17.2.5 Large Displacement Optical Flow

Chapter Contents (Back)
Optical Flow. Large Displacement Flow.

Wu, Q.X.,
A Correlation-Relaxation-Labeling Framework for Computing Optical Flow: Template Matching from a New Perspective,
PAMI(17), No. 9, September 1995, pp. 843-853.
IEEE DOI Template Matching. Clouds. Low contrast, large displacements, and non-rigid motions. BibRef 9509

Gong, M.L.[Ming-Lun], Yang, Y.H.[Yee-Hong],
Estimate Large Motions Using the Reliability-Based Motion Estimation Algorithm,
IJCV(68), No. 3, July 2006, pp. 319-330.
Springer DOI 0606
BibRef
Earlier:
Estimate Large Motions Using Reliability-Based Dynamic Programming,
ICIP04(IV: 2559-2562).
IEEE DOI 0505
See also Real-Time Stereo Matching Using Orthogonal Reliability-Based Dynamic Programming. BibRef

Gong, M.L.[Ming-Lun],
Motion estimation using dynamic programming with selective path search,
ICPR04(IV: 203-206).
IEEE DOI 0409
BibRef

Wills, J.[Josh], Agarwal, S.[Sameer], Belongie, S.J.[Serge J.],
A Feature-based Approach for Dense Segmentation and Estimation of Large Disparity Motion,
IJCV(68), No. 2, June 2006, pp. 125-143.
Springer DOI 0606
BibRef

Wills, J.[Josh], Belongie, S.J.[Serge J.],
A Feature-Based Approach for Determining Dense Long Range Correspondences,
ECCV04(Vol III: 170-182).
Springer DOI 0405
two stage process in which a planar model is used to get an approximation for the segmentation and the gross motion, and then a spline is used to refine the fit. BibRef

Alvarez, L.[Luis], Weickert, J.[Joachim], Sánchez, J.[Javier],
Reliable Estimation of Dense Optical Flow Fields with Large Displacements,
IJCV(39), No. 1, August 2000, pp. 41-56.
DOI Link 0008
BibRef
Earlier:
A Scale-Space Approach to Nonlocal Optical Flow Calculations,
ScaleSpace99(235-246). See also Dense Disparity Map Estimation Respecting Image Discontinuities: A PDE and Scale-Space Based Approach. Code: See also Robust Optical Flow Estimation. BibRef

Alvarez, L.[Luis], Deriche, R.[Rachid], Papadopoulo, T.[Théo], Sánchez, J.[Javier],
Symmetrical Dense Optical Flow Estimation with Occlusions Detection,
IJCV(75), No. 3, December 2007, pp. 371-385.
Springer DOI 0710
BibRef
Earlier: ECCV02(I: 721 ff.).
Springer DOI 0205
BibRef

Fransens, R.[Rik], Strecha, C.[Christoph], Van Gool, L.J.[Luc J.],
Optical flow based super-resolution: A probabilistic approach,
CVIU(106), No. 1, April 2007, pp. 106-115.
Elsevier DOI 0704
BibRef
Earlier:
Robust Estimation in the Presence of Spatially Coherent Outliers,
RANSAC06(102).
IEEE DOI 0609
BibRef
And:
A Mean Field EM-algorithm for Coherent Occlusion Handling in MAP-Estimation Prob,
CVPR06(I: 300-307).
IEEE DOI 0606
BibRef
Earlier:
A Probabilistic Approach to Optical Flow based Super-Resolution,
GenModel04(191).
IEEE DOI 0406
BibRef
Earlier: A2, A1, A3:
A Probabilistic Approach to Large Displacement Optical Flow and Occlusion Detection,
SMVP04(71-82).
Springer DOI 0505
Super-resolution; Optical flow; Visibility computation; EM See also Combined Depth and Outlier Estimation in Multi-View Stereo. BibRef

Brox, T.[Thomas], Malik, J.[Jitendra],
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation,
PAMI(33), No. 3, March 2011, pp. 500-513.
IEEE DOI 1102
Integrate correspondences from descriptor matching into a variational approach to gain the benefits of both. BibRef

Brox, T.[Thomas], Bregler, C.[Christoph], Malik, J.[Jitendra],
Large displacement optical flow,
CVPR09(41-48).
IEEE DOI 0906
BibRef

Bao, L.C.[Lin-Chao], Yang, Q.X.[Qing-Xiong], Jin, H.L.[Hai-Lin],
Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow,
IP(23), No. 12, December 2014, pp. 4996-5006.
IEEE DOI 1402
BibRef
Earlier: CVPR14(3534-3541)
IEEE DOI 1409
edge detection. Bilateral Filter BibRef

Revaud, J.[Jérôme], Weinzaepfel, P.[Philippe], Harchaoui, Z.[Zaid], Schmid, C.[Cordelia],
DeepMatching: Hierarchical Deformable Dense Matching,
IJCV(120), No. 3, December 2016, pp. 300-323.
Springer DOI 1609
BibRef
Earlier: A1, A2, A3, A4:
EpicFlow: Edge-preserving interpolation of correspondences for optical flow,
CVPR15(1164-1172)
IEEE DOI 1510
BibRef
Earlier: A2, A1, A3, A4:
DeepFlow: Large Displacement Optical Flow with Deep Matching,
ICCV13(1385-1392)
IEEE DOI 1403
deep convolutional networks BibRef

Palomares, R.P.[Roberto P.], Meinhardt-Llopis, E.[Enric], Ballester, C.[Coloma], Haro, G.[Gloria],
FALDOI: A New Minimization Strategy for Large Displacement Variational Optical Flow,
JMIV(58), No. 1, May 2017, pp. 27-46.
Springer DOI 1704
BibRef
Earlier: A1, A4, A3, Only:
A Rotation-Invariant Regularization Term for Optical Flow Related Problems,
ACCV14(V: 304-319).
Springer DOI 1504
Code: See also Analysis and Speedup of the FALDOI Method for Optical Flow Estimation, An. BibRef

Ballester, C.[Coloma], Garrido, L.[Lluis], Lazcano, V.[Vanel], Caselles, V.[Vicent],
A TV-L1 Optical Flow Method with Occlusion Detection,
DAGM12(31-40).
Springer DOI 1209
BibRef

Gamonal, F.P.[Ferran P.], Ballester, C.[Coloma], Haro, G.[Gloria], Meinhardt-Llopis, E.[Enric], Palomares, R.P.[Roberto P.],
An Analysis and Speedup of the FALDOI Method for Optical Flow Estimation,
IPOL(9), 2019, pp. 94-123.
DOI Link 1904
Code, Optical Flow. See also FALDOI: A New Minimization Strategy for Large Displacement Variational Optical Flow. BibRef

Fan, J.Z.[Jing-Zhe], Wang, Y.[Yan], Guo, L.[Lei],
Hierarchical coherency sensitive hashing and interpolation with RANSAC for large displacement optical flow,
CVIU(175), 2018, pp. 1-10.
Elsevier DOI 1812
BibRef
Earlier:
A modified variational method for large displacement optical flow,
ICIVC17(128-132)
IEEE DOI 1708
Nearest neighbor field, Sparse to dense interpolation, Optical flow. Estimation, Image color analysis, Image motion analysis, Interpolation, Optical imaging, Robustness, descriptor matching, large displacement optical flow, variational method. BibRef


Zu, Y., Gao, K., Bao, X., Tang, W.,
Saliency guided fast interpolation for large displacement optical flow,
ICPR18(1145-1150)
IEEE DOI 1812
Interpolation, Optical imaging, Computational modeling, Mathematical model, Estimation, Cameras, Integrated optics BibRef

Munda, G.[Gottfried], Shekhovtsov, A.[Alexander], Knöbelreiter, P.[Patrick], Pock, T.[Thomas],
Scalable Full Flow with Learned Binary Descriptors,
GCPR17(321-332).
Springer DOI 1711
Large displacement optical flow. BibRef

Hu, Y., Li, Y., Song, R.,
Robust Interpolation of Correspondences for Large Displacement Optical Flow,
CVPR17(4791-4799)
IEEE DOI 1711
BibRef
Earlier: A1, A3, A2:
Efficient Coarse-to-Fine Patch Match for Large Displacement Optical Flow,
CVPR16(5704-5712)
IEEE DOI 1612
Adaptation models, Adaptive optics, Computational modeling, Estimation, Impedance matching, Interpolation, Robustness BibRef

Bailer, C., Taetz, B., Stricker, D.,
Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation,
ICCV15(4015-4023)
IEEE DOI 1602
Adaptive optics BibRef

Verma, N.K., Gunesh, D.E., Rao, G.S.S.S., Mishra, A.,
High Accuracy Optical Flow Based Future Image Frame Predictor Model,
AIPR15(1-6)
IEEE DOI 1605
edge detection BibRef

Verma, N.K., Mishra, A.,
Large displacement optical flow based image predictor model,
AIPR14(1-7)
IEEE DOI 1504
edge detection BibRef

Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
Sparse Flow: Sparse Matching for Small to Large Displacement Optical Flow,
WACV15(1100-1106)
IEEE DOI 1503
Computer vision BibRef

Le Coat, F.[Francois], Pissaloux, E.E.[Edwige E.],
Modelling the optical-flow with projective-transform approximation for large camera movements,
ICIP14(199-203)
IEEE DOI 1502
Biomedical optical imaging BibRef

Nie, Q.[Qiong], Bouchafa, S.[Samia], Merigot, A.[Alain],
Model-based optical flow for large displacements and homogeneous regions,
ICIP13(3865-3869)
IEEE DOI 1402
3D motion; block matching; c-velocity; egomotion; optical flow BibRef

Chang, H.S.[Haw-Shiuan], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Superpixel-based large displacement optical flow,
ICIP13(3835-3839)
IEEE DOI 1402
large displacement optical flow; mean shift; superpixel BibRef

Chen, Z.Y.[Zhuo-Yuan], Jin, H.L.[Hai-Lin], Lin, Z.[Zhe], Cohen, S.[Scott], Wu, Y.[Ying],
Large Displacement Optical Flow from Nearest Neighbor Fields,
CVPR13(2443-2450)
IEEE DOI 1309
Motion Segmentation; Optical Flow; PatchMatch; Randomized Algorithm BibRef

Ummenhofer, B.[Benjamin],
Large Displacement Optical Flow for Volumetric Image Sequences,
DAGM11(432-437).
Springer DOI 1109
BibRef

Willert, V.[Volker], Eggert, J.[Julian],
A stochastic dynamical system for optical flow estimation,
WDV09(711-718).
IEEE DOI 0910
BibRef

Willert, V., Schmuedderich, J., Eggert, J., Goerick, C., Koerner, E.,
Probabilistic Optical Flow Estimation for Large Pixel Displacements Utilizing Egomotion Flow Compensation,
BMVC08(xx-yy).
PDF File. 0809
BibRef

Willert, V.[Volker], Eggert, J.[Julian], Clever, S.[Sebastian], Körner, E.[Edgar],
Probabilistic Color Optical Flow,
DAGM05(9).
Springer DOI 0509
BibRef

Agarwal, R., Sklansky, J.,
Estimating optical flow for large interframe displacements,
CAIP93(403-411).
Springer DOI 9309
BibRef

Agarwal, R., Sklansky, J.,
Estimating Optical Flow from Clustered Trajectories in Velocity-Time,
ICPR92(I:215-219).
IEEE DOI BibRef 9200

Chapter on Optical Flow Field Computations and Use continues in
Optical Flow for Simple Motions .


Last update:Apr 20, 2019 at 12:32:38