18.3 Optic Flow Computation and Use, Other Approaches

Chapter Contents (Back)
Optical Flow.
See also Opeical Flow, Learning, Neural Networks, GAN.

Yachida, M.[Masahiko],
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Earlier:
Determining Velocity Map by 3-D Iterative Estimation,
IJCAI81(716-718). From the sequence, use first 2 to guide the rest. BibRef

Zucker, S.W.[Steven W.], Iverson, L.[Lee],
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CVGIP(37), No. 2, February 1987, pp. 196-220.
Elsevier DOI BibRef 8702
Earlier: A2, A1:
Orientation Selection to Optical Flow: A Computational Perspective,
CVWS87(184-189). BibRef

Iverson, L.[Lee],
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PS File. or Tar file with separate chapters:
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And: No title available, but it is different. Perception(5), 1976, pp. 437-xx. BibRef

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PAMI(11), No. 5, May 1989, pp. 512-522.
IEEE DOI Gradient based approach with only the intensity gradient as a derivative. Performs better when images have NO strong texture. BibRef 8905

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And: Erratum: CVGIP(54), No. 3, November 1991, pp. 431-432.
Elsevier DOI BibRef
Earlier:
Implementation of a Nonlinear Approach to the Motion Correspondence Problem,
Motion89(87-98). Implementation issues for the earlier: BibRef
A Nonlinear Approach to the Motion Correspondence Problem,
ICCV88(619-628).
IEEE DOI Combining the optical flow constraints (relating image values) and directional smoothness constraints to get better estimates. There is a lot of derivation of the technique. BibRef

Shvaytser, H.[Haim],
Occam Algorithms for Computing Visual-Motion,
PAMI(17), No. 11, November 1995, pp. 1033-1042.
IEEE DOI BibRef 9511
Earlier: ICCV93(551-555).
IEEE DOI The best predictor is the simplest, defined in terms of encoding length. BibRef

Vaidya, V.G., Haralick, R.M.,
Wigner Distribution for 2D Motion Estimation from Noisy Images,
JVCIR(4), 1993, pp. 281-297. BibRef 9300

Huang, C.L.[Chung-Lin], Chen, Y.T.[Ying-Tsang],
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IVC(13), No. 1, February 1995, pp. 21-32.
Elsevier DOI BibRef 9502

Sayrol, E., Gasull, A., Fonollosa, J.R.,
Motion Estimation Using Higher-Order Statistics,
IP(5), No. 6, June 1996, pp. 1077-1084.
IEEE DOI 9607
BibRef

Anderson, J.M.M., Giannakis, G.B.,
Image Motion Estimation Algorithms Using Cumulants,
IP(4), No. 3, March 1995, pp. 346-357.
IEEE DOI BibRef 9503

Kirchner, H., Niemann, H.,
Finite Element Method for Determination of Optical Flow,
PRL(13), 1992, pp. 131-141. BibRef 9200

Strintzis, M.G., Kokkinidis, I.,
Maximum-Likelihood Motion Estimation in Ultrasound Image Sequences,
SPLetters(4), No. 6, June 1997, pp. 156-157.
IEEE Top Reference. 9706
BibRef

Rakshit, S., Anderson, C.H.,
Computation of Optical-Flow Using Basis Functions,
IP(6), No. 9, September 1997, pp. 1246-1254.
IEEE DOI 9709
BibRef

Spinei, A., Pellerin, D., Herault, J.,
Spatiotemporal Energy-Based Method for Velocity Estimation,
SP(65), No. 3, March 1998, pp. 347-362. 9806
BibRef

Vernon, D.[David],
Computation of instantaneous optical flow using the phase of Fourier components,
IVC(17), No. 3/4, March 1999, pp. 189-199.
Elsevier DOI BibRef 9903
Earlier:
Decoupling Fourier Components of Dynamic Image Sequences: A Theory of Signal Separation, Image Segmentation, and Optical Flow Estimation,
ECCV98(II: 69).
Springer DOI Hough approach. BibRef

Li, H.D.[Hong-Dong], Liu, J.L.[Ji-Lin], Gu, W.[Weikang],
A new and fast approach for DPIV using an incompressible affine flow model,
MVA(11), No. 5, 2000, pp. 252-256.
Springer DOI 0004
BibRef

Yang, J.L.[Jiao-Long], Li, H.D.[Hong-Dong],
Dense, accurate optical flow estimation with piecewise parametric model,
CVPR15(1019-1027)
IEEE DOI 1510
BibRef

Yao, J.C.[Jian-Chao],
Estimation of 2D Displacement Field Based on Affine Geometric Invariance and Scene Constraints,
IJCV(46), No. 1, January 2002, pp. 25-50.
DOI Link 0201
BibRef
Earlier:
Estimation of 2D Motion Field Based on Affine Geometric Invariance,
ICPR00(Vol III: 1037-1040).
IEEE DOI 0009
BibRef
Earlier:
Motion Blur Identification Based on Phase Change Experienced After Trial Restorations,
ICIP99(I:180-184).
IEEE Abstract. BibRef

Yao, J.C.[Jian-Chao],
Dynamic Vision in the Dynamic Scene: An Algebraic Approach,
ICARCV06(1-6).
IEEE DOI 0612
BibRef

Chen, L.F.[Li-Fen], Liao, H.Y.M.[Hong-Yuan Mark], Lin, J.C.[Ja-Chen],
Wavelet-based Optical Flow Estimation,
CirSysVideo(12), No. 1, January 2002, pp. 1-12.
IEEE Top Reference. 0202
BibRef
Earlier: A1, A3, A2: ICPR00(Vol III: 1056-1059).
IEEE DOI
IEEE DOI 0009
BibRef

Foroosh, H.[Hassan], and Hoge, W.S.[W. Scott],
Motion Information in the Phase Domain,
VideoRegister03(Chapter 3). BibRef 0300

Barcelos, C.A.Z., Boaventura, M., Silva, Jr., E.C.,
A well-balanced flow equation for noise removal and edge detection,
IP(12), No. 7, July 2003, pp. 751-763.
IEEE DOI 0308
BibRef

Lai, S.H.[Shang-Hong],
Computation of optical flow under non-uniform brightness variations,
PRL(25), No. 8, June 2004, pp. 885-892.
Elsevier DOI 0405
BibRef

Teng, C.H.[Chin-Hung], Lai, S.H.[Shang-Hong], Chen, Y.S.[Yung-Sheng], Hsu, W.H.[Wen-Hsing],
Accurate optical flow computation under non-uniform brightness variations,
CVIU(97), No. 3, March 2005, pp. 315-346.
Elsevier DOI 0412
BibRef
Earlier:
Robust computation of optical flow under non-uniform illumination variations,
ICPR02(I: 327-330).
IEEE DOI 0211
BibRef

Condell, J.V.[Joan V.], Scotney, B.W.[Bryan W.], Morrow, P.J.[Philip J.],
Adaptive Grid Refinement Procedures for Efficient Optical Flow Computation,
IJCV(61), No. 1, January 2005, pp. 31-54.
DOI Link 0410
BibRef
Earlier:
Evaluation of Uniform and Non-uniform Optical Flow Techniques Using Finite Element Methods,
DAGM03(116-123).
Springer DOI 0310
BibRef
Earlier:
Estimation of Motion through Inverse Finite Element Methods with Triangular Meshes,
CAIP01(333 ff.).
Springer DOI 0210
BibRef

Condell, J.V.[Joan V.], Scotney, B.W.[Bryan W.], Morrow, P.J.[Philip J.],
Adaptive vs. non-adaptive strategies for the computation of optical flow,
IJIST(16), No. 2, 2006, pp. 35-50.
DOI Link 0703
BibRef
And:
The Effect of Presmoothing Image Sequences on the Computation of Optical Flow,
ICIAR06(I: 780-791).
Springer DOI 0610
BibRef

Foroosh, H.[Hassan],
Pixelwise-Adaptive Blind Optical Flow Assuming Nonstationary Statistics,
IP(14), No. 2, February 2005, pp. 222-230.
IEEE DOI 0501
BibRef
Earlier:
An adaptive scheme for estimating motion,
ICIP04(III: 1831-1834).
IEEE DOI 0505
BibRef

Foroosh, H.,
A Closed-form Solution for Optical Flow by Imposing Temporal Constraints,
ICIP01(III: 656-659).
IEEE DOI 0108
BibRef

Coimbra, M.T., Davies, M.,
Approximating Optical Flow Within the MPEG-2 Compressed Domain,
CirSysVideo(15), No. 1, January 2005, pp. 103-107.
IEEE Abstract. 0501
BibRef

Trucco, E., Tommasini, T., Roberto, V.,
Near-recursive optical flow from weighted image differences,
SMC-B(35), No. 1, February 2005, pp. 124-129.
IEEE Abstract. 0501
BibRef

Trucco, E., Viel, F., Roberto, V.,
Near-recursive optical flow from disturbance fields,
BMVC02(Poster Session). 0208
BibRef

Papenberg, N.[Nils], Bruhn, A.[Andrés], Brox, T.[Thomas], Didas, S.[Stephan], Weickert, J.[Joachim],
Highly Accurate Optic Flow Computation with Theoretically Justified Warping,
IJCV(67), No. 2, April 2006, pp. 141-158.
Springer DOI 0605
Grey value constancy, and gradient constancy, and the constancy of the Hessian and the Laplacian. BibRef

Brox, T.[Thomas], Bruhn, A.[Andrés], Papenberg, N.[Nils], Weickert, J.[Joachim],
High Accuracy Optical Flow Estimation Based on a Theory for Warping,
ECCV04(Vol IV: 25-36).
Springer DOI Award, ECCV. Award, Koenderink Prize. 0405
BibRef

Zimmer, H.[Henning], Bruhn, A.[Andrés], Weickert, J.[Joachim],
Optic Flow in Harmony,
IJCV(93), No. 3, July 2011, pp. 368-388.
WWW Link. 1104
BibRef

Volz, S.[Sebastian], Bruhn, A.[Andres], Valgaerts, L.[Levi], Zimmer, H.[Henning],
Modeling temporal coherence for optical flow,
ICCV11(1116-1123).
IEEE DOI 1201
BibRef

Hafner, D.[David], Demetz, O.[Oliver], Weickert, J.[Joachim],
Mathematical Foundations and Generalisations of the Census Transform for Robust Optic Flow Computation,
JMIV(52), No. 1, May 2015, pp. 71-86.
Springer DOI 1505
BibRef
Earlier:
Simultaneous HDR and Optic Flow Computation,
ICPR14(2065-2070)
IEEE DOI 1412
BibRef
Earlier:
Why Is the Census Transform Good for Robust Optic Flow Computation?,
SSVM13(210-221).
Springer DOI 1305
Adaptive optics BibRef

Demetz, O.[Oliver], Weickert, J.[Joachim], Bruhn, A.[Andrés], Zimmer, H.[Henning],
Optic Flow Scale Space,
SSVM11(713-724).
Springer DOI 1201
BibRef

Zimmer, H.[Henning], Bruhn, A.[Andrés], Weickert, J.[Joachim], Valgaerts, L.[Levi], Salgado, A.[Agustín], Rosenhahn, B.[Bodo], Seidel, H.P.[Hans-Peter],
Complementary Optic Flow,
EMMCVPR09(207-220).
Springer DOI 0908
BibRef

Valgaerts, L.[Levi], Bruhn, A.[Andrés], Mainberger, M.[Markus], Weickert, J.[Joachim],
Dense versus Sparse Approaches for Estimating the Fundamental Matrix,
IJCV(96), No. 2, February 2012, pp. 212-234.
WWW Link. 1201
BibRef
Earlier: A3, A2, A4, Only:
Is Dense Optic Flow Useful to Compute the Fundamental Matrix?,
ICIAR08(xx-yy).
Springer DOI 0806
BibRef

Valgaerts, L.[Levi], Bruhn, A.[Andrés], Weickert, J.[Joachim],
A Variational Model for the Joint Recovery of the Fundamental Matrix and the Optical Flow,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Brox, T.[Thomas], Rosenhahn, B.[Bodo], Cremers, D.[Daniel], Seidel, H.P.[Hans-Peter],
High Accuracy Optical Flow Serves 3-D Pose Tracking: Exploiting Contour and Flow Based Constraints,
ECCV06(II: 98-111).
Springer DOI 0608
BibRef

Rosenhahn, B.[Bodo], Brox, T.[Thomas], Cremers, D.[Daniel], Seidel, H.P.[Hans-Peter],
A Comparison of Shape Matching Methods for Contour Based Pose Estimation,
IWCIA06(263-276).
Springer DOI 0606
BibRef

Brox, T.[Thomas], Rosenhahn, B.[Bodo], Cremers, D.[Daniel], Seidel, H.P.[Hans-Peter],
Nonparametric Density Estimation with Adaptive, Anisotropic Kernels for Human Motion Tracking,
HUMO07(152-165).
Springer DOI 0710
BibRef

Brox, T.[Thomas], Rosenhahn, B.[Bodo], Kersting, U.G.[Uwe G.], Cremers, D.[Daniel],
Nonparametric Density Estimation for Human Pose Tracking,
DAGM06(546-555).
Springer DOI 0610

See also Optimization and Filtering for Human Motion Capture: A Multi-Layer Framework. BibRef

Rosenhahn, B., Kersting, U.G., Smith, A.W., Gurney, J.K., Brox, T., Klette, R.,
A System for Marker-Less Human Motion Estimation,
DAGM05(230).
Springer DOI 0509
Award, GCPR.
See also Optimization and Filtering for Human Motion Capture: A Multi-Layer Framework. BibRef

Brox, T., Weickert, J.,
Nonlinear Matrix Diffusion for Optic Flow Estimation,
DAGM02(446 ff.).
Springer DOI 0303
BibRef

Makadia, A.[Ameesh], Daniilidis, K.[Kostas],
Rotation Recovery from Spherical Images without Correspondences,
PAMI(28), No. 7, July 2006, pp. 1170-1175.
IEEE DOI 0606
BibRef
Earlier:
Direct 3D-rotation estimation from spherical images via a generalized shift theorem,
CVPR03(II: 217-224).
IEEE DOI 0307
Mapping of omnidirectional image to sphere. Rotational camera motion analysis through generalized Fourier transform method.
See also Correspondence-free Structure from Motion. BibRef

Daniilidis, K., Makadia, A., Bulow, T.,
Image processing in catadioptric planes: spatiotemporal derivatives and optical flow computation,
OMNIVIS02(3-10).
IEEE Abstract. 0310
BibRef

Sorgi, L.[Lorenzo],
Edgelet tracking using Gauss-Laguerre Circular Harmonic filters,
ICIP11(2897-2900).
IEEE DOI 1201
BibRef

Makadia, A., Sorgi, L., Daniilidis, K.,
Rotation estimation from spherical images,
ICPR04(III: 590-593).
IEEE DOI 0409

See also Normalized Cross-Correlation for Spherical Images. BibRef

Tagliasacchi, M.[Marco],
A genetic algorithm for optical flow estimation,
IVC(25), No. 2, February 2007, pp. 141-147.
Elsevier DOI 0701
Optical flow; Genetic algorithms; Motion estimation BibRef

Lu, Z.Q.[Zong-Qing], Liao, Q.M.[Qing-Min], Pei, J.H.[Ji-Hong],
A Nonlinear Filtering Based Optical Flow Computation,
IJIG(9), No. 1, January 2009, pp. 121-132. 0903
BibRef

Shi, K.H.[Kai-Hong], Lu, Z.Q.[Zong-Qing], She, Q.Y.[Qing-Yun], Zhou, F.[Fei], Liao, Q.M.[Qing-Min],
Optical Flow Estimation Combining Spatial-Temporal Derivatives Based Nonlinear Filtering,
IEICE(E97-D), No. 9, September 2014, pp. 2559-2562.
WWW Link. 1410
BibRef

She, Q.Y.[Qing-Yun], Lu, Z.Q.[Zong-Qing], Li, W.F.[Wei-Feng], Liao, Q.M.[Qing-Min],
Multigrid Bilateral Filtering,
IEICE(E97-D), No. 10, October 2014, pp. 2748-2759.
WWW Link. 1411
BibRef

Lu, Z.Q.[Zong-Qing], Xie, W.X.[Wei-Xin],
A PDE-Based Method For Optical Flow Estimation,
ICPR06(II: 78-81).
IEEE DOI 0609
BibRef

Aalaoui, E.M.I.[E.M. Ismaili], Ibn-Elhaj, E.[Elhassane], Bouyakhf, E.H.,
A Robust Subpixel Motion Estimation Algorithm Using HOS in the Parametric Domain,
JIVP(2009), No. 2009, pp. xx-yy.
DOI Link 0903
Consider noise issues. BibRef

Aalaoui, E.M.I.[E.M. Ismaili], Ibn-Elhaj, E.[Elhassane],
A comparative study of new HOS-based estimators for moving objects in noisy video sequence,
SIViP(11), No. 7, October 2017, pp. 1297-1304.
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Sabatini, S.P.[Silvio P.], Gastaldi, G.[Giulia], Solari, F.[Fabio], Pauwels, K.[Karl], van Hulle, M.M.[Marc M.], Diaz, J.[Javier], Ros, E.[Eduardo], Pugeault, N.[Nicolas], Kruger, N.[Norbert],
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CVIU(114), No. 6, June 2010, pp. 681-699.
Elsevier DOI 1006
Early vision; Phase-based image analysis; Multichannel filtering; Image representations; Stereo; Motion; Bio-inspired vision processing
See also Adjustable linear models for optic flow based obstacle avoidance. BibRef

Molnar, J.[Jozsef], Chetverikov, D.[Dmitry], Fazekas, S.[Sandor],
Illumination-robust variational optical flow using cross-correlation,
CVIU(114), No. 10, October 2010, pp. 1104-1114.
Elsevier DOI 1003
BibRef
Earlier: A3, A2, A1:
An implicit non-linear numerical scheme for illumination-robust variational optical flow,
BMVC09(xx-yy).
PDF File. 0909
Variational optical flow; Illumination-robustness; Cross-correlation BibRef

Chetverikov, D.[Dmitry], Molnar, J.[Jozsef],
An Experimental Study of Image Components and Data Metrics for Illumination-Robust Variational Optical Flow,
ICPR10(1694-1697).
IEEE DOI 1008
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Dellen, B.[Babette], Wörgötter, F.[Florentin],
A Local Algorithm for the Computation of Image Velocity via Constructive Interference of Global Fourier Components,
IJCV(92), No. 1, March 2011, pp. 53-70.
WWW Link. 1103
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Earlier:
A Local Algorithm for the Computation of Optic Flow via Constructive Interference of Global Fourier Components,
BMVC08(xx-yy).
PDF File. 0809

See also Disparity from stereo-segment silhouettes of weakly-textured images. BibRef

Feigin, M.[Micha], Sochen, N.A.[Nir A.], Vemuri, B.C.[Baba C.],
Anisotropic alpha-Kernels and Associated Flows,
SIIMS(3), No. 4, 2010, pp. 904-925.
DOI Link BibRef 1000
Earlier: SSVM07(484-495).
Springer DOI 0705
Scale space computations. Fractional Laplacian. BibRef
And:
Efficient anisotropic alpha-Kernels decompositions and flows,
Tensor08(1-8).
IEEE DOI 0806
scale space; image denoising; sparse decomposition; eigenspaces
See also Scale Space and Edge Detection using Anisotropic Diffusion.
See also Anisotropic Regularization for Inverse Problems with Application to the Wiener Filter with Gaussian and Impulse Noise. BibRef

Belhachmi, Z.[Zakaria], Hecht, F.,
Control of the Effects of Regularization on Variational Optic Flow Computations,
JMIV(40), No. 1, May 2011, pp. 119.
WWW Link. 1103
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Belhachmi, Z.[Zakaria], Hecht, F.,
An Adaptive Approach for the Segmentation and the TV-Filtering in the Optic Flow Estimation,
JMIV(54), No. 3, March 2016, pp. 358-377.
WWW Link. 1604
BibRef

Bauer, M.[Martin], Grasmair, M.[Markus], Kirisits, C.[Clemens],
Optical Flow on Moving Manifolds,
SIIMS(8), No. 1, 2015, pp. 484-512.
DOI Link 1503
BibRef

Kirisits, C.[Clemens], Lang, L.F.[Lukas F.], Scherzer, O.[Otmar],
Optical Flow on Evolving Surfaces with Space and Time Regularisation,
JMIV(52), No. 1, May 2015, pp. 55-70.
Springer DOI 1505
BibRef
Earlier:
Optical Flow on Evolving Surfaces with an Application to the Analysis of 4D Microscopy Data,
SSVM13(246-257).
Springer DOI 1305
BibRef

Abhau, J.[Jochen], Belhachmi, Z.[Zakaria], Scherzer, O.[Otmar],
On a Decomposition Model for Optical Flow,
EMMCVPR09(126-139).
Springer DOI 0908
BibRef

Kharbat, M.[Mohd], Aouf, N.[Nabil],
Dense optical flow via robust data fusion,
SIViP(5), No. 2, June 2011, pp. 203-215.
WWW Link. 1101
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Kharbat, M.[Mohd], Aouf, N.[Nabil], Tsourdos, A., White, B.,
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PDF File. 0809
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Radgui, A.[Amina], Demonceaux, C.[Cédric], Mouaddib, E.[El_Mustapha], Rziza, M.[Mohammed], Aboutajdine, D.[Driss],
Optical flow estimation from multichannel spherical image decomposition,
CVIU(115), No. 9, September 2011, pp. 1263-1272.
Elsevier DOI 1107
BibRef
Earlier: A1, A2, A4, A3, A5:
An adapted Lucas-Kanade's method for optical flow estimation in catadioptric images,
OMNIVIS08(xx-yy). 0810
Omnidirectional images; Optical flow; Spherical wavelets
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Alibouch, B.[Brahim], Radgui, A.[Amina], Demonceaux, C.[Cédric], Rziza, M.[Mohammed], Aboutajdine, D.[Driss],
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SIViP(10), No. 1, February 2016, pp. 285-292.
Springer DOI 1601
BibRef
Earlier: A1, A2, A4, A5, Only:
Optical Flow Estimation on Omnidirectional Images: An Adapted Phase Based Method,
ICISP12(468-475).
Springer DOI 1208
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Sellent, A.[Anita], Eisemann, M.[Martin], Goldlucke, B., Cremers, D., Magnor, M.[Marcus],
Motion Field Estimation from Alternate Exposure Images,
PAMI(33), No. 8, August 2011, pp. 1577-1589.
IEEE DOI 1107
BibRef
Earlier: A1, A2, A5, Only:
Two Algorithms for Motion Estimation from Alternate Exposure Images,
CompVideo10(25-51).
Springer DOI 1111
BibRef
Earlier: A1, A2, A5, Only:
Motion field and occlusion time estimation via alternate exposure flow,
ICCP09(1-8).
IEEE DOI 1208
Use an additional long exposure image (blurring) for motion field. Also adds time of occlusion. BibRef

Sellent, A.[Anita], Ruhl, K.[Kai], Magnor, M.[Marcus],
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IVC(30), No. 9, September 2012, pp. 641-654.
Elsevier DOI 1210
Multi-view video; Correspondence estimation; Confidence measure; Optical flow BibRef

Sellent, A.[Anita], Linz, C.[Christian], Magnor, M.[Marcus],
Consistent optical flow for stereo video,
ICIP10(1-4).
IEEE DOI 1009
BibRef

Héas, P., Herzet, C., Mémin, E.,
Bayesian Inference of Models and Hyperparameters for Robust Optical-Flow Estimation,
IP(21), No. 4, April 2012, pp. 1437-1451.
IEEE DOI 1204
BibRef
Earlier:
Robust Optic-Flow Estimation with Bayesian Inference of Model and Hyper-parameters,
SSVM11(773-785).
Springer DOI 1201
BibRef

Becker, F.[Florian], Wieneke, B., Petra, S., Schroder, A., Schnorr, C.[Christoph],
Variational Adaptive Correlation Method for Flow Estimation,
IP(21), No. 6, June 2012, pp. 3053-3065.
IEEE DOI 1202
BibRef
Earlier: Corrections: IP(21), No. 8, August 2012, pp. 3813-3814.
IEEE DOI 1208
BibRef

Becker, F.[Florian], Lenzen, F.[Frank], Kappes, J.H.[Jörg H.], Schnörr, C.[Christoph],
Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences,
IJCV(105), No. 3, December 2013, pp. 269-297.
Springer DOI 1309
BibRef
Earlier: ICCV11(1692-1699).
IEEE DOI 1201
BibRef

Becker, F.[Florian], Schnörr, C.[Christoph],
Decomposition of Quadratic Variational Problems,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Bodnariuc, E.[Ecaterina], Petra, S.[Stefania], Poelma, C.[Christian], Schnörr, C.[Christoph],
Parametric Dictionary-Based Velocimetry for Echo PIV,
GCPR16(332-343).
Springer DOI 1611
BibRef

Bodnariuc, E.[Ecaterina], Gurung, A.[Arati], Petra, S.[Stefania], Schnörr, C.[Christoph],
Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV,
EMMCVPR15(378-391).
Springer DOI 1504
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Yamashita, Y.[Yuya], Harada, T.[Tatsuya], Kuniyoshi, Y.[Yasuo],
Causal Flow,
MultMed(14), No. 3, 2012, pp. 619-629.
IEEE DOI 1202
The dominant real motion. Model as pixel-to-pixel information transfer, not motion of pixels. BibRef

Jia, K.[Kui], Wang, X.G.[Xiao-Gang], Tang, X.[Xiaoou],
Image Transformation Based on Learning Dictionaries across Image Spaces,
PAMI(35), No. 2, February 2013, pp. 367-380.
IEEE DOI 1301
BibRef
Earlier:
Optical flow estimation using learned sparse model,
ICCV11(2391-2398).
IEEE DOI 1201
Used for superresolution and shading or albedo extraction. BibRef

Yan, B.[Bo], Chen, Y.[Yue],
Low complexity image interpolation method based on path selection,
JVCIR(24), No. 6, August 2013, pp. 661-668.
Elsevier DOI 1306
Low complexity; Image animation; Pixel interlacing; Path-based interpolation; Image interpolation; View interpolation; Optical flow; Sub-pixel strategy BibRef

Drulea, M., Nedevschi, S.,
Motion Estimation Using the Correlation Transform,
IP(22), No. 8, 2013, pp. 3260-3270.
IEEE DOI 1307
Correlation transform; changes in illumination; correlation-based descriptors BibRef

Garg, R.[Ravi], Roussos, A.[Anastasios], Agapito, L.[Lourdes],
A Variational Approach to Video Registration with Subspace Constraints,
IJCV(104), No. 3, September 2013, pp. 286-314.
WWW Link.
Springer DOI 1308
BibRef
Earlier:
Robust Trajectory-Space TV-L1 Optical Flow for Non-rigid Sequences,
EMMCVPR11(300-314).
Springer DOI 1107
non-rigid registration, or optic flow computation. BibRef

Garg, R.[Ravi], Pizarro, L.[Luis], Rueckert, D.[Daniel], Agapito, L.[Lourdes],
Dense Multi-frame Optic Flow for Non-rigid Objects Using Subspace Constraints,
ACCV10(IV: 460-473).
Springer DOI 1011
BibRef

Kumar, A., Tung, F., Wong, A., Clausi, D.A.,
A Decoupled Approach to Illumination-Robust Optical Flow Estimation,
IP(22), No. 10, 2013, pp. 4136-4147.
IEEE DOI 1309
Optical flow BibRef

Chantas, G.[Giannis], Gkamas, T.[Theodosios], Nikou, C.[Christophoros],
Variational-Bayes Optical Flow,
JMIV(50), No. 3, November 2014, pp. 199-213.
Springer DOI 1410
BibRef
Earlier: A2, A1, A3:
A probabilistic formulation of the optical flow problem,
ICPR12(754-757).
WWW Link. 1302
BibRef

Solari, F.[Fabio], Chessa, M.[Manuela], Medathati, N.V.K.[N.V. Kartheek], Kornprobst, P.[Pierre],
What can we expect from a V1-MT feedforward architecture for optical flow estimation?,
SP:IC(39, Part B), No. 1, 2015, pp. 342-354.
Elsevier DOI 1512
Optical flow BibRef

Bengtsson, T.[Tomas], McKelvey, T.[Tomas], Lindström, K.[Konstantin],
On robust optical flow estimation on image sequences with differently exposed frames using primal-dual optimization,
IVC(57), No. 1, 2017, pp. 78-88.
Elsevier DOI 1702
Optical flow estimation BibRef

Chen, J.[Jun], Cai, Z.M.[Ze-Min], Lai, J.H.[Jian-Huang], Xie, X.H.[Xiao-Hua],
Fast Optical Flow Estimation Based on the Split Bregman Method,
CirSysVideo(28), No. 3, March 2018, pp. 664-678.
IEEE DOI 1804
Computational modeling, Convergence, Estimation, Mathematical model, Optical imaging, Optimization, optical flow estimation BibRef

Chen, J.[Jun], Cai, Z.M.[Ze-Min], Lai, J.H.[Jian-Huang], Xie, X.H.[Xiao-Hua],
A Filtering-Based Framework for Optical Flow Estimation,
CirSysVideo(29), No. 5, May 2019, pp. 1350-1364.
IEEE DOI 1905
Optical imaging, Estimation, Adaptive optics, Optical filters, Optical design, Algorithm design and analysis, 3D filtering BibRef

Kong, K., Shin, S., Lee, J., Song, W.,
How to Estimate Global Motion Non-Iteratively From a Coarsely Sampled Motion Vector Field,
CirSysVideo(29), No. 12, December 2019, pp. 3729-3742.
IEEE DOI 1912
Motion estimation, Mathematical model, Estimation, Cameras, Linear programming, Video sequences, Motion segmentation, outlier removal BibRef

Zhang, C., Ge, L., Chen, Z., Li, M., Liu, W., Chen, H.,
Refined TV-L1 Optical Flow Estimation Using Joint Filtering,
MultMed(22), No. 2, February 2020, pp. 349-364.
IEEE DOI 2001
Optical flow, Filtering, Image edge detection, Estimation, Optical filters, Linear programming, Optical flow, edge-preserving BibRef

Mei, L., Lai, J., Xie, X., Zhu, J., Chen, J.,
Illumination-Invariance Optical Flow Estimation Using Weighted Regularization Transform,
CirSysVideo(30), No. 2, February 2020, pp. 495-508.
IEEE DOI 2002
Lighting, Transforms, Optical imaging, Estimation, Computed tomography, Standards, Image color analysis, Optical flow, face liveness detection BibRef

Young, S.I.[Sean I.], Naman, A.T., Taubman, D.S.[David S.],
Graph Laplacian Regularization for Robust Optical Flow Estimation,
IP(29), 2020, pp. 3970-3983.
IEEE DOI 2002
BibRef
And: A1, A3, Only:
Rate-distortion optimized optical flow estimation,
ICIP15(1677-1681)
IEEE DOI 1512
Laplace equations, Estimation, Optical imaging, Kernel, Eigenvalues and eigenfunctions, Optimization, Inverse problems, robust estimation. motion estimation BibRef

Tian, L.[Long], Tu, Z.G.[Zhi-Gang], Zhang, D.J.[De-Jun], Liu, J.[Jun], Li, B.X.[Bao-Xin], Yuan, J.S.[Jun-Song],
Unsupervised Learning of Optical Flow With CNN-Based Non-Local Filtering,
IP(29), 2020, pp. 8429-8442.
IEEE DOI 2008
Optical imaging, Optical computing, Optical losses, Optical variables control, Estimation, Optical sensors, occlusion map BibRef

Chen, J., Lai, J., Cai, Z., Xie, X., Pan, Z.,
Optical Flow Estimation Based on the Frequency-Domain Regularization,
CirSysVideo(31), No. 1, January 2021, pp. 217-230.
IEEE DOI 2101
Optical imaging, Frequency-domain analysis, Wavelet transforms, Estimation, Image edge detection, Integrated optics, wavelet transform BibRef

Bhandari, K.[Keshav], Zong, Z.L.[Zi-Liang], Yan, Y.[Yan],
Revisiting Optical Flow Estimation in 360 Videos,
ICPR21(8196-8203)
IEEE DOI 2105
Training, Convolution, Wearable computers, Estimation, Optical distortion, Computer architecture, Distortion BibRef

Lê, H.Â.[Hoàng-Ân], Nimbhorkar, T.[Tushar], Mensink, T.[Thomas], Baslamisli, A.S.[Anil S.], Karaoglu, S.[Sezer], Gevers, T.[Theo],
Automatic generation of dense non-rigid optical flow,
CVIU(212), 2021, pp. 103274.
Elsevier DOI 2110
non-rigi, optical flow, dataset, generation, as-rigid-as-possible BibRef

Song, X.L.[Xiao-Lin], Zhao, Y.Y.[Yu-Yang], Yang, J.Y.[Jing-Yu],
STC-Flow: Spatio-temporal context-aware optical flow estimation,
SP:IC(99), 2021, pp. 116441.
Elsevier DOI 2111
Spatio-temporal network, Context modeling, Optical flow estimation, Deep learning BibRef

Luo, W.[Wei], Zhang, F.L.[Fang-Long], Yang, J.[Jian], Yang, J.Y.[Jing-Yu],
Region Tree Based Sparse Model for Optical Flow Estimation,
ICPR14(2077-2082)
IEEE DOI 1412
Dictionaries BibRef

Boquet-Pujadas, A.[Aleix], Olivo-Marin, J.C.[Jean-Christophe],
Reformulating Optical Flow to Solve Image-Based Inverse Problems and Quantify Uncertainty,
PAMI(45), No. 5, May 2023, pp. 6125-6141.
IEEE DOI 2304
Uncertainty, Force, Brightness, Meteorology, Mathematical models, Image reconstruction, Inverse problems, Inverse problems, mechanobiology BibRef

Shi, H.[Hao], Zhou, Y.F.[Yi-Fan], Yang, K.L.[Kai-Lun], Yin, X.T.[Xiao-Ting], Wang, Z.[Ze], Ye, Y.Z.[Yao-Zu], Yin, Z.[Zhe], Meng, S.[Shi], Li, P.[Peng], Wang, K.W.[Kai-Wei],
PanoFlow: Learning 360° Optical Flow for Surrounding Temporal Understanding,
ITS(24), No. 5, May 2023, pp. 5570-5585.
IEEE DOI 2305
Optical flow, Estimation, Optical distortion, Distortion, Image motion analysis, Cameras, Intelligent vehicles, synthetic dataset BibRef

Deng, C.X.[Chang-Xing], Luo, A.[Ao], Huang, H.B.[Hai-Bin], Ma, S.[Shaodan], Liu, J.Y.[Jiang-Yu], Liu, S.C.[Shuai-Cheng],
Explicit Motion Disentangling for Efficient Optical Flow Estimation,
ICCV23(9487-9496)
IEEE DOI Code:
WWW Link. 2401
BibRef

Fan, Z.Y.[Zheng-Yuan], Cai, Z.[Zemin],
Random epipolar constraint loss functions for supervised optical flow estimation,
PR(148), 2024, pp. 110141.
Elsevier DOI Code:
WWW Link. 2402
Optical flow estimation, Epipolar geometry, Lightweight neural network BibRef

Tchenko, Y.C.[Yanick Christian], Abdelkader, H.H.[Hicham Hadj], Tabia, H.[Hedi],
Draft - Distilled Recurrent All-Pairs Field Transforms for Optical Flow,
ICIP24(1547-1553)
IEEE DOI Code:
WWW Link. 2411
Deep learning, Adaptation models, Accuracy, Transforms, Benchmark testing, Feature extraction, Optical flow, Deep Learning, 3D reconstruction BibRef

Lin, J.M.[Jamie Menjay], Jeong, J.[Jisoo], Cai, H.[Hong], Garrepalli, R.[Risheek], Wang, K.[Kai], Porikli, F.M.[Fatih M.],
SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations,
ECVW24(2162-2171)
IEEE DOI 2410
Optical losses, Training, Performance evaluation, Image motion analysis, Computational modeling, Estimation, Confidence BibRef

Xu, J.W.[Jia-Wei], Lu, Z.Q.[Zong-Qing], Liao, Q.M.[Qing-Min],
LLA-Flow: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation,
ICIP23(3220-3224)
IEEE DOI 2312
BibRef

Kesenci, Y.[Yekta], Boquet-Pujadas, A.[Aleix], van Bodegraven, E.[Emma], Étienne-Manneville, S.[Sandrine], Labruyère, E.[Elisabeth], Olivo-Marin, J.C.[Jean-Christophe],
PDE-Constrained Optimization for Nuclear Mechanics,
ICIP22(2192-2195)
IEEE DOI 2211
Proteins, Deformable models, Computational modeling, Force, Optimization, Optical flow, Nuclear mechanics, adjoint method. BibRef

Goyal, A.[Ankit], Mousavian, A.[Arsalan], Paxton, C.[Chris], Chao, Y.W.[Yu-Wei], Okorn, B.[Brian], Deng, J.[Jia], Fox, D.[Dieter],
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement,
CVPR22(14767-14777)
IEEE DOI 2210

HTML Version. Training, Minimization, Iterative algorithms, Data models, Pattern recognition, Optical flow, Robot vision, Vision applications and systems BibRef

Sui, X.C.[Xiu-Chao], Li, S.H.[Shao-Hua], Geng, X.[Xue], Wu, Y.[Yan], Xu, X.X.[Xin-Xing], Liu, Y.[Yong], Goh, R.[Rick], Zhu, H.Y.[Hong-Yuan],
CRAFT: Cross-Attentional Flow Transformer for Robust Optical Flow,
CVPR22(17581-17590)
IEEE DOI 2210
Convolutional codes, Image motion analysis, Correlation, Smoothing methods, Semantics, Estimation, Low-level vision, Video analysis and understanding BibRef

Sherina, E.[Ekaterina], Krainz, L.[Lisa], Hubmer, S.[Simon], Drexler, W.[Wolfgang], Scherzer, O.[Otmar],
Challenges for Optical Flow Estimates in Elastography,
SSVM21(128-139).
Springer DOI 2106
BibRef

Godet, P.[Pierre], Boulch, A.[Alexandre], Plyer, A.[Aurélien], Besnerais, G.L.[Guy Le],
STaRFlow: A SpatioTemporal Recurrent Cell for Lightweight Multi-Frame Optical Flow Estimation,
ICPR21(2462-2469)
IEEE DOI 2105
Tensors, Redundancy, Estimation, Writing, Spatial databases, Robustness, Spatiotemporal phenomena BibRef

Zhao, R., Xiong, R., Zhu, S., Zeng, B., Huang, T., Gao, W.,
Optical Flow Estimation Between Images of Different Resolutions via Variational Method,
VCIP20(427-430)
IEEE DOI 2102
Optical imaging, Image resolution, Estimation, Task analysis, Nonlinear optics, Mathematical model, Lighting, Optical flow, aliasing BibRef

Im, W.B.[Woo-Bin], Kim, T.K.[Tae-Kyun], Yoon, S.E.[Sung-Eui],
Unsupervised Learning of Optical Flow with Deep Feature Similarity,
ECCV20(XXIV:172-188).
Springer DOI 2012
BibRef

Zheng, Y., Zhang, M., Lu, F.,
Optical Flow in the Dark,
CVPR20(6748-6756)
IEEE DOI 2008
Optical sensors, Brightness, Adaptive optics, Optical noise, Optical flow, Cameras BibRef

Hur, J.[Junhwa], Roth, S.[Stefan],
Self-Supervised Multi-Frame Monocular Scene Flow,
CVPR21(2683-2693)
IEEE DOI 2111
BibRef
Earlier:
Self-Supervised Monocular Scene Flow Estimation,
CVPR20(7394-7403)
IEEE DOI 2008
Training, Solid modeling, Visualization, Runtime, Pipelines, Real-time systems. Optical imaging, Estimation, Cameras, Optical losses, Decoding, Task analysis BibRef

Bar-Haim, A., Wolf, L.,
ScopeFlow: Dynamic Scene Scoping for Optical Flow,
CVPR20(7995-8004)
IEEE DOI 2008
Training, Optical imaging, Agriculture, Estimation, Protocols, Benchmark testing, Adaptive optics BibRef

Liu, L.[Liang], Zhang, J.N.[Jiang-Ning], He, R.F.[Rui-Fei], Liu, Y.[Yong], Wang, Y.B.[Ya-Biao], Tai, Y.[Ying], Luo, D.H.[Dong-Hao], Wang, C.J.[Cheng-Jie], Li, J.L.[Ji-Lin], Huang, F.Y.[Fei-Yue],
Learning by Analogy: Reliable Supervision From Transformations for Unsupervised Optical Flow Estimation,
CVPR20(6488-6497)
IEEE DOI 2008
Optical imaging, Training, Optical variables control, Reliability, Pipelines, Adaptive optics, Unsupervised learning BibRef

Yang, G., Deng, Z., Wang, S., Li, Z.,
Masked Label Learning for Optical Flow Regression,
ICPR18(1139-1144)
IEEE DOI 1812
Computational modeling, Adaptation models, Optical imaging, Training, Task analysis, Calibration, Optical losses BibRef

Ramakrishnan, N., Prakash, A., Srikanthan, T.,
Low-Complexity Global Motion Estimation for Aerial Vehicles,
ECVW17(402-410)
IEEE DOI 1709
Cameras, Complexity theory, Computational modeling, Estimation, Feature extraction, Surveillance, Videos BibRef

Dahlan, H.A., Hancock, E.R., Smith, W.A.P.,
Reflectance-aware optical flow,
ICPR16(2860-2865)
IEEE DOI 1705
Image color analysis, Light emitting diodes, Light sources, Lighting, Optical imaging, Optical sensors, Optical, variables, control BibRef

Bergamasco, F.[Filippo], Torsello, A.[Andrea], Robles-Kelly, A.[Antonio],
Spectral Dichromatic Parameter Recovery from Two Views via Total Variation Hyper-priors,
HISP16(I: 317-333).
Springer DOI 1704
Joint estimation of illuminant, reflectance, and shading of each pixel, as well as the optical flow between the two views. BibRef

Pathak, S., Moro, A., Yamashita, A., Asama, H.,
A decoupled virtual camera using spherical optical flow,
ICIP16(4488-4492)
IEEE DOI 1610
Adaptive optics BibRef

Snape, P.[Patrick], Roussos, A.[Anastasios], Panagakis, Y.[Yannis], Zafeiriou, S.P.[Stefanos P.],
Face Flow,
ICCV15(2993-3001)
IEEE DOI 1602
multi-frame optical flow in an expressive sequence of facial images. BibRef

Walker, J., Gupta, A., Hebert, M.,
Dense Optical Flow Prediction from a Static Image,
ICCV15(2443-2451)
IEEE DOI 1602
Context BibRef

Blu, T.[Thierry], Moulin, P.[Pierre], Gilliam, C.[Christopher],
Approximation order of the LAP optical flow algorithm,
ICIP15(48-52)
IEEE DOI 1512
Optical flow; Padé approximante; all-pass filtering; approximation BibRef

Li, J.Z.[Ji-Zhou], Gilliam, C.[Christopher], Blu, T.[Thierry],
A multi-frame optical flow spot tracker,
ICIP15(3670-3674)
IEEE DOI 1512
Spot tracking BibRef

Elliethy, A.S.[Ahmed S.], Sharma, G.[Gaurav],
Improved specular regions localization and optical-flow based motion estimation via joint processing,
ICIP15(232-236)
IEEE DOI 1512
Specular region estimation; motion estimation; optical flow BibRef

Jeong, J.[Jisoo], Lin, J.M.[Jamie Menjay], Porikli, F.M.[Fatih M.], Kwak, N.[Nojun],
Imposing Consistency for Optical Flow Estimation,
CVPR22(3171-3181)
IEEE DOI 2210
Tracking, Annotations, Estimation, Self-supervised learning, Semisupervised learning, Benchmark testing, Motion and tracking BibRef

Park, S.[Sungheon], Kwak, N.[Nojun],
Illumination robust optical flow estimation by illumination-chromaticity decoupling,
ICIP15(1910-1914)
IEEE DOI 1512
HSL color space; Optical flow; illumination robust BibRef

Luo, Y.[Ye], Cheong, L.F.[Loong-Fah], Cabibihan, J.J.[John-John],
Modeling the Temporality of Saliency,
ACCV14(III: 205-220).
Springer DOI 1504
Evolution of changes over multiple frames. BibRef

Vacar, C.[Cornelia], Cheriet, F.[Farida],
Robust probabilistic optical flow for video sequences,
ICIP14(1962-1966)
IEEE DOI 1502
Approximation algorithms BibRef

Daraei, M.H.[Mohammad Hossein],
Optical Flow Computation in the Presence of Spatially-Varying Motion Blur,
ISVC14(I: 140-150).
Springer DOI 1501
BibRef

Drews, P.[Paulo], Nascimento, E.R.[Erickson R.], Xavier, A.[Arthur], Campos, M.[Mario],
Generalized Optical Flow Model for Scattering Media,
ICPR14(3999-4004)
IEEE DOI 1412
Adaptive optics BibRef

Fan, M.Y.[Ming-Ying], Imiya, A.[Atsushi], Kawamoto, K.[Kazuhiko],
Affine Colour Optical Flow Computation,
CAIP13(507-514).
Springer DOI 1308
BibRef

Stoll, M.[Michael], Maurer, D.[Daniel], Bruhn, A.[Andrés],
Variational Large Displacement Optical Flow Without Feature Matches,
EMMCVPR17(79-92).
Springer DOI 1805
BibRef

Stoll, M.[Michael], Volz, S.[Sebastian], Bruhn, A.[Andrés],
Adaptive Integration of Feature Matches into Variational Optical Flow Methods,
ACCV12(III:1-14).
Springer DOI 1304
BibRef

Li, W.B.[Wen-Bin], Chen, Y.[Yang], Lee, J.[Jee_Hang], Ren, G.[Gang], Cosker, D.[Darren],
Robust optical flow estimation for continuous blurred scenes using RGB-motion imaging and directional filtering,
WACV14(792-799)
IEEE DOI 1406
Cameras BibRef

Li, W.B.[Wen-Bin], Cosker, D.[Darren], Brown, M.[Matthew], Tang, R.[Rui],
Optical Flow Estimation Using Laplacian Mesh Energy,
CVPR13(2435-2442)
IEEE DOI 1309
Laplacian Mesh; Optical Flow BibRef

Li, W.B.[Wen-Bin], Cosker, D.[Darren], Brown, M.[Matthew],
An Anchor Patch Based Optimization Framework for Reducing Optical Flow Drift in Long Image Sequences,
ACCV12(III:112-125).
Springer DOI 1304
BibRef

Lim, H.J.[Hyung-Jun], Kim, D.Y.[Dong-Yoon], Choi, J.[Joonsung], Park, S.H.[Seung-Ho], Park, S.H.[Se Hyeok], Kim, J.H.[Jae Hyun], Park, H.W.[Hyun-Wook],
An optimal motion vector regularization method using variance-distortion curve,
ICIP12(1525-1528).
IEEE DOI 1302
BibRef

Portz, T.[Travis], Zhang, L.[Li], Jiang, H.R.[Hong-Rui],
Optical flow in the presence of spatially-varying motion blur,
CVPR12(1752-1759).
IEEE DOI 1208
BibRef

Wang, H.B.[Hai-Bo], Pan, C.H.[Chun-Hong], Davoine, F.[Franck], Liu, S.G.[Shao-Guo],
Hierarchical fusion of descriptor matching and L-K optical flow,
ICIP11(1893-1896).
IEEE DOI 1201
BibRef

Quelin, M., Bouzerdoum, A., Phung, S.L.[Son Lam],
Fast digital optical flow estimation based on EMD,
EUVIP10(155-158).
IEEE DOI 1110
BibRef

Maier, J.[Josef], Ambrosch, K.[Kristian],
Distortion Compensation for Movement Detection Based on Dense Optical Flow,
ISVC11(I: 168-179).
Springer DOI 1109
BibRef

Puxbaum, P.[Philipp], Ambrosch, K.[Kristian],
Gradient-Based Modified Census Transform for Optical Flow,
ISVC10(I: 437-448).
Springer DOI 1011
BibRef

Chin, Y.[Yi], Tsai, C.J.[Chun-Jen],
Dense true motion field compensation for video coding,
ICIP13(1958-1961)
IEEE DOI 1402
BibRef
Earlier:
Bayesian dense motion field estimation with landmark constraint,
ICIP10(773-776).
IEEE DOI 1009
decoder-side motion estimation BibRef

Marti, R.[Robert], Noble, J.A.[J. Alison],
Elastic modulus imaging using optical flow and image registration,
ICIP10(605-608).
IEEE DOI 1009
BibRef

Hossain, I.[Imtiaz], Gunturk, B.[Bahadir],
Joint photometric registration and optical flow estimation,
ICIP10(1201-1204).
IEEE DOI 1009
BibRef

Gai, J.D.[Jia-Ding], Stevenson, R.L.[Robert L.],
Optical flow estimation with p-harmonic regularization,
ICIP10(1969-1972).
IEEE DOI 1009
BibRef

Glocker, B.[Ben], Heibel, T.H.[T. Hauke], Navab, N.[Nassir], Kohli, P.[Pushmeet], Rother, C.[Carsten],
TriangleFlow: Optical Flow with Triangulation-Based Higher-Order Likelihoods,
ECCV10(III: 272-285).
Springer DOI 1009

See also Discrete tracking of parametrized curves. BibRef

Schoueri, Y.[Yasmina], Scaccia, M.[Milena], Rekleitis, I.[Ioannis],
Optical Flow from Motion Blurred Color Images,
CRV09(1-7).
IEEE DOI 0905
BibRef

Rodríguez, A.L.[Antonio L.], López-de-Teruel, P.E.[Pedro E.], Ruiz, A.[Alberto],
Real-Time Descriptorless Feature Tracking,
CIAP09(853-862).
Springer DOI 0909
Long-term sparse optical flow. BibRef

Lin, D.[Dahua], Grimson, W.E.L.[W. Eric L.], Fisher, J.W.[John W.],
Modeling and estimating persistent motion with geometric flows,
CVPR10(1-8).
IEEE DOI 1006
BibRef
Earlier:
Learning visual flows: A Lie algebraic approach,
CVPR09(747-754).
IEEE DOI 0906
BibRef

Fehr, J.[Janis],
Local Rotation Invariant Patch Descriptors for 3D Vector Fields,
ICPR10(1381-1384).
IEEE DOI 1008
BibRef

Fehr, J.[Janis], Reisert, M.[Marco], Burkhardt, H.[Hans],
Cross-Correlation and Rotation Estimation of Local 3D Vector Field Patches,
ISVC09(I: 287-296).
Springer DOI 0911
BibRef
Earlier:
Fast and Accurate Rotation Estimation on the 2-Sphere without Correspondences,
ECCV08(II: 239-251).
Springer DOI 0810
BibRef

Glocker, B.[Ben], Komodakis, N.[Nikos], Paragios, N.[Nikos], Navab, N.[Nassir],
Approximated Curvature Penalty in Non-rigid Registration Using Pairwise MRFs,
ISVC09(I: 1101-1109).
Springer DOI 0911
BibRef

Glocker, B.[Ben], Paragios, N.[Nikos], Komodakis, N.[Nikos], Tziritas, G.[Georgios], Navab, N.[Nassir],
Optical flow estimation with uncertainties through dynamic MRFs,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Fashandi, H.[Homa], Fazel-Rezai, R.[Reza], Pistorius, S.[Stephen],
Optical Flow and Total Least Squares Solution for Multi-scale Data in an Over-Determined System,
ISVC07(II: 33-42).
Springer DOI 0711
BibRef

Fahad, A.[Ahmed], Morris, T.[Tim],
Multiple Combined Constraints for Optical Flow Estimation,
ISVC07(II: 11-20).
Springer DOI 0711
BibRef

Chen, W.X.[Wei-Xin], Barron, J.L.[John L.],
High Accuracy Optical Flow Method Based on a Theory for Warping: 3D Extension,
ICIAR10(I: 250-262).
Springer DOI 1006
BibRef

Faisal, M.[Mohammad], Barron, J.L.[John L.],
High Accuracy Optical Flow Method Based on a Theory for Warping: Implementation and Qualitative/Quantitative Evaluation,
ICIAR07(513-525).
Springer DOI 0708
BibRef

Dong, G.[Gang], Baskin, T.I., Palaniappan, K.,
Motion Flow Estimation from Image Sequences with Applications to Biological Growth and Motility,
ICIP06(1245-1248). 0610

IEEE DOI BibRef

Sun, Z.H.[Zhao-Hui],
A Three-Frame Approach to Constraint-Consistent Motion Estimation,
ICPR06(I: 35-38).
IEEE DOI 0609
BibRef

Wang, H.Y.[Hai-Yun], Ma, K.K.[Kai-Kuang],
Accurate Optical Flow Estimation in Noisy Sequences by Robust Tensor-driven Anisotropic Diffusion,
ICIP05(III: 1292-1295).
IEEE DOI 0512
BibRef

Karantzalos, K., Paragios, N.,
Higher Order Polynomials, Free Form Deformations and Optical Flow Estimation,
ICIP05(III: 1280-1283).
IEEE DOI 0512
BibRef

Kim, J.[Jangheon], Sikora, T.,
Hybrid Recursive Energy-based Method for Robust Optical Flow on Large Motion Fields,
ICIP05(I: 129-132).
IEEE DOI 0512
BibRef

Hamid, R., Bobick, A., Yezzi, A.J.,
Audio-visual flow: A variational approach to multi-modal flow estimation,
ICIP04(IV: 2563-2566).
IEEE DOI 0505
BibRef

Stein, F.[Fridtjof],
Efficient Computation of Optical Flow Using the Census Transform,
DAGM04(79-86).
Springer DOI 0505
BibRef

Myerscough, P.J.,
Guiding Optical Flow Estimation,
BMVC03(xx-yy).
HTML Version. 0409
BibRef

Goncalves, N.[Nuno], Araujo, H.[Helder],
Linear solution for the pose estimation of noncentral catadioptric systems,
OMNIVIS07(1-7).
IEEE DOI 0710
BibRef
Earlier:
Projection model, 3D reconstruction and rigid motion estimation from non-central catadioptric images,
3DPVT04(325-332).
IEEE DOI 0412
BibRef
And:
Rigid motion estimation from non-central catadioptric images,
ICPR04(IV: 268-271).
IEEE DOI 0409

See also Fitting conics to paracatadioptric projections of lines. BibRef

Gupta, D., Daniilidis, K.,
Planar motion of a parabolic catadioptric camera,
ICPR04(IV: 68-71).
IEEE DOI 0409
BibRef

Ying, X.H.[Xiang-Hua], Hu, Z.Y.[Zhan-Yi],
Spherical objects based motion estimation for catadioptric cameras,
ICPR04(III: 231-234).
IEEE DOI 0409
BibRef

Eriksson, M., Carlsson, S.,
Maximizing validity in 2d motion analysis,
ICPR04(II: 179-183).
IEEE DOI 0409
BibRef

Stratmann, I.,
Omnidirectional imaging and optical flow,
OMNIVIS02(104-111).
IEEE Abstract. 0310
BibRef

Coquin, D., Bolon, P.,
A new method to compute the distortion vector field from two images,
ICPR02(I: 279-282).
IEEE DOI 0211
BibRef

Barron, J.L., Klette, R.,
Quantitative color optical flow,
ICPR02(IV: 251-255).
IEEE DOI 0211
BibRef

Auclair-Fortier, M.F., Poulin, P., Ziou, D., Allili, M.,
A computational algebraic topology approach for optical flow,
ICPR02(I: 352-355).
IEEE DOI 0211
BibRef

Auclair-Fortier, M.F., Poulin, P., Ziou, D., Allili, M.,
A Computational Algebraic Topology Model for the Deformation of Curves,
AMDO02(56 ff).
Springer DOI 0303
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Makhervaks, V., Barequet, G., Bruckstein, A.M.,
Image flows and one-liner graphical image representation,
ICPR02(I: 640-643).
IEEE DOI 0211
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Lim, S., El Gamal, A.,
Optical Flow Estimation Using High Frame Rate Sequences,
ICIP01(II: 925-928).
IEEE DOI 0108
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Baumela, L., de Agapito, L., Bustos, P., Reid, I.D.,
Motion Estimation Using the Differential Epipolar Equation,
ICPR00(Vol III: 840-843).
IEEE DOI
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Yao, J.,
Visual Motion Estimation Via Second Order Cone Programming,
ICIP00(Vol III: 604-607).
IEEE DOI 0008
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Chen, L.,
A Novel Affine Invariant Feature Set and Its Application in Motion Estimation,
ICIP00(Vol III: 612-615).
IEEE DOI 0008
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Roy, S.[Sebastien], Govindu, V.[Venu],
MRF Solutions for Probabilistic Optical Flow Formulations,
ICPR00(Vol III: 1041-1047).
IEEE DOI 0009
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Qiu, M.L.[Mao-Lin],
Computing Optical Flow Based on the Mass-conserving Assumption,
ICPR00(Vol III: 1029-1032).
IEEE DOI 0009
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Kristoffersen, E., Austvoll, I., Engan, K.,
Dense Motion Field Estimation Using Spatial Filtering and Quasi Eigenfunction Approximations,
ICIP05(III: 1268-1271).
IEEE DOI 0512
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Austvoll, I.[Ivar],
Directional Filters and a New Structure for Estimation of Optical Flow,
ICIP00(Vol II: 574-577).
IEEE DOI 0008
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Clocksin, W.,
A New Method for Computing Optical Flow,
BMVC00(xx-yy).
PDF File. 0009
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Socolinsky, D.A.[Diego A.], Wolff, L.B.[Lawrence B.],
Multispectral Optic Flow,
DARPA98(755-760).
See also Multispectral image visualization through first-order fusion. BibRef 9800

Lundberg, A.J.[Andrew J.], Wolff, L.B.[Lawrence B.],
Optic Flow Estimation from 3D Wavelet Edge Detection,
DARPA97(375-378). BibRef 9700

Mester, R., Mühlich, M.,
Improving Motion and Orientation Estimation Using an Equilibrated Total Least Squares Approach,
ICIP01(II: 929-932).
IEEE DOI 0108
BibRef
Earlier: A2, A1:
The role of total least squares in motion analysis,
ECCV98(II: 305).
Springer DOI BibRef

Sporring, J.[Jon], and Nielsen, M.[Mads],
Direct estimation of First Order Optic Flow,
TAIA95(225-238). First order optic flow using Lie derivatives to make spatial filters where the flow is measured as Fourier phase shift. BibRef 9500

Kothari, R., and Bellando, J.,
Optical Flow Determination Using Topology Preserving Mappings,
ICIP97(III: 344-347).
IEEE DOI BibRef 9700

Giaccone, P.R., Greenhill, D.R., and Jones, G.A.,
Recovering Very Large Visual Motion Fields,
SCIA97(xx-yy) 9705

HTML Version. BibRef

Arnspang, J.,
Optic Acceleration,
ICCV88(364-373).
IEEE DOI BibRef 8800

Rougee, A., Levy, B.C., Willsky, A.S.,
Reconstruction of Two-Dimensional Velocity Fields as a Linear Estimation Problem,
ICCV87(646-650). BibRef 8700

Lai, J., Gauch, J., and Crisman, J.,
Using Color to Computer Optical Flow,
SPIE(2056), 1993, pp. 186-194. BibRef 9300

Cooper, D.H.[David H.], Madsen, B.R.[Bo René], Graham, J.[Jim],
Estimating Motion in Ultrasound Images of the Small Bowel: Optical Flow without Image Structure,
SCIA03(571-578).
Springer DOI 0310
BibRef

Cooper, D.H., and Graham, J.,
Estimating Motion in Noisy, Textured Images: Optical Flow in Medical Ultrasound,
BMVC96(Poster Session 2). 9608
University of Manchester BibRef

Lee, D., Papageorgiou, A., and Wasilkowski, G.W.,
Computing Optical Flow,
Motion89(99-106). BibRef 8900
Earlier:
Computational Aspects of Determining Optical Flow,
ICCV88(612-618).
IEEE DOI A study of some aspects (quote from abstract). BibRef

Blicher, A.P.[A. Peter], and Omohundro, S.M.[Stephen M.],
Unique Recovery of Motion and Optic Flow Via Lie Algebras,
IJCAI85(889-891). An abstract method that shows rigid 3D motion recovery is possible from the time derivative of smooth monochrome image at 6 points, or 2 points for color. BibRef 8500

Rodrigues, V., Castan, S., and Pailhes, L.M.,
Displacement Vector Field Computation by Temporal Covariance Model,
CVPR85(212-214). (Laboratoire CERFIA) Preliminary. BibRef 8500

Huang, L.Q.[Liu-Qing], Aloimonos, Y.[Yiannis],
How Normal Flow Constrains Relative Depth for an Active Observer,
IVC(12), No. 7, September 1994, pp. 435-445.
Elsevier DOI BibRef 9409
Earlier:
Relative Depth from Motion Using Normal Flow: An Active and Purposive Solution,
Motion91(196-204). You can get relative information without computing optical flow. BibRef

Huang, L., Aloimonos, Y.,
The geometry of visual interception,
CVPR92(741-743).
IEEE DOI 0403
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Nelson, R.C., and Aloimonos, Y.,
Obstacle Avoidance Using Flow Field Divergence,
PAMI(11), No. 10, October 1989, pp. 1102-1106.
IEEE DOI BibRef 8910
Earlier:
Using Flow Field Divergence for Obstacle Avoidance: Towards Qualitative Vision,
ICCV88(188-196).
IEEE DOI Flow field divergence is a clue to obstacles. Simple system. Terrible bibliography with many errors in it. BibRef

Nelson, R.C., and Aloimonos, Y.,
Finding Motion Parameters from Spherical Flow Fields (or the Advantages of Having Eyes in the Back of Your Head),
BioCyber(58), 1988, pp. 261-273. BibRef 8800
Earlier: CVWS87(145-150). If you have more information than is physically possible then you can solve some problems. BibRef

Gharavi, H., and Mills, M.,
Block Matching Motion Estimation Algorithms: New Results,
CirSys(37), No. 5, May 1990, pp. 649-651.
IEEE Top Reference. BibRef 9005

Magarey, J.[Julian], Kingsbury, N.G.[Nick G.],
Motion Estimation Using A Complex-Valued Wavelet Transform,
TSP(46), No. 4, April 1998, pp. 1069-1084. 9804
BibRef
Earlier:
An Improved Motion Estimation Algorithm Using Complex Wavelets,
ICIP96(I: 969-972).
IEEE DOI BibRef

Magarey, J.[Julian], Kokaram, A.[Anil], Kingsbury, N.[Nick],
Robust motion estimation using chrominance information in colour image sequences,
CIAP97(I: 486-).
Springer DOI 9709
BibRef
And:
Optimal Schemes for Motion Estimation on Colour Image Sequences,
ICIP97(II: 187-190).
IEEE DOI 9710
BibRef

Young, R.W., Kingsbury, N.G.,
Frequency Domain Motion Estimation Using a Complex Lapped Transform,
IP(2), No. 1, January 1993, pp. 2-17.
IEEE DOI BibRef 9301

Efstratiadis, S.N., Katsaggelos, A.K.,
An Adaptive Regularized Recursive Displacement Estimation Algorithm,
IP(2), No. 3, July 1993, pp. 341-352.
IEEE DOI BibRef 9307

Zhang, J., Hanauer, G.G.,
The Application of Mean-Field Theory to Image Motion Estimation,
IP(4), No. 1, January 1995, pp. 19-33.
IEEE DOI BibRef 9501

Li, W., Salari, E.,
Successive Elimination Algorithm for Motion Estimation,
IP(4), No. 1, January 1995, pp. 105-107.
IEEE DOI BibRef 9501

Jong, C.M.[Chiou-Muh], Salari, E.[Ezzatollah],
Analysis of Image Deformation under Orthographic Projection and Flow Parameter Estimation,
PR(22), No. 3, 1989, pp. 309-315.
Elsevier DOI BibRef 8900

Nomura, A., Miike, H., Koga, K.,
Field Theory Approach for Determining Optical Flow,
PRL(12), 1991, pp. 183-190. BibRef 9100

Nomura, A., Miike, H., Koga, K.,
Determining Motion Fields Under Nonuniform Illumination,
PRL(16), No. 3, March 1995, pp. 285-296. BibRef 9503

Nomura, A.[Atsushi],
Spatio-temporal optimization method for determining motion vector fields under non-stationary illumination,
IVC(18), No. 12, September 2000, pp. 939-950.
Elsevier DOI 0008
BibRef
Earlier:
Integral based approach for determining motion vector fields,
CIAP97(I: 462-469).
Springer DOI 9709
BibRef

Cropper, S.J., Derrington, A.M.,
Detection and Motion Detection in Chromatic and Luminance Beats,
JOSA-A(13), No. 3, March 1996, pp. 401-407. BibRef 9603

Srinivasan, S.[Sridhar], Chellappa, R.[Rama],
Noise-resilient estimation of optical flow by use of overlapped basis functions,
JOSA-A(16), No. 3, March 1999, pp. 493-507. BibRef 9903
Earlier:
Optical Flow Using Overlapped Basis Functions for Solving Global Motion Problems,
ECCV98(II: 288).
Springer DOI BibRef
Earlier:
Robust Modeling and Estimation of Optical Flow with Overlapped Basis Functions,
UMDTR3721, December 1996.
WWW Link.
WWW Link. BibRef

Srinivasan, S., Chellappa, R.,
An Integrated Approach to Image Stabilization, Mosaicking and Super-Resolution,
DARPA97(247-254). BibRef 9700
And:
Image Stabilization and Mosaicking Using the Overlapped Basis Optical Flow Field,
ICIP97(III: 356-359).
IEEE DOI BibRef

Liu, H.C.[Hong-Che], Hong, T.H.[Tsai-Hong], Herman, M., Chellappa, R.,
A Generalized Motion Model for Estimating Optical Flow Using 3-D Hermite Polynomials,
ICPR94(A:361-366).
IEEE DOI BibRef 9400

Bonkovic, M.[Mirjana], Stipanicev, D.[Darko], Stula, M.[Maja],
2D Motion Analysis by Fuzzy Pattern Comparator,
CAIP99(472-479).
Springer DOI 9909
BibRef

Rekleitis, I.M.,
Optical flow recognition from the power spectrum of a single blurred image,
ICIP96(III: 791-794).
IEEE DOI 9610
BibRef

Davis, C.Q., Karu, Z.Z., Freeman, D.M.,
Equivalence of Subpixel Motion Estimators Based on Optical Flow and Block Matching,
SCV95(7-12)
IEEE DOI M.I.T. Rigid motions. Block matching: minimize the difference between shifted versions of the images. BibRef 9500