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.
Elsevier DOI 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.
Elsevier DOI
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.
Elsevier DOI
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.
Elsevier DOI 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:
Elsevier DOI
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.
Elsevier DOI
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.
Elsevier DOI
0105
BibRef
Pourian, N.[Niloufar],
Nestares, O.[Oscar],
Multi-Level Optical Flow Estimation Based on Spatial Partitioning,
ICIP21(2723-2727)
IEEE DOI
2201
Deep learning, Learning systems, Interpolation, Merging,
Memory management, Estimation, Graphics processing units,
View Interpolation.
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.
Elsevier DOI
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.
Elsevier DOI
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
BibRef
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.S.[Dimitrios S.],
Mitianoudis, N.[Nikolaos],
Stathaki, T.[Tania],
Multidimensional directional steerable filters: Theory and
application to 3D flow estimation,
IVC(71), 2018, pp. 38 - 67.
Elsevier DOI
1804
BibRef
Earlier:
Multidimensional steerable filters and 3D flow estimation,
ICIP14(2012-2016)
IEEE DOI
1502
Steerable filters, Multi-dimensional signal processing,
Frequency domain, 3D flow estimation.
Decision support systems
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
BibRef
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
Xu, J.,
Ranftl, R.,
Koltun, V.[Vladlen],
Accurate Optical Flow via Direct Cost Volume Processing,
CVPR17(5807-5815)
IEEE DOI
1711
Adaptive optics, Benchmark testing, Estimation, Optical imaging,
Optical network units, Pipelines, Training
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.L.[Xiao-Lin],
Phung, S.L.[Son Lam],
Bouzerdoum, A.[Abdesselam],
Tang, V.H.[Van Ha],
Pooling-Based Feature Extraction and Coarse-to-fine Patch Matching for
Optical Flow Estimation,
ACCV18(IV:597-612).
Springer DOI
1906
BibRef
Tang, X.L.[Xiao-Lin],
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
BibRef
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.[Theo],
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
Large Displacement Optical Flow .