Barron, J.L.,
Fleet, D.J., and
Beauchemin, S.S.,
Performance of Optical Flow Techniques,
IJCV(12), No. 1, February 1994, pp. 43-77.
Springer DOI
HTML Version.
WWW Link.
BibRef
9402
And:
Add:
Burkitt, T.A.,
CVPR92(236-242).
IEEE DOI
Code, Optic Flow.
Survey, Optic Flow. Survey of the field and a comparison of a variety of techniques.
Compares quality of results, not execution time.
Compares: Lucas/Kanade (
See also Generalized Image Matching by the Method of Differences. ),
Fleet/Jepson (
See also Hierarchial Construction of Orientation and Velocity Selective Filters. ),
Uras (
See also Computational Approach to Motion Perception, A. ),
Nagel (
See also On a Constraint Equation for the Estimation of Displacement Rates in Image Sequences. ),
Anandan (
See also Computational Framework and an Algorithm for the Measurement of Visual Motion, A. ),
Horn/Shunck (
See also Determining Optical Flow. ),
Singh (
See also Image-Flow Computation: An Estimation-Theoretic Framework and a Unified Perspective. ).
Code for all of these is available from:
WWW Link.
BibRef
Kearney, J.K.,
Thompson, W.B., and
Boley, D.L.,
Optical Flow Estimation: An Error Analysis of Gradient-Based
Methods with Local Optimization,
PAMI(9), No. 2, March 1987, pp. 229-244.
BibRef
8703
Earlier:
Gradient Based Estimation of Disparity,
PRIP82(246-251).
Optical Flow, Evaluation.
Develops a framework to evaluate errors in local gradient based
techniques. Analyze the sources of errors to see which can be
addressed. Errors include highly textured regions, non locally
constant image flows, and poor variation in the brightness gradient.
There is a good summary of optical flow techniques in the paper.
BibRef
Kearney, J.K.,
Gradient-Based Estimation of Optical-Flow,
Ph.D.Thesis (CS), Univ. of MN, 1983.
BibRef
8300
Kearney, J.K.,
Thompson, W.B.,
Gradient-Based Estimation of Optical Flow with Global Optimization,
CAIA84(376-380).
BibRef
8400
Adiv, G.,
Inherent Ambiguities in Recovering 3-D Motion and
Structure from a Noisy Flow Field,
PAMI(11), No. 5, May 1989, pp. 477-489.
IEEE DOI
BibRef
8905
Earlier:
CVPR85(70-77).
BibRef
And:
DARPA85(399-412) in a longer version.
(UMass) Noise causes errors (ambiguity) which is inherent, but some
parameters are still valid. Nothing
surprising in the general conclusions.
See also Determining 3-D Motion and Structure from Optical Flow Generated by Several Moving Objects.
BibRef
Jasinschi, R.S.,
Intrinsic Constraints in Space-Time Filtering: A
New Approach to Representing Uncertainity in Low-Level Vision,
PAMI(14), No. 3, March 1992, pp. 353-366.
IEEE DOI
BibRef
9203
Jasinschi, R.S.,
The Properties of Space-Time Sampling and the Extraction of the
Optical Flow: The Effects of Motion Uncertainty,
JVCIR(2), 1991, pp. 222-229.
BibRef
9100
Earlier:
Space-Time Sampling with Motion Uncertainty:
Constraints on Space-Time Filtering,
ICCV88(428-434).
IEEE DOI
BibRef
Jasinschi, R.[Rado],
Yuille, A.L.[Alan L.],
Non-Rigid Motion and Regge Calculus,
MIT AI Memo-996, November 1987.
BibRef
8711
Holt, R.J.,
Netravali, A.N.,
Motion from Optic Flow: Multiplicity of Solutions,
JVCIR(4), 1993, pp. 14-24.
See also Number of Solutions for Motion and Structure from Multiple Frame Correspondence.
BibRef
9300
Ben-Tzvi, D.,
del Bimbo, A.,
Nesi, P.,
Optical Flow from Constraint Lines Parametrization,
PR(26), No. 10, October 1993, pp. 1549-1561.
Elsevier DOI
BibRef
9310
Nesi, P.,
del Bimbo, A.,
Ben-Tzvi, D.,
A Robust Algorithm for Optical-Flow Estimation,
CVIU(62), No. 1, July 1995, pp. 59-68.
DOI Link
See also Algorithms for Optical Flow Estimation in Real-Time on Connection Machine-2.
BibRef
9507
del Bimbo, A.,
Nesi, P.,
Sanz, J.L.C.,
Optical flow computation using extended constraints,
IP(5), No. 5, May 1996, pp. 720-739.
IEEE DOI
0402
BibRef
del Bimbo, A.,
Nesi, P., and
Sanz, J.L.C.,
Analysis of Optical-Flow Constraints,
IP(4), No. 4, April 1995, pp. 460-469.
IEEE DOI
BibRef
9504
Earlier:
Univ. of FlorenceTR. Analysis of constraints.
BibRef
Denney, Jr., T.S.,
Prince, J.L.,
A frequency domain performance analysis of Horn and Schunck's optical
flow algorithm for deformable motion,
IP(4), No. 9, September 1995, pp. 1324-1327.
IEEE DOI
0402
See also Determining Optical Flow.
BibRef
Denney, Jr., T.S.,
On estimating 3-D incompressible motion,
ICIP95(III: 492-495).
IEEE DOI
9510
BibRef
Gupta, S.N.,
Prince, J.L.,
Stochastic formulations of optical flow algorithms under variable
brightness conditions,
ICIP95(III: 484-487).
IEEE DOI
9510
BibRef
Earnshaw, A.M.[A. Mark],
Blostein, S.D.[Steven D.],
The Performance of Camera Translation Direction Estimators from
Optical-flow: Analysis, Comparison, and Theoretical Limits,
PAMI(18), No. 9, September 1996, pp. 927-932.
IEEE DOI
Translation Estimation.
Linear Constraints.
BibRef
9609
Earlier:
An error analysis of camera translation direction estimation from
optical flow using linear constraints,
ICIP95(I: 394-397).
IEEE DOI
9510
BibRef
And:
The Performance of Camera Translation Direction Estimators,
TR- 95-10, Dept. ECE, Queens Univ. 1995.
BibRef
Yang, J.,
Stevenson, S.B.,
Effects of Spatial-Frequency, Duration, and Contrast on
Discriminating Motion Directions,
JOSA-A(14), No. 9, September 1997, pp. 2041-2048.
9709
BibRef
Fermüller, C.[Cornelia],
Aloimonos, Y.[Yannis],
Ambiguity in Structure from Motion: Sphere Versus Plane,
IJCV(28), No. 2, June/July 1998, pp. 137-154.
DOI Link
9808
BibRef
Earlier:
What Is Computed by Structure from Motion Algorithms?,
ECCV98(I: 359).
Springer DOI
BibRef
And:
UMD--TR3809, June 1997.
Structure from Motion.
Normal Flow.
Error Analysis.
WWW Link.
BibRef
Fermüller, C.[Cornelia],
Aloimonos, Y.[Yiannis],
Observability of 3D Motion,
IJCV(37), No. 1, June 2000, pp. 43-63.
DOI Link
0005
BibRef
Earlier:
Which Shape from Motion?,
ICCV98(689-695).
IEEE DOI
BibRef
Fermüller, C.,
Aloimonos, Y.,
The Confounding of Translation and Rotation in Reconstruction from
Multiple Views,
CVPR97(250-256).
IEEE DOI
9704
Given optic flow, what are optimal relations of T and R?
BibRef
Fermüller, C.[Cornelia],
Aloimonos, Y.[Yiannis],
Analysis of Reconstruction from Multiple Views,
DARPA97(1411-1418).
BibRef
9700
Fermüller, C.[Cornelia], and
Aloimonos, Y.[Yiannis],
Algorithm-Independent Stability Analysis of Structure from Motion,
UMDTR3691, September 1996.
WWW Link. Analysis of flow along some direction and spherical retina.
BibRef
9609
Liu, H.C.[Hong-Che],
Hong, T.H.[Tsai-Hong],
Herman, M.,
Camus, T.A.,
Chellappa, R.,
Accuracy vs. Efficiency Trade-Offs in Optical Flow Algorithms,
CVIU(72), No. 3, December 1998, pp. 271-286.
DOI Link
BibRef
9812
Earlier: A1, A2, A3, A5 Only:
ECCV96(II:174-183).
Springer DOI Comparison of different techniques.
Liu (
See also Generalized Motion Model for Estimating Optical Flow Using 3-D Hermite Polynomials, A. ), Lucas/Kanade (
See also Generalized Image Matching by the Method of Differences. ),
Fleet/Jepson (
See also Hierarchial Construction of Orientation and Velocity Selective Filters. ),
Anandan (
See also Computational Framework and an Algorithm for the Measurement of Visual Motion, A. ),
Camus (
See also Real-Time Quantized Optical Flow. ), Nagel (
See also On a Constraint Equation for the Estimation of Displacement Rates in Image Sequences. ),
Uras (
See also Computational Approach to Motion Perception, A. ), Horn/Shunck (
See also Determining Optical Flow. ).
BibRef
Fleury, M.,
Clark, A.F.,
Downton, A.C.,
Evaluating optical-flow algorithms on a parallel machine,
IVC(19), No. 3, February 2001, pp. 131-143.
Elsevier DOI
0103
BibRef
Fermüller, C.[Cornelia],
Shulman, D.[David],
Aloimonos, Y.[Yiannis],
The Statistics of Optical Flow,
CVIU(82), No. 1, April 2001, pp. 1-32.
DOI Link
0001
BibRef
And:
But: A3 is:
Pless, R.[Robert],
UMD--TR4080, November 1999.
WWW Link.
See also On the Geometry of Visual Correspondence.
BibRef
Fermüller, C.,
Aloimonos, Y.,
The Statistics of Optical Flow:
Implications for the Process of Correspondence in Vision,
ICPR00(Vol I: 119-126).
IEEE DOI
0009
BibRef
Fermüller, C.[Cornelia],
Pless, R.[Robert],
Aloimonos, Y.[Yiannis],
Statistical Biases in Optic Flow,
CVPR99(I: 561-566).
IEEE DOI
BibRef
9900
Fermüller, C.[Cornelia],
Baker, P.[Patrick],
Aloimonos, Y.[Yiannis],
Visual space-time geometry: A tool for perception and the imagination,
PIEEE(90), No. 7, July 2002, pp. 1113-1135.
IEEE DOI
0207
BibRef
Earlier:
Geometry and Statistics of Visual Space-Time,
VI02(53).
PDF File.
0208
BibRef
Dror, R.O.[Ron O.],
O'Carroll, D.C.[David C.],
Laughlin, S.B.[Simon B.],
Accuracy of velocity estimation by Reichardt correlators,
JOSA-A(18), No. 2, February 2001, pp. 241-252.
0102
BibRef
Mizukami, Y.[Yoshiki],
Sato, T.[Taiji],
Tanaka, K.[Kanya],
A Comparison Study for Displacement Computation:
Horn and Shunck's method versus March's method,
PRL(22), No. 6-7, May 2001, pp. 825-831.
Elsevier DOI
0105
See also Determining Optical Flow.
See also Computation of Stereo Disparity Using Regularization.
BibRef
Ng, L.[Lydia],
Solo, V.[Victor],
Errors-in-variables modeling in optical flow estimation,
IP(10), No. 10, October 2001, pp. 1528-1540.
IEEE DOI
0110
BibRef
Earlier:
Optical Flow Estimation using Adaptive Wavelet Zeroing,
ICIP99(III:722-726).
IEEE Abstract.
BibRef
Earlier:
Choosing the optimal neighbourhood size in optical flow problems with
errors-in-variables modelling,
ICIP98(II: 186-190).
IEEE DOI
9810
BibRef
Earlier:
A Data-Driven Method for Choosing Smoothing Parameters in
Optical Flow Problems,
ICIP97(III: 360-363).
IEEE DOI
BibRef
Solo, V.,
A sure-fired way to choose smoothing parameters in ill-conditioned
inverse problems,
ICIP96(III: 89-92).
IEEE DOI
9610
BibRef
Dougherty, L.,
Asmuth, J.C.,
Blom, A.S.,
Axel, L.,
Kumar, R.,
Validation of an optical flow method for tag displacement estimation,
MedImg(18), No. 4, April 1999, pp. 359-363.
IEEE Top Reference.
0110
BibRef
McCane, B.[Brendan],
Novins, K.[Kevin],
Crannitch, D.[Dion], and
Galvin, B.[Ben],
On Benchmarking Optical Flow,
CVIU(84), No. 1, October 2001, pp. 126-143.
DOI Link
0203
BibRef
McCane, B.[Brendan],
Optic Flow Evaluation,
OnlineMarch 2007.
WWW Link.
Code, Optic Flow.
BibRef
0703
Galvin, B.,
McCane, B.,
Novins, K.,
Mason, D.,
Mills, S.,
Recovering Motion Fields:
An Evaluation of Eight Optical Flow Algorithms,
BMVC98(xx-yy).
BibRef
9800
Langer, M.S.[Michael S.],
Mann, R.[Richard],
Optical Snow,
IJCV(55), No. 1, September 2003, pp. 55-71.
DOI Link
0307
BibRef
Earlier:
Tracking through Optical Snow,
BMCV02(181 ff.).
Springer DOI
0303
BibRef
Earlier:
Dimensional Analysis of Image Motion,
ICCV01(I: 155-162).
IEEE DOI
0106
Optical snow -- motion through a very cluttered scene.
See also Spectrum analysis of motion parallax in a 3D cluttered scene and application to egomotion.
BibRef
Chapdelaine-Couture, V.,
Roy, S.,
Langer, M.S.,
Mann, R.,
Principal Components Analysis of Optical Snow,
BMVC04(xx-yy).
HTML Version.
0508
BibRef
Mann, R.[Richard],
Langer, M.S.[Michael S.],
Optical flow and the aperture problem,
ICPR02(IV: 264-267).
IEEE DOI
0211
BibRef
Lim, S.H.[Suk-Hwan],
Apostolopoulos, J.G.,
Gamal, A.E.,
Optical flow estimation using temporally oversampled video,
IP(14), No. 8, August 2005, pp. 1074-1087.
IEEE DOI
0508
BibRef
Earlier:
Benefits of temporal oversampling in optical flow estimation,
ICIP04(IV: 2567-2570).
IEEE DOI
0505
BibRef
Ohta, N.[Naoya],
Nishizawa, S.[Satoe],
How Much Does Color Information Help Optical Flow Computation?,
IEICE(E89-D), No. 5, May 2006, pp. 1759-1762.
DOI Link
0605
BibRef
Nguyen, D.D.[Duc Dung],
Jeon, J.W.[Jae Wook],
Enhancing accuracy and sharpness of motion field with
adaptive scheme and occlusion-aware filter,
IET-IPR(7), No. 2, 2013, pp. xx-yy.
DOI Link
1307
BibRef
Earlier:
Enhancing motion field with OA-filter and alternative measurement,
ICPR12(870-873).
WWW Link.
1302
Occlusion-Aware filter.
evaluate quality of flow field
BibRef
Sun, D.Q.[De-Qing],
Roth, S.[Stefan],
Black, M.J.[Michael J.],
A Quantitative Analysis of Current Practices in Optical Flow Estimation
and the Principles Behind Them,
IJCV(106), No. 2, January 2014, pp. 115-137.
WWW Link.
1402
BibRef
Andalibi, M.[Mehran],
Hoberock, L.L.[Lawrence.L.],
Mohamadipanah, H.[Hossein],
Effects of texture addition on optical flow performance in images
with poor texture,
IVC(40), No. 1, 2015, pp. 1-15.
Elsevier DOI
1506
Optical flow
BibRef
Márquez-Valle, P.[Patricia],
Hernández-Sabaté, A.[Aura],
Gil, D.[Debora],
A Confidence Framework for the Assessment of Optical Flow Performance,
ELCVIA(15), No. 2, 2016, pp. 4-6.
DOI Link
1611
BibRef
Earlier: A1, A3, A2:
Evaluation of the Capabilities of Confidence Measures for Assessing
Optical Flow Quality,
CVVT13(624-631)
IEEE DOI
1403
BibRef
Earlier: A1, A3, A2:
A Complete Confidence Framework for Optical Flow,
UnOptFlow12(II: 124-133).
Springer DOI
1210
image sequences
BibRef
Adams, H.[Henry],
Bush, J.[Johnathan],
Carr, B.[Brittany],
Kassab, L.[Lara],
Mirth, J.[Joshua],
A torus model for optical flow,
PRL(129), 2020, pp. 304-310.
Elsevier DOI
2001
BibRef
Earlier:
On the Nonlinear Statistics of Optical Flow,
CTIC19(151-165).
Springer DOI
1901
Optical flow, Computational topology, Persistent homology,
Fiber bundle, Zigzag persistence
BibRef
Tu, Z.G.[Zhi-Gang],
Xie, W.[Wei],
Zhang, D.J.[De-Jun],
Poppe, R.[Ronald],
Veltkamp, R.C.[Remco C.],
Li, B.X.[Bao-Xin],
Yuan, J.S.[Jun-Song],
A survey of variational and CNN-based optical flow techniques,
SP:IC(72), 2019, pp. 9-24.
Elsevier DOI
1902
Optical flow, Variational method, CNN-based method,
Evaluation measures, Challenges
BibRef
Dagobert, T.[Tristan],
Monzón, N.[Nelson],
Sánchez, J.[Javier],
Comparison of Optical Flow Methods under Stereomatching with Short
Baselines,
IPOL(9), 2019, pp. 329-359.
DOI Link
1911
Code, Optical Flow. Code for:
Lucas-Kanade 1D, Robust Optical Flow 1D and
oRobust Discontinuity Preserving 1D.
BibRef
Han, Y.[Yunhui],
Luo, K.M.[Kun-Ming],
Luo, A.[Ao],
Liu, J.Y.[Jiang-Yu],
Fan, H.Q.[Hao-Qiang],
Luo, G.M.[Gui-Ming],
Liu, S.C.[Shuai-Cheng],
RealFlow:
EM-Based Realistic Optical Flow Dataset Generation from Videos,
ECCV22(XIX:288-305).
Springer DOI
2211
BibRef
Schmalfuss, J.[Jenny],
Scholze, P.[Philipp],
Bruhn, A.[Andrés],
A Perturbation-Constrained Adversarial Attack for Evaluating the
Robustness of Optical Flow,
ECCV22(XXII:183-200).
Springer DOI
2211
BibRef
Shugrina, M.[Maria],
Liang, Z.H.[Zi-Heng],
Kar, A.[Amlan],
Li, J.[Jiaman],
Singh, A.[Angad],
Singh, K.[Karan],
Fidler, S.[Sanja],
Creative Flow+ Dataset,
CVPR19(5379-5388).
IEEE DOI
2002
Dataset, Optical Flow.
WWW Link. Video dataset richly labeled with per-pixel optical flow, occlusions,
correspondences, segmentation labels, normals, and depth.
BibRef
Wulff, J.[Jonas],
Black, M.J.[Michael J.],
Temporal Interpolation as an Unsupervised Pretraining Task for Optical
Flow Estimation,
GCPR18(567-582).
Springer DOI
1905
BibRef
Wulff, J.[Jonas],
Sevilla-Lara, L.,
Black, M.J.[Michael Julian],
Optical Flow in Mostly Rigid Scenes,
CVPR17(6911-6920)
IEEE DOI
1711
Benchmark testing, Cameras, Estimation, Motion segmentation,
Optical imaging, Semantics,
BibRef
Janai, J.[Joel],
Güney, F.[Fatma],
Ranjan, A.[Anurag],
Black, M.J.[Michael J.],
Geiger, A.[Andreas],
Unsupervised Learning of Multi-Frame Optical Flow with Occlusions,
ECCV18(XVI: 713-731).
Springer DOI
1810
BibRef
Janai, J.[Joel],
Güney, F.[Fatma],
Wulff, J.[Jonas],
Black, M.J.[Michael Julian],
Geiger, A.[Andreas],
Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse
Optical Flow Reference Data,
CVPR17(1406-1416)
IEEE DOI
1711
Adaptive optics, Cameras, High-speed optical techniques,
Optical imaging, Optical recording, Spatial resolution
BibRef
Güssefeld, B.[Burkhard],
Honauer, K.[Katrin],
Kondermann, D.[Daniel],
Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets,
ISVC16(I: 77-90).
Springer DOI
1701
BibRef
Mayer, N.[Nikolaus],
Ilg, E.[Eddy],
Häusser, P.[Philip],
Fischer, P.[Philipp],
Cremers, D.[Daniel],
Dosovitskiy, A.[Alexey],
Brox, T.[Thomas],
A Large Dataset to Train Convolutional Networks for Disparity,
Optical Flow, and Scene Flow Estimation,
CVPR16(4040-4048)
IEEE DOI
1612
Dataset, Optical Flow.
BibRef
Xue, T.F.[Tian-Fan],
Mobahi, H.[Hossein],
Durand, F.[Fredo],
Freeman, W.T.[William T.],
The aperture problem for refractive motion,
CVPR15(3386-3394)
IEEE DOI
1510
BibRef
Gussefeld, B.[Burkhard],
Kondermann, D.[Daniel],
Schwartz, C.[Christopher],
Klein, R.[Reinhard],
Are reflectance field renderings appropriate for optical flow
evaluation?,
ICIP14(1982-1986)
IEEE DOI
1502
Adaptive optics
BibRef
Mordohai, P.[Philippos],
On the Evaluation of Scene Flow Estimation,
UnOptFlow12(II: 148-157).
Springer DOI
1210
BibRef
Wulff, J.[Jonas],
Butler, D.J.[Daniel J.],
Stanley, G.B.[Garrett B.],
Black, M.J.[Michael J.],
Lessons and Insights from Creating a Synthetic Optical Flow Benchmark,
UnOptFlow12(II: 168-177).
Springer DOI
1210
BibRef
Butler, D.J.[Daniel J.],
Wulff, J.[Jonas],
Stanley, G.B.[Garrett B.],
Black, M.J.[Michael J.],
A Naturalistic Open Source Movie for Optical Flow Evaluation,
ECCV12(VI: 611-625).
Springer DOI
1210
BibRef
Kondermann, D.[Daniel],
Abraham, S.[Steffen],
Brostow, G.J.[Gabriel J.],
Förstner, W.[Wolfgang],
Gehrig, S.K.[Stefan K.],
Imiya, A.[Atsushi],
Jähne, B.[Bernd],
Klose, F.[Felix],
Magnor, M.[Marcus],
Mayer, H.[Helmut],
Mester, R.[Rudolf],
Pajdla, T.[Thomas],
Reulke, R.[Ralf],
Zimmer, H.[Henning],
On Performance Analysis of Optical Flow Algorithms,
WTFCV11(329-355).
Springer DOI
1210
BibRef
Márquez-Valle, P.[Patricia],
Gil, D.[Debora],
Hernàndez-Sabaté, A.[Aura],
Error Analysis for Lucas-Kanade Based Schemes,
ICIAR12(I: 184-191).
Springer DOI
1206
BibRef
And:
A confidence measure for assessing optical flow accuracy in the absence
of ground truth,
CVVT11(2042-2049).
IEEE DOI
1201
See also Iterative Image Registration Technique with an Application to Stereo Vision, An.
BibRef
Gehrig, S.K.[Stefan K.],
Scharwachter, T.[Timo],
A real-time multi-cue framework for determining optical flow confidence,
CVVT11(1978-1985).
IEEE DOI
1201
BibRef
Adkins-Hill, J.P.,
Fortunato, J.M.,
Zhang, Y.,
Sullins, J.R.,
An empirical comparison of high definition video and regular video in
optical flow computation,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Sun, D.Q.[De-Qing],
Roth, S.[Stefan],
Black, M.J.[Michael J.],
Secrets of optical flow estimation and their principles,
CVPR10(2432-2439).
IEEE DOI
1006
Award, Longuet-Higgins. (after 10 years).
BibRef
Sun, D.Q.[De-Qing],
Roth, S.[Stefan],
Lewis, J.P.,
Black, M.J.[Michael J.],
Learning Optical Flow,
ECCV08(III: 83-97).
Springer DOI
0810
BibRef
Baker, S.[Simon],
Scharstein, D.[Daniel],
Lewis, J.P.,
Roth, S.[Stefan],
Black, M.J.[Michael J.],
Szeliski, R.S.[Richard S.],
A Database and Evaluation Methodology for Optical Flow,
IJCV(92), No. 1, March 2011, pp. 1-31.
Springer DOI
1103
BibRef
Earlier: A1, A4, A2, A5, A3, A6:
ICCV07(1-8).
IEEE DOI
0710
Dataset, Optical Flow.
WWW Link.
BibRef
Ulman, V.[Vladimír],
Hubený, J.[Jan],
Pseudo-real Image Sequence Generator for Optical Flow Computations,
SCIA07(976-985).
Springer DOI
0706
Generation of artificial flow data for evaluation of computations.
BibRef
Adachi, E.[Eisuke],
Kurita, T.[Takio],
Otsu, N.[Nobuyuki],
Reliability index of optical flow that considers error margin of
matches and stabilizes camera movement estimation,
ICPR06(I: 699-702).
IEEE DOI
0609
BibRef
Austvoll, I.[Ivar],
A Study of the Yosemite Sequence Used as a Test Sequence for Estimation
of Optical Flow,
SCIA05(659-668).
Springer DOI
0506
BibRef
Spies, H.,
Barron, J.L.,
Evaluating certainties in image intensity differentation for optical
flow,
CRV04(408-416).
IEEE DOI
0408
BibRef
Earlier:
Evaluating the range flow motion constraint,
ICPR02(III: 517-520).
IEEE DOI
0211
BibRef
Okada, R.,
Maki, A.,
Taniguchi, Y.,
Onoguchi, K.,
Temporally evaluated optical flow: study on accuracy,
ICPR02(I: 343-347).
IEEE DOI
0211
BibRef
Zhao, W.,
Sawhney, H.S.,
Is Super-Resolution with Optical Flow Feasible?,
ECCV02(I: 599 ff.).
Springer DOI
0205
BibRef
Olson, C.F.[Clark F.],
Matthies, L.H.[Larry H.],
Schoppers, M.J.[Marcel J.],
Maimone, M.W.[Mark W.],
Robust Stereo Ego-Motion for Long Distance Navigation,
CVPR00(II: 453-458).
IEEE DOI
0005
Add orientation sensor; then position error is reduced
BibRef
Nishimura, T.,
Oka, R.,
Held, A.,
Kojima, H.,
Effect of Time-Spatial Size of Motion Image for Localization by
Using the Spotting Method,
ICPR96(I: 191-195).
IEEE DOI
9608
(Information Integration Lab. J)
BibRef
Shaw, G.B.,
Determining Motion Parameters Using a Perturbation Approach,
COINSTR 83-30, UMass., September 1983.
now at Univ of Oregon,
Discusses the error problems of traditional methods for computation
of motion parameters from noisy data, and proposes a solution.
BibRef
8309
Little, J.J., and
Verri, A.,
Analysis of Differential and Matching Methods for Optical Flow,
Motion89(173-180).
BibRef
8900
And:
MIT AI Memo-1066, August 1988.
Optical Flow, Evaluation.
BibRef
Denzler, J.[Joachim],
Schless, V.,
Paulus, D.,
Niemann, H.,
Statistical Approach to Classification
of Flow Patterns for Motion Detection,
ICIP96(I: 517-520).
IEEE DOI
PS File.
BibRef
9600
Niemann, H.,
Arnold, J.,
Sagerer, G.,
On the Accuracy of Optical Flow Computation Using Global Optimization,
ICPR88(II: 1094-1096).
IEEE DOI
BibRef
8800
Chapter on Optical Flow Field Computations and Use continues in
Optical Flow Field -- Smoothness .