17.4 Error Analysis, Evaluation for Optical Flow

Chapter Contents (Back)
Evaluation, Optical Flow. Optical Flow, Evaluation.

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.
WWW Link. 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.
WWW Link. 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-PR(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


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., Güney, F., Wulff, J.[Jonas], Black, M.J.[Michael Julian], Geiger, A.,
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

Vogel, C.[Christoph], Roth, S.[Stefan], Schindler, K.[Konrad],
View-Consistent 3D Scene Flow Estimation over Multiple Frames,
ECCV14(IV: 263-278).
Springer DOI 1408
BibRef
Earlier: A1, A3, A2:
Piecewise Rigid Scene Flow,
ICCV13(1377-1384)
IEEE DOI 1403
BibRef
And: A1, A2, A3:
An Evaluation of Data Costs for Optical Flow,
GCPR13(343-353).
Springer DOI 1311
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.[Stefan], 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
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.[Richard],
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 .


Last update:Nov 18, 2017 at 20:56:18