOI Link 1609
Code, Optical Flow. BibRef

Sánchez, J.[Javier], Salgado, A.[Agustín], Monzón, N.[Nelson],
Computing inverse optical flow,
PRL(52), No. 1, 2015, pp. 32-39.
Elsevier DOI 1412
Inverse optical flow BibRef
Earlier: A1, A2, A3:
An Efficient Algorithm for Estimating the Inverse Optical Flow,
IbPRIA13(390-397).
Springer DOI 1307
BibRef

Sánchez, J.[Javier],
The Inverse Compositional Algorithm for Parametric Registration,
IPOL(6), 2016, pp. 212-232.
DOI Link 1609
Code, Optical Flow. BibRef

Salgado, A.[Agustín], Sánchez, J.[Javier],
A Temporal Regularizer for Large Optical Flow Estimation,
ICIP06(1233-1236). 0610

IEEE DOI BibRef

Choi, S.H.[Sung-Hwan], Min, D.B.[Dong-Bo], Ham, B.[Bumsub], Sohn, K.H.[Kwang-Hoon],
Unsupervised Texture Flow Estimation Using Appearance-Space Clustering and Correspondence,
IP(24), No. 11, November 2015, pp. 3652-3665.
IEEE DOI 1509
BibRef
Earlier: A1, A2, A4, Only:
Randomized texture flow estimation using visual similarity,
ICIP14(4662-4666)
IEEE DOI 1502
estimation theory. Estimation BibRef

Fortun, D.[Denis], Bouthemy, P.[Patrick], Kervrann, C.[Charles],
Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow,
CVIU(145), No. 1, 2016, pp. 81-94.
Elsevier DOI 1604
BibRef
Earlier: A1, A3, Only:
Semi-local variational optical flow estimation,
ICIP12(77-80).
IEEE DOI 1302
Optical flow BibRef


Alexiadis, D.[Dimitrios], Mitianoudis, N.[Nikolaos], Stathaki, T.[Tania],
Multidimensional steerable filters and 3D flow estimation,
ICIP14(2012-2016)
IEEE DOI 1502
Decision support systems BibRef

Wang, C.[Chen], Lu, Z.Q.[Zong-Qing], Liao, Q.M.[Qing-Min],
Local texture based optical flow for complex brightness variations,
ICIP14(1972-1976)
IEEE DOI 1502
Adaptive optics BibRef

Arora, C.[Chetan], Werman, M.[Michael],
Optical flow for non Lambertian surfaces by cancelling illuminant chromaticity,
ICIP14(1977-1981)
IEEE DOI 1502
Adaptive optics BibRef

Sevilla-Lara, L.[Laura], Sun, D.[Deqing], Jampani, V., Black, M.J.[Michael J.],
Optical Flow with Semantic Segmentation and Localized Layers,
CVPR16(3889-3898)
IEEE DOI 1612
BibRef

Sevilla-Lara, L.[Laura], Sun, D.[Deqing], Learned-Miller, E.G.[Erik G.], Black, M.J.[Michael J.],
Optical Flow Estimation with Channel Constancy,
ECCV14(I: 423-438).
Springer DOI 1408
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Sabater, N.[Neus], Leprince, S.[Sebastien], Avouac, J.P.[Jean-Philippe],
Contrast Invariant and Affine sub-pixel Optical Flow,
ICIP12(53-56).
IEEE DOI 1302
BibRef

Chen, Q., Koltun, V.[Vladlen],
Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids,
CVPR16(4706-4714)
IEEE DOI 1612
BibRef

Krähenbühl, P.[Philipp], Koltun, V.[Vladlen],
Efficient Nonlocal Regularization for Optical Flow,
ECCV12(I: 356-369).
Springer DOI 1210
BibRef

Tang, X., Bouzerdoum, A.[Abdesselam], Phung, S.L.[Son Lam],
Video Classification Based on Spatial Gradient and Optical Flow Descriptors,
DICTA15(1-8)
IEEE DOI 1603
feature extraction BibRef

Nawaz, M.W.[Muhammad Wasim], Bouzerdoum, A.[Abdesselam], Phung, S.L.[Son Lam],
Optical flow estimation using sparse gradient representation,
ICIP11(2681-2684).
IEEE DOI 1201
BibRef

Meilland, M.[Maxime], Comport, A.I.[Andrew I.], Rives, P.[Patrick],
Real-time Dense Visual Tracking under Large Lighting Variations,
BMVC11(xx-yy).
HTML Version. 1110
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Müller, T.[Thomas], Rabe, C.[Clemens], Rannacher, J.[Jens], Franke, U.[Uwe], Mester, R.[Rudolf],
Illumination-Robust Dense Optical Flow Using Census Signatures,
DAGM11(236-245).
Springer DOI 1109
BibRef

Hoeffken, M.[Matthias], Oberhoff, D.[Daniel], Kolesnik, M.[Marina],
Temporal Prediction and Spatial Regularization in Differential Optical Flow,
ACIVS11(576-585).
Springer DOI 1108
BibRef

Ulman, V.[Vladimír],
Improving Accuracy of Optical Flow of Heeger's Original Method on Biomedical Images,
ICIAR10(I: 263-273).
Springer DOI 1006
BibRef

Lempitsky, V.[Victor], Roth, S.[Stefan], Rother, C.[Carsten],
FusionFlow: Discrete-continuous optimization for optical flow estimation,
CVPR08(1-8).
IEEE DOI 0806
See also Fusion Moves for Markov Random Field Optimization. BibRef

Cofaru, C.[Corneliu], Philips, W.[Wilfried], Van Paepegem, W.[Wim],
Gradient-Based Optical Flow for Sub-Pixel Registration of Speckle Image Sequences Using a Spatial/Temporal Postprocessing Technique,
ICIP08(841-844).
IEEE DOI 0810
BibRef

Guo, X.X.[Xiao-Xin], Xu, Z.W.[Zhi-Wen], Feng, Y.P.[Yue-Ping], Wang, Y.X.[Yun-Xiao], Wang, Z.X.[Zheng-Xuan],
Optical Flow Computation with Fourth Order Partial Differential Equations,
SSPR06(279-286).
Springer DOI 0608
BibRef

Lee, T.[Teahyung], Anderson, D.V.,
Checkerboard-Type Filtering for a Low-Power Gradient-Based Optical Flow Estimation System,
ICIP06(3285-3288). 0610

IEEE DOI BibRef

van de Weijer, J., Gevers, T.,
Robust optical flow from photometric invariants,
ICIP04(III: 1835-1838).
IEEE DOI 0505
See also Edge and Corner Detection by Photometric Quasi-Invariants. BibRef

Ng, L.,
Selecting the Neighbourhood Size, Shape, Weights and Model Order in Optical Flow Estimation,
ICIP00(Vol III: 600-603).
IEEE DOI 0008
BibRef

Ohta, N.[Naoya],
Optical flow detection using a general noise model for gradient constraint,
CAIP97(669-676).
Springer DOI 9709
BibRef

Niessen, W.J., Duncan, J.S., Florack, L.M.J., ter Haar Romeny, B.M., Viergever, M.A.,
Spatiotemporal Operators and Optic Flow,
PBMCV95(SESSION 3) BibRef 9500

Jiang, M., Wu, Z.Q., Wu, Y.S.,
Recursively Estimating Optical Flow from a Noisy Image Sequence,
ICPR88(II: 888-890).
IEEE DOI BibRef 8800

Liu, W., Liu, J., Wan, F.,
The Theorem Analysis on Optical Flow Estimation from Three Frames of Image Sequences,
ICPR88(II: 1103-1105).
IEEE DOI BibRef 8800

Tretiak, O.J., Pastor, L.,
Velocity Estimation from Image Sequences with Second Order Differential Operators,
ICPR84(16-19). BibRef 8400

Chapter on Optical Flow Field Computations and Use continues in
Optical Flow for Simple Motions .


Last update:Sep 25, 2017 at 16:36:46