17.2.8 Optical Flow -- Hierarchical, Multi-Grid, Multi-Scale Approaches

Chapter Contents (Back)
Multiple Resolutions. Optical Flow, Multigrid.

Terzopoulos, D.[Demetri],
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PAMI(8), No. 2, March 1986, pp. 129-139. Relaxation. BibRef 8603
Earlier:
Concurrent Multiple Relaxation,
DARPA85(156-162). BibRef
Earlier:
Multigrid Relaxation Methods and the Analysis of Lightness, Shading and Flow,
MIT AI Memo-803, October 1984.
WWW Link. See also Computation of Visible-Surface Representations, The. Use the constraints at other levels, simple description is that these are just included in the weighted sum. There are no references to the classical relaxation work, even if it does not do anything at the multiple levels. BibRef

Enkelmann, W.[Wilfried],
Investigation of Multigrid Algorithms for the Estimation of Optical Flow Fields in Image Sequences,
CVGIP(43), No. 2, August 1988, pp. 150-177.
WWW Link. BibRef 8808
Earlier: Motion86(81-87). Multiple resolution (cue the coarse directions to guide the smoothing at the finer resolutions) is used to improve the results. Some of the best real examples of optical flow results. BibRef

Hutchinson, J.[James], Koch, C., Luo, J., and Mead, C.,
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And: MIT AI Memo-1265, October 1990, Scale Space. BibRef

Hwang, S.H.[Shin Hwan], Lee, S.U.[Sang Uk],
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WWW Link. BibRef 9306

Luettgen, M.R., Karl, W.C., Willsky, A.S.,
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Anandan, P.,
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Ph.D.Thesis (CS), 1987, BibRef 8700 COINSU Mass. BibRef

Anandan, P.,
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IJCV(2), No. 3, January? 1989, pp. 283-310.
Springer DOI BibRef 8901
Earlier:
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ICCV87(219-230). BibRef
And: DARPA87(719-732). Scale Space. A continuation of the prior work. Use a hierarchical scale space approach to the process. BibRef

Meghabghab, G., Kandel, A.,
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SMC(22), 1992, pp. 813-820. BibRef 9200

Konrad, J., and Dubois, E.,
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PAMI(14), No. 9, September 1992, pp. 910-927.
IEEE DOI BibRef 9209
Earlier:
Multigrid Bayesian Estimation of Image Motion Fields Using Stochastic Relaxation,
ICCV88(354-362).
IEEE DOI Relaxation. Generate dense vector fields. Multiple resolution approach. BibRef

Konrad, J.[Janusz], Dubois, E.[Eric],
A Comparison of Stochastic and Deterministic Solution Methods in Bayesian Estimation of 2-D Motion,
IVC(9), No. 4, August 1991, pp. 215-228.
WWW Link. BibRef 9108
Earlier: ECCV90(149-160).
Springer DOI Optical flow using MAP. BibRef

Mitiche, A., Wang, Y.F., and Aggarwal, J.K.,
Experiments in Computing Optical Flow with the Gradient-Based, Multiconstraint Method,
PR(20), No. 2, 1987, pp. 173-179.
WWW Link. This paper presents results on camera acquired images using feature operators to derive image functions for the data. The method is a multiconstraint approach. BibRef 8700

Xie, K., van Eycken, L., Oosterlinck, A.,
Hierarchical Motion Estimation with Smoothness Constraints and Postprocessing,
OptEng(35), No. 1, January 1996, pp. 145-155. BibRef 9601

Song, S., Liao, M., Qin, J.,
Multiresolution Image Motion Detection and Displacement Estimation,
MVA(3), 1990, pp. 17-20. See also Multiresolution Image Dynamic Thresholding. BibRef 9000

Yang, Q., Ma, S.D.,
Intrinsic Multiscale Representation Using Optical Flow in the Scale-Space,
IP(8), No. 3, March 1999, pp. 444-447.
IEEE DOI BibRef 9903

Close, R.[Robert], Tamura, S.[Shinichi], Naito, H.[Hiroaki],
Estimation of motion from sequential images using integral constraints,
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WWW Link. 0401
The equations which transform arbitrary integrals between sequential images are expressed as a function of the displacement field and intensity changes. The apparent displacement field is then computed by iterative projections onto the solution space of each linearized transformation equation. BibRef

Mahzoun, M.R.[Mohammad Reza], Kim, J.W.[Jin-Woo], Sawazaki, S.[Satoru], Okazaki, K.[Kozo], Tamura, S.[Shinichi],
A scaled multigrid optical flow algorithm based on the least RMS error between real and estimated second images,
PR(32), No. 4, April 1999, pp. 657-670.
WWW Link. Continuous and uniform motion. BibRef 9904

Yacoob, Y.[Yaser], Davis, L.S.[Larry S.],
Temporal Multi-Scale Models for Flow and Acceleration,
IJCV(32), No. 2, September 1999, pp. 147-163.
DOI Link BibRef 9909
Earlier: CVPR97(921-927).
IEEE DOI 9704
Image acceleration. See also Learned Models for Estimation of Rigid and Articulated Human Motion from Stationary or Moving Camera. BibRef

Yacoob, Y.[Yaser], Davis, L.S.[Larry S.],
Temporal Multi-Scale Models for Image Motion Estimation,
DARPA97(135-142). BibRef 9700

Yacoob, Y.[Yaser], and Davis, L.S.[Larry S.],
Estimating Image Motion Using Temporal Multi-Scale Optical Flow and Acceleration,
MBR97(Chapter 2) Maryland. BibRef 9700

Kim, J.D.[Jong Dae], Mitra, S.K.[Sanjit K.],
A local relaxation method for optical flow estimation,
SP:IC(11), No. 1, November 1997, pp. 21-38.
WWW Link. Improve convergence of local relaxation method. BibRef 9711

Cohen, I.[Isaac], Herlin, I.[Isabelle],
Non Uniform Multiresolution Method for Optical Flow and Phase Portrait Models: Environmental Applications,
IJCV(33), No. 1, September 1999, pp. 29-49.
DOI Link BibRef 9909
Earlier:
Optical Flow and Phase Portrait Methods for Environmental Satellite Image Sequences,
ECCV96(II:141-150).
Springer DOI BibRef
And: INRIARR 2819, March 1996.
HTML Version. BibRef
And:
A Motion Computation and Interpretation Framework for Oceanographic Satellite Images,
SCV95(13-18).
IEEE DOI
HTML Version. INRIA. Regularization. Adaptive mesh for computations. BibRef

Herlin, I.[Isabelle], Cohen, I.[Isaac], and Bouzidi, S.[Sonia],
Detection and Tracking of Vortices on Oceanographic Images,
SCIA95(xx). BibRef 9500

Cohen, I.[Isaac],
Nonlinear Variational Method for Optical Flow Computation,
SCIA93(523-530). BibRef 9300

Kim, J.D.[Jong-Dae], Kim, J.W.[Jong-Won],
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SP(81), No. 10, October 2001, pp. 2249-2252.
WWW Link. 0110
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Wu, Y.T.[Yu-Te], Kanade, T.[Takeo], Li, C.C.[Ching-Chung], Cohn, J.F.[Jeffrey F.],
Image Registration Using Wavelet-Based Motion Model,
IJCV(38), No. 2, July 2000, pp. 129-152.
DOI Link 0008
BibRef

Wu, Y.T.[Yu-Te], Kanade, T.[Takeo], Cohn, J.F.[Jeffrey F.], and Li, C.C.[Ching-Chung],
Optical Flow Estimation Using Wavelet Motion Model,
ICCV98(992-998).
IEEE DOI
PDF File. BibRef 9800

Lefébure, M.[Martin], Cohen, L.D.[Laurent D.],
Image Registration, Optical Flow and Local Rigidity,
JMIV(14), No. 2, March 2001, pp. 131-147.
DOI Link
PDF File. 0106
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And: ScaleSpace01(xx-yy).
PS File. 0106
BibRef
Earlier:
A Multiresolution Algorithm for Signal and Image Registration,
ICIP97(III: 252-255).
IEEE DOI BibRef

Lefébure, M.[Martin], Cohen, L.D.[Laurent D.],
Optical Flow and Image Registration: A New Local Rigidity Approach for Global Minimization,
EMMCVPR01(592-607).
Springer DOI 0205
BibRef

Irani, M.[Michal],
Multi-Frame Correspondence Estimation Using Subspace Constraints,
IJCV(48), No. 3, July-August 2002, pp. 173-194.
DOI Link 0207
BibRef
Earlier:
Multi-Frame Optical Flow Estimation using Subspace Constraints,
ICCV99(626-633).
IEEE DOI Multi-frame constraints used to constrain the 2D correspondence. Variety of imaging models. See also Multiview Constraints on Homographies. BibRef

Garbe, C.S.[Christoph S.], Spies, H.[Hagen], Jähne, B.[Bernd],
Estimation of Surface Flow and Net Heat Flux from Infrared Image Sequences,
JMIV(19), No. 3, November 2003, pp. 159-174.
DOI Link 0310
BibRef
Earlier:
Mixed OLS-TLS for the Estimation of Dynamic Processes with a Linear Source Term,
DAGM02(463 ff.).
Springer DOI 0303
BibRef

Kybic, J.[Jan], Nieuwenhuis, C.[Claudia],
Bootstrap optical flow confidence and uncertainty measure,
CVIU(115), No. 10, October 2011, pp. 1449-1462.
Elsevier DOI 1108
Optical flow; Bootstrap; Confidence measure; Motion estimation; Uncertainty estimation BibRef

Nieuwenhuis, C.[Claudia], Kondermann, D.[Daniel], Garbe, C.S.[Christoph S.],
Complex Motion Models for Simple Optical Flow Estimation,
DAGM10(141-150).
Springer DOI 1009
BibRef

Kondermann, C.[Claudia], Mester, R.[Rudolf], Garbe, C.S.[Christoph S.],
A Statistical Confidence Measure for Optical Flows,
ECCV08(III: 290-301).
Springer DOI 0810
BibRef

Kondermann, C.[Claudia], Kondermann, D.[Daniel], Jähne, B.[Bernd], Garbe, C.S.[Christoph S.],
An Adaptive Confidence Measure for Optical Flows Based on Linear Subspace Projections,
DAGM07(132-141).
Springer DOI 0709
BibRef

Andres, B.[Björn], Hamprecht, F.A.[Fred A.], Garbe, C.S.[Christoph S.],
Selection of Local Optical Flow Models by Means of Residual Analysis,
DAGM07(72-81).
Springer DOI 0709
BibRef

Berkels, B.[Benjamin], Kondermann, C.[Claudia], Garbe, C.S.[Christoph S.], Rumpf, M.[Martin],
Reconstructing Optical Flow Fields by Motion Inpainting,
EMMCVPR09(388-400).
Springer DOI 0908
BibRef

Kondermann, C.[Claudia], Kondermann, D.[Daniel], Garbe, C.[Christoph],
Postprocessing of Optical Flows Via Surface Measures and Motion Inpainting,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Andres, B.[Bjorn], Kondermann, C.[Claudia], Kondermann, D.[Daniel], Kothe, U.[Ullrich], Hamprecht, F.A.[Fred A.], Garbe, C.S.[Christoph S.],
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CVPR08(1-6).
IEEE DOI 0806
BibRef

Bruhn, A.[Andrés], Weickert, J.[Joachim], Schnörr, C.[Christoph],
Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods,
IJCV(61), No. 3, February-March 2005, pp. 211-231.
DOI Link 0412
BibRef
Earlier:
Combining the Advantages of Local and Global Optic Flow Methods,
DAGM02(454 ff.).
Springer DOI 0303
Code: See also Implementation of Combined Local-Global Optical Flow, An. BibRef

Maurer, D.[Daniel], Stoll, M.[Michael], Volz, S.[Sebastian], Gairing, P.[Patrick], Bruhn, A.[Andrés],
A Comparison of Isotropic and Anisotropic Second Order Regularisers for Optical Flow,
SSVM17(537-549).
Springer DOI 1706
BibRef

Maurer, D.[Daniel], Stoll, M.[Michael], Bruhn, A.[Andrés],
Order-Adaptive Regularisation for Variational Optical Flow: Global, Local and in Between,
SSVM17(550-562).
Springer DOI 1706
BibRef

Stoll, M.[Michael], Volz, S.[Sebastian], Maurer, D.[Daniel], Bruhn, A.[Andrés],
A Time-Efficient Optimisation Framework for Parameters of Optical Flow Methods,
SCIA17(I: 41-53).
Springer DOI 1706
BibRef

Demetz, O.[Oliver], Stoll, M.[Michael], Volz, S.[Sebastian], Weickert, J.[Joachim], Bruhn, A.[Andrés],
Learning Brightness Transfer Functions for the Joint Recovery of Illumination Changes and Optical Flow,
ECCV14(I: 455-471).
Springer DOI 1408
BibRef

Gong, H.F.[Hai-Feng], Pan, C.H.[Chun-Hong], Yang, Q.[Qing], Lu, H.Q.[Han-Qing], Ma, S.D.[Song-De],
Generalized optical flow in the scale space,
CVIU(105), No. 1, January 2007, pp. 86-92.
WWW Link. 0701
Scale space; Optical flow; Segmentation; Filtering BibRef

Chamorro-Martínez, J.[Jesús], Fernndez-Valdivia, J.,
A New Approach to Motion Pattern Recognition and Its Application to Optical Flow Estimation,
SMC-C(37), No. 1, January 2007, pp. 39-51.
IEEE DOI 0701
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Legrand, L., Dipanda, A., Marzani, F., Kardouchi, M.[Mustapha],
Using Fourier local magnitude in adaptive smoothness constraints in motion estimation,
PRL(28), No. 9, 1 July 2007, pp. 1019-1028.
WWW Link. 0704
Motion estimation; Optical flow; Motion discontinuities; Fourier transform; Markov random fields BibRef

Ha, S.V.U., Jeon, J.W.,
Readjusting Unstable Regions to Improve the Quality of High Accuracy Optical Flow,
CirSysVideo(20), No. 4, April 2010, pp. 540-547.
IEEE DOI 1003
BibRef

Ramírez-Manzanares, A.[Alonso], Rivera, M.[Mariano], Kornprobst, P.[Pierre], Lauze, F.[François],
Variational Multi-Valued Velocity Field Estimation for Transparent Sequences,
JMIV(40), No. 3, July 2011, pp. 285-304.
WWW Link. 1103
BibRef
Earlier:
A Variational Approach for Multi-valued Velocity Field Estimation in Transparent Sequences,
SSVM07(227-238).
Springer DOI 0705
BibRef

Rakêt, L.L.[Lars Lau], Roholm, L.[Lars], Nielsen, M.[Mads], Lauze, F.[François],
TV-L1 Optical Flow for Vector Valued Images,
EMMCVPR11(329-343).
Springer DOI 1107
BibRef

Reyes, A.[Alejandro], Alba, A.[Alfonso], Arce-Santana, E.[Edgar],
Efficiency Analysis of POC-Derived Bases for Combinatorial Motion Estimation,
PSIVT13(124-135).
Springer DOI 1402
BibRef

Alba, A.[Alfonso], Arce-Santana, E.[Edgar], Rivera, M.[Mariano],
Optical Flow Estimation with Prior Models Obtained from Phase Correlation,
ISVC10(I: 417-426).
Springer DOI 1011
BibRef

Lauze, F., Kornprobst, P., Memin, E.,
A Course to Fine Multiscale Approach for Linear Least Squares Optical Flow Estimation,
BMVC04(xx-yy).
HTML Version. 0508
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van Dorst, P.A.G.[Pieter A.G.], Janssen, B.J.[Bart J.], Florack, L.M.J.[Luc M.J.], ter Haar Romeny, B.M.[Bart M.],
Optic flow based on multi-scale anchor point movement and discontinuity-preserving regularization,
PR(44), No. 9, September 2011, pp. 2057-2062.
Elsevier DOI 1106
BibRef
Earlier:
Optic Flow Using Multi-scale Anchor Points,
CAIP09(1104-1112).
Springer DOI 0909
Image processing; Optic flow; Multi-scale; Scale-space; Toppoints; Discontinuity-preserving regularization BibRef

Florack, L.M.J.[Luc M.J.], Janssen, B.J.[Bart J.], Kanters, F.M.W.[Frans M.W.], Duits, R.[Remco],
Towards a New Paradigm for Motion Extraction,
ICIAR06(I: 743-754).
Springer DOI 0610
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Janssen, B.J., Florack, L.M.J., Duits, R., ter Haar Romeny, B.M.,
Optic Flow from Multi-scale Dynamic Anchor Point Attributes,
ICIAR06(I: 767-779).
Springer DOI 0610
BibRef

Niu, Y., Dick, A., Brooks, M.J.,
Locally Oriented Optical Flow Computation,
IP(21), No. 4, April 2012, pp. 1573-1586.
IEEE DOI 1204
BibRef
Earlier:
A new combination of local and global constraints for optical flow computation,
IVCNZ08(1-6).
IEEE DOI 0811
BibRef

Niu, Y.[Yan], Dick, A., Brooks, M.J.,
Compass Rose: A Rotational Robust Signature for Optical Flow Computation,
CirSysVideo(24), No. 1, January 2014, pp. 63-73.
IEEE DOI 1402
image sequences BibRef

Aodha, O.M.[Oisin Mac], Humayun, A.[Ahmad], Pollefeys, M.[Marc], Brostow, G.J.[Gabriel J.],
Learning a Confidence Measure for Optical Flow,
PAMI(35), No. 5, May 2013, pp. 1107-1120.
IEEE DOI 1304
Multi-technique, not multi-level. per-pixel confidence for optical flow vectors for different algorithms. Thus can choose different algorithms for different areas. BibRef

Héas, P.[Patrick], Herzet, C.[Cédric], Mémin, E.[Etienne], Heitz, D.[Dominique], Mininni, P.D.[Pablo D.],
Bayesian Estimation of Turbulent Motion,
PAMI(35), No. 6, June 2013, pp. 1343-1356.
IEEE DOI 1305
BibRef
Earlier: A1, A3, A4, A5, Only:
Bayesian selection of scaling laws for motion modeling in images,
ICCV09(971-978).
IEEE DOI 0909
multiscale regularization for optical flow. Turbulent motion. BibRef

Sánchez Pérez, J.[Javier], Meinhardt-Llopis, E.[Enric], Facciolo, G.[Gabriele],
TV-L1 Optical Flow Estimation,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link 1309
Code, Optical Flow. See also Algorithm for Total Variation Minimization and Applications, An. See also Nonlinear total variation based noise removal algorithms. BibRef

Pérez, J.S.[Javier Sánchez], López, N.M.[Nelson Monzón], Salgado de la Nuez, A.[Agustín],
Robust Optical Flow Estimation,
IPOL(2013), No. 2013, pp. 242-261.
DOI Link 1311
Code, Optical Flow. See also Reliable Estimation of Dense Optical Flow Fields with Large Displacements. BibRef

Garcia-Dopico, A.[Antonio], Pedraza, J.L.[Jose Luis], Nieto, M.[Manuel], Perez, A.[Antonio], Rodriguez, S.[Santiago], Navas, J.[Juan],
Parallelization of the optical flow computation in sequences from moving cameras,
JIVP(2014), No. 1, 2014, pp. 18.
DOI Link 1404
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Garcia-Dopico, A.[Antonio], Pedraza, J.L.[Jose Luis], Nieto, M.[Manuel], Perez, A.[Antonio], Rodriguez, S.[Santiago], Osendi, L.[Luis],
Locating moving objects in car-driving sequences,
JIVP(2014), No. 1, 2014, pp. 24.
DOI Link 1405
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Zille, P., Corpetti, T., Shao, L.[Liang], Chen, X.[Xu],
Observation Model Based on Scale Interactions for Optical Flow Estimation,
IP(23), No. 8, August 2014, pp. 3281-3293.
IEEE DOI 1408
image resolution BibRef

Jara-Wilde, J.[Jorge], Cerda, M.[Mauricio], Delpiano, J.[José], Härtel, S.[Steffen],
An Implementation of Combined Local-Global Optical Flow,
IPOL(5), 2015, pp. 139-158.
DOI Link 1508
Code, Optical Flow. See also Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods. See also Variational Optical Flow Computation in Real Time. BibRef

Pereira, D.R.[Danillo Roberto], Delpiano, J.[Jose], Papa, J.P.[João Paulo],
On the optical flow model selection through metaheuristics,
JIVP(2015), No. 1, 2015, pp. 11.
DOI Link 1508
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Gibson, J.[Joel], Marques, O.[Oge],
Sparsity in optical flow and trajectories,
SIViP(10), No. 3, March 2016, pp. 487-494.
WWW Link. 1602
BibRef
Earlier:
Sparse Regularization of TV-L1 Optical Flow,
ICISP14(460-467).
Springer DOI 1406
BibRef

Zhang, C., Chen, Z., Wang, M., Li, M., Jiang, S.,
Robust Non-Local TV-L^1 Optical Flow Estimation With Occlusion Detection,
IP(26), No. 8, August 2017, pp. 4055-4067.
IEEE DOI 1707
image filtering, image motion analysis, image sequences, iterative methods, median filters, Charbonnier function, Middlebury database test sequence, brightness constancy, coarse-to-fine computing strategy, displacement motion, robust nonlocal TV-L1 optical flow estimation, triangulation, BibRef


Ranjan, A., Black, M.J.[Michael J.],
Optical Flow Estimation Using a Spatial Pyramid Network,
CVPR17(2720-2729)
IEEE DOI 1711
Adaptive optics, Biomedical optical imaging, Computational modeling, Estimation, Neural networks, Optical computing, Optical, imaging BibRef

Choi, J.W.[Jong-Won], Kim, H.W.[Hyeong-Woo], Oh, T.H.[Tae-Hyun], Kweon, I.S.[In So],
Balanced optical flow refinement by bidirectional constraint,
ICIP14(5477-5481)
IEEE DOI 1502
Adaptive optics BibRef

Stoll, M.[Michael], Volz, S.[Sebastian], Bruhn, A.[Andres],
Joint trilateral filtering for multiframe optical flow,
ICIP13(3845-3849)
IEEE DOI 1402
Cross Bilateral Upsampling BibRef

Singh, A.[Abhishek], Ahuja, N.[Narendra],
Exploiting ramp structures for improving optical flow estimation,
ICPR12(2504-2507).
WWW Link. 1302
BibRef

Tepper, M.[Mariano], Sapiro, G.[Guillermo],
Decoupled coarse-to-fine matching and nonlinear regularization for efficient motion estimation,
ICIP12(1517-1520).
IEEE DOI 1302
BibRef

Mohamed, M.A.[Mahmoud A.], Mertsching, B.[Baerbel],
TV-L1 Optical Flow Estimation with Image Details Recovering Based on Modified Census Transform,
ISVC12(I: 482-491).
Springer DOI 1209
BibRef

Hoeltgen, L.[Laurent], Setzer, S.[Simon], Breuß, M.[Michael],
Intermediate Flow Field Filtering in Energy Based Optic Flow Computations,
EMMCVPR11(315-328).
Springer DOI 1107
BibRef

Wartak, S.[Szymon], Bors, A.G.[Adrian G.],
Optical Flow Estimation Using Diffusion Distances,
ICPR10(189-192).
IEEE DOI 1008
BibRef

Lei, C.[Cheng], Yang, Y.H.[Yee-Hong],
Optical flow estimation on coarse-to-fine region-trees using discrete optimization,
ICCV09(1562-1569).
IEEE DOI 0909
BibRef

Heindlmaier, M.[Michael], Yu, L.[Lang], Diepold, K.[Klaus],
The impact of nonlinear filtering and confidence information on optical flow estimation in a Lucas and Kanade framework,
ICIP09(1593-1596).
IEEE DOI 0911
BibRef

Ring, D., Pitie, F.,
Feature-Assisted Sparse to Dense Motion Estimation Using Geodesic Distances,
IMVIP09(7-12).
IEEE DOI 0909
BibRef

Chen, D.[Daniel], Denman, S.[Simon], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Accurate Silhouettes for Surveillance: Improved Motion Segmentation Using Graph Cuts,
DICTA10(369-374).
IEEE DOI 1012
BibRef

Denman, S.[Simon], Fookes, C.[Clinton], Sridharan, S.[Sridha],
Improved Simultaneous Computation of Motion Detection and Optical Flow for Object Tracking,
DICTA09(175-182).
IEEE DOI 0912
See also Multi-Modal Object Tracking using Dynamic Performance Metrics. BibRef

Wilson, R.G.[Roland G.], Bowen, A.[Adam], Mullins, A.[Andrew], Rajpoot, N.[Nasir],
Estimation of a 3D motion field from a multi-camera array using a multiresolution Gaussian mixture model,
M2SFA208(xx-yy). 0810
BibRef

Wilson, R., Calway, A.D.,
Multiresolution Gaussian Mixture Models for Visual Motion Estimation,
ICIP01(II: 921-924).
IEEE DOI 0108
BibRef

Wilson, R.[Ronald],
MGMM: Multiresolution Gaussian Mixture Models for Computer Vision,
ICPR00(Vol I: 212-215).
IEEE DOI 0009
BibRef

Ho, H.T.[Huy Tho], Goecke, R.[Roland],
Optical flow estimation using Fourier Mellin Transform,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Koppel, D.[Dan], Tsai, C.M.[Chang-Ming], Wang, Y.F.[Yuan-Fang],
Regularizing optical-flow computation using tensor theory and complex analysis,
Tensor08(1-6).
IEEE DOI 0806
BibRef

Li, J.[Jian], Benton, C.P.[Christopher P.], Nikolov, S.G.[Stavri G.], Scott-Samuel, N.E.[Nicholas E.],
Adaptive Multiscale Optical Flow Estimation,
ICIP07(II: 509-512).
IEEE DOI 0709
BibRef

Alvino, C., Tannenbaum, A., Yezzi, A.J., Curry, C.,
Multigrid Computation of Rotationally Invariant Non-Linear Optical Flow,
ICIP05(III: 1296-1299).
IEEE DOI 0512
BibRef

Yang, L.X.[Li-Xin], Sahli, H.,
A Nonlinear Multigrid Diffusion Model for Efficient Dense Optical Flow estimation,
ICIP05(I: 149-152).
IEEE DOI 0512
BibRef

Li, M., Kambhamettu, C., Stone, M.,
A General Framework for 2D Multiframe and 3D Surface-to-surface Motion Estimation,
BMVC04(xx-yy).
HTML Version. 0508
BibRef

Teng, C.H.[Chin-Hung], Lai, S.H.[Shang-Hong], Chen, Y.S.[Yung-Sheng], Hsu, W.H.[Wen-Hsing],
An accurate and adaptive optical flow estimation algorithm,
ICIP04(III: 1839-1842).
IEEE DOI 0505
BibRef

Adachi, E., Horiguchi, S.,
Multi-resolutional optical flow estimation with local optimization,
ICIP02(II: 257-260).
IEEE DOI 0210
BibRef

Wang, H.Y.[Hai-Yan], Ma, K.K.[Kai-Kuang],
Automatic video object segmentation via 3D structure tensor,
ICIP03(I: 153-156).
IEEE DOI 0312
BibRef
Earlier:
Accurate optical flow estimation using adaptive scale-space and 3d structure tensor,
ICIP02(II: 301-304).
IEEE DOI 0210
BibRef

Steenstrup Pedersen, K., Nielsen, M.,
Computing optic flow by scale-space integration of normal flow,
ScaleSpace01(xx-yy). 0106
BibRef

Maas, R.[Robert], ter Haar Romeny, B.M.[Bart M.], Viergever, M.A.[Max A.],
A Multiscale Taylor Series Approaches to Optic Flow and Stereo: A Generalization of Optic Flow Under the Aperture,
ScaleSpace99(519-524). BibRef 9900

George, M., Tjahjadi, T.,
Multiresolution Optical Flow Estimation using Adaptive Shifting,
ICIP99(III:717-721).
IEEE Abstract. BibRef 9900

Mendelsohn, J.[Jeffrey], Simoncelli, E.[Eero], Bajcsy, R.[Ruzena],
Discrete-time rigidity-constrained optical flow,
CAIP97(255-262).
Springer DOI 9709

HTML Version. And full paper:
PS File. Structure from optic flow. BibRef

Johannesson, M., and Gokstorp, M.,
Video-rate Pyramid Optical Flow Computations on the Linear SIMD Array IVP,
CAMP95(xx). BibRef 9500

Gokstorp, M., Danielsson, P.E.,
Velocity tuned generalized Sobel operators for multiresolution computation of optical flow,
ICIP94(II: 765-769).
IEEE DOI 9411
BibRef

Colombo, C., del Bimbo, A., Santini, S.,
A Multilayer Massively Parallel Architecture for Optical Flow Computation,
ICPR92(IV:209-213).
IEEE DOI BibRef 9200

Bernard, C.,
Discrete Wavelet Analysis: A New Framework for Fast Optic Flow Computation,
ECCV98(II: 354).
Springer DOI BibRef 9800

Ríos, H.[Homero],
Computing image flow using a coarse-to-fine strategy for spatiotemporal filters,
CAIP93(355-362).
Springer DOI 9309
BibRef

Kories, R., Rehfeld, N., Zimmermann, G.,
Towards Autonomous Convoy Driving: Recognizing the Starting Vehicle in Front,
ICPR88(I: 531-535).
IEEE DOI BibRef 8800

Kories, R., and Zimmermann, G.,
A Versatile Method for the Estimation of Displacement Vector Fields from Image Sequences,
Motion86(101-106). See also Investigation of Multigrid Algorithms for the Estimation of Optical Flow Fields in Image Sequences. BibRef 8600

Kories, R., Hecker, G., Zimmermann, G.,
On the Precision of a Feature Based Displacement Measurement,
ICPR86(1193-1196). BibRef 8600

Zimmermann, G., Kories, R.,
Image Sequence Processing as an Aid for Three-Dimensional Display,
ICPR86(821-824). BibRef 8600

Kories, R., Zimmermann, G.,
Motion Detection in Image Sequences: An Evaluation of Feature Detectors,
ICPR84(778-780). BibRef 8400
And:
A Class of Stable Feature Extractors for Time-Varying Imagery,
ICPR84(919). BibRef

Korn, A.F.[Axel F.], Kories, R.,
Motion Analysis in Natural Scenes Picked up by a Moving Optical Sensor,
ICPR80(1251-1254). BibRef 8000

Kories, R.,
Determination of Displacement Vector Fields for General Camera Motions,
PRIP81(115-117). BibRef 8100

Bergeron, C., and Dubois, E.,
Parametric Block Estimation of Motion and Application to Temporal Interpolation of Video Sequences,
ICPR90(II: 140-146).
IEEE DOI BibRef 9000

Glazer, F.,
Hierarchial Gradient-Based Motion Detection,
DARPA87(733-748). BibRef 8700
Earlier:
Computing Optic Flow,
IJCAI81(644-647). Gradient-based approaches only work with small motions, but is extended by using a hierarchical approach. This seems in keeping with the UMass approach. BibRef

Bandyopadhyay, A.,
A Multiple Channel Model for Perception of Optical Flow,
CVWS84(78-82). BibRef 8400

Burt, P.J., Yen, C., Xy, X.,
Local Correlation Measures for Motion Analysis: A Comparative Study,
PRIP82(269-274). BibRef 8200

Burt, P.J., Yen, C., Xy, X.,
Multi-Resolution Flow - Through Motion Analysis,
CVPR83(246-252). BibRef 8300

Chapter on Optical Flow Field Computations and Use continues in
Parallel Optic Flow Computation, Efficient Computation .


Last update:Dec 7, 2017 at 17:23:10