Optical flow modeling and computation: A survey

Computer Vision and Image Understanding

Volumn 134, Issue , 2015, Pages 1-21

  Fortun, Denis ^a   Bouthemy, Patrick ^a   Kervrann, Charles ^a  

^a INRIA   (France)

Author keywords

Feature matching; Motion estimation; Occlusions; Optical flow; Optimization; Parametric models; Regularization

Indexed keywords

COMPUTER VISION; OPTICAL FLOWS; OPTIMIZATION; SURVEYS;

FEATURE MATCHING; FLOW MODEL; MODELING AND OPTIMIZATION; OCCLUSIONS; OPTICAL FLOW ESTIMATION; PARAMETRIC MODELS; REGULARIZATION; RESEARCH DOMAINS;

MOTION ESTIMATION;

EID: 84931039844     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2015.02.008     Document Type: Article

References (287)

1. S. Ali, and M. Shah Human action recognition in videos using kinematic features and multiple instance learning IEEE Trans. Pattern Anal. Mach. Intell. 32 2 2010 288 303
2. L. Alvarez, J. Esclarín, M. Lefebure, S. Javier. A PDE model for computing the optical flow, in: Proc. XVI Congreso de ecuaciones diferenciales y aplicaciones, Las Palmas de Gran Canaria, Spain, September 1999, pp. 1349-1356.
3. F. Amat, E.W. Myers, and P.J. Keller Fast and robust optical flow for time-lapse microscopy using super-voxels Bioinformatics 29 3 2013 373 380
4. T. Amiaz, and N. Kiryati Piecewise-smooth dense optical flow via level sets Int. J. Comput. Vision 68 2 2006 111 124
5. T. Amiaz, E. Lubetzky, and N. Kiryati Coarse to over-fine optical flow estimation Pattern Recogn. 40 9 2007 2496 2503
6. P. Arias, G. Facciolo, V. Caselles, and G. Sapiro A variational framework for exemplar-based image inpainting Int. J. Comput. Vision 93 3 2011 319 347
7. G. Aubert, R. Deriche, and P. Kornprobst Computing optical flow via variational techniques SIAM J. Appl. Math. 60 1 1999 156 182
8. J.-F. Aujol, G. Gilboa, T. Chan, and S. Osher Structure-texture image decomposition - modeling, algorithms, and parameter selection Int. J. Comput. Vision 67 1 2006 111 136
9. S. Ayer, H. Sawhney. Layered representation of motion video using robust maximum-likelihood estimation of mixture models and MDL encoding, in: IEEE Int. Conference on Computer Vision (ICCV), Boston, June 1995, pp. 777-784.
10. A. Ayvaci, M. Raptis, and S. Soatto Sparse occlusion detection with optical flow Int. J. Comput. Vision 97 3 2012 322 338
11. S. Baker, M.J. Black, J. Lewis, S. Roth, D. Scharstein, and R. Szeliski. A database and evaluation methodology for optical flow, in: International Conference on Computer Vision (ICCV), Rio de Janeiro, Brazil, October 2007, pp. 1-8.
12. S. Baker, and I. Matthews Lucas-Kanade 20 years on: a unifying framework Int. J. Comput. Vision 56 3 2004 221 255
13. S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, and R. Szeliski A database and evaluation methodology for optical flow Int. J. Comput. Vision 92 1 2011 1 31
14. C. Ballester, L. Garrido, V. Lazcano, and V. Caselles A TV-L1 optical flow method with occlusion detection Pattern Recogn. 2012 31 40
15. C. Barnes, E. Shechtman, A. Finkelstein, and D.B. Goldman Patchmatch: a randomized correspondence algorithm for structural image editing ACM Trans. on Graphics vol. 28 2009 ACM 24
16. C. Barnes, E. Shechtman, D.B. Goldman, A. Finkelstein, The generalized patchmatch correspondence algorithm, in: European Conference on Computer Vision (ECCV), 2010, pp. 29-43.
17. J. Barron, D. Fleet, and S. Beauchemin Evaluation of optical flow Int. J. Comput. Vision 12 1 1994 43 77
18. T. Basha, Y. Moses, and N. Kiryati Multi-view scene flow estimation: a view centered variational approach Int. J. Comput. Vision 101 1 2013 6 21
19. A. Basset, P. Bouthemy, C. Kervrann, Recovery of motion patterns and dominant paths in videos of crowded scenes, in: International Conference on Image Processing (ICIP), Paris, October 2014.
20. H. Bay, T. Tuytelaars, L. Van Gool, Surf: speeded up robust features, in: European Conference Computer Vision (ECCV), 2006, pp. 404-417.
21. S.S. Beauchemin, and J.L. Barron The computation of optical flow ACM Comput. Surv. (CSUR) 27 3 1995 433 466
22. F. Becker, B. Wieneke, S. Petra, A. Schroder, and C. Schnörr Variational adaptive correlation method for flow estimation IEEE Trans. Image Process. 21 6 2012 3053 3065
23. J.R. Bergen, P. Anandan, K.J. Hanna, and R. Hingorani Hierarchical model-based motion estimation European Conference on Computer Vision (ECCV) 1992 Springer 237 252
24. M. Bertalmio, G. Sapiro, V. Caselles, C. Ballester, Image inpainting, in: Proceedings of the Annual Conference on Computer Graphics and Interactive Techniques, 2000, pp. 417-424.
25. J. Besag On the statistical analysis of dirty pictures J. Roy. Stat. Soc. Ser. B (Methodol.) 1986 59 302
26. J. Bigun, G.H. Granlund, and J. Wiklund Multidimensional orientation estimation with applications to texture analysis and optical flow IEEE Trans. Pattern Anal. Mach. Intell. 13 8 1991 775 790
27. M. Black, and P. Anandan The robust estimation of multiple motions: parametric and piecewise-smooth flow fields Comput. Vis. Image Underst. 63 1 1996 75 104
28. M. Black, and A. Jepson Estimating optical flow in segmented images using variable-order parametric models with local deformations IEEE Trans. Pattern Anal. Mach. Intell. 18 10 1996 972 986
29. M.J. Black, Recursive non-linear estimation of discontinuous flow fields, in: European Conference on Computer Vision (ECCV), 1994, pp. 138-145.
30. M.J. Black, P. Anandan, A framework for the robust estimation of optical flow, in: International Conference on Computer Vision (ICCV), 1993, pp. 231-236.
31. M.J. Black, and Y. Yacoob Recognizing facial expressions in image sequences using local parameterized models of image motion Int. J. Comput. Vision 25 1 1997 23 48
32. M.J. Black, Y. Yacoob, A.D. Jepson, D.J. Fleet, Learning parameterized models of image motion, in: Computer Vision and Pattern Recognition (CVPR), 1997, pp. 561-567.
33. A. Blake, and A. Zisserman Visual Reconstruction 1987 MIT press Cambridge
34. M. Bleyer, C. Rhemann, M. Gelautz, Segmentation-based motion with occlusions using graph-cut optimization, in: DAGM Symposium on Pattern Recognition, Berlin, Germany, September 2006, pp. 465-474.
35. E. Boros, P. Hammer, X. Sun, Network Flows and Minimization of Quadratic Pseudo-boolean Functions, Technical Report, Technical Report RRR 17-1991, RUTCOR, 1991.
36. J.-Y. Bouguet, Pyramidal Implementation of the Lucas-Kanade Feature Tracker, Description of the Algorithm, Technical Report, Intel Microprocessor Research Labs, 1999.
37. P. Bouthemy, and E. François Motion segmentation and qualitative dynamic scene analysis from an image sequence Int. J. Comput. Vision 10 2 1993 157 182
38. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein Distributed optimization and statistical learning via the alternating direction method of multipliers Found. Trends® Mach. Learn. 3 1 2011 1 122
39. Y. Boykov, O. Veksler, R. Zabih, Markov random fields with efficient approximations, in: Computer Vision and Pattern Recognition (CVPR), 1998, pp. 648-655.
40. Y. Boykov, O. Veksler, and R. Zabih Fast approximate energy minimization via graph cuts IEEE Trans. Pattern Anal. Mach. Intell. 23 11 2001 1222 1239
41. G. Bradski Opencv Dr. Dobb's J. Softw. Tools 2000
42. J. Braux-Zin, R. Dupont, A. Bartoli, A general dense image matching framework combining direct and feature-based costs, in: International Conference on Computer Vision (ICCV), 2013.
43. K. Bredies, K. Kunisch, and T. Pock Total generalized variation SIAM J. Imag. Sci. 3 3 2010 492 526
44. L.G. Brown A survey of image registration techniques ACM Comput. Surv. (CSUR) 24 4 1992 325 376
45. T. Brox, A. Bruhn, N. Papenberg, J. Weickert, High accuracy optical flow estimation based on a theory for warping, in: European Conference on Computer Vision (ECCV), Prague, Czech Republic, May 2004, pp. 25-36.
46. T. Brox, A. Bruhn, J. Weickert, Variational motion segmentation with level sets, in: European Conference on Computer Vision (ECCV), 2006, pp. 471-483.
47. T. Brox, and J. Malik Large displacement optical flow: descriptor matching in variational motion estimation IEEE Trans. Pattern Anal. Mach. Intell. 33 3 2011 500 513
48. T. Brox, J. Weickert, B. Burgeth, and P. Mrázek Nonlinear structure tensors Image Vis. Comput. 24 1 2006 41 55
49. A. Bruhn, J. Weickert, and C. Schnörr Lucas/Kanade meets horn/schunck: combining local and global optic flow methods Int. J. Comput. Vision 61 3 2005 211 231
50. A. Bruhn, and W. Weickert A confidence measure for variational optic flow methods Geom. Properties Incomplete Data 2006 283 298
51. A. Bugeau, M. Bertalmío, V. Caselles, and G. Sapiro A comprehensive framework for image inpainting IEEE Trans. Image Process. 19 10 2010 2634 2645
52. D.J. Butler, J. Wulff, G.B. Stanley, and M.J. Black A naturalistic open source movie for optical flow evaluation European Conference on Computer Vision (ECCV) 2012 Springer-Verlag 611 625
53. X. Cai, J.H. Fitschen, M. Nikolova, G. Steidl, M. Storath, Disparity and optical flow partitioning using extended Potts priors, Inform. Inference: J. IMA (2014) (submitted for publication) arXiv:1405.1594.
54. A. Chambolle An algorithm for total variation minimization and applications J. Math. Imag. Vision 20 1-2 2004 89 97
55. A. Chambolle, and T. Pock A first-order primal-dual algorithm for convex problems with applications to imaging J. Math. Imag. Vision 40 1 2011 120 145
56. T.F. Chan, S.H. Kang, and J. Shen Euler's elastica and curvature-based inpainting SIAM J. Appl. Math. 2002 564 592
57. H. Chao, Y. Gu, and M. Napolitano A survey of optical flow techniques for robotics navigation applications J. Intell. Robotic Syst. 73 1-4 2014 361 372
58. P. Charbonnier, L. Blanc-Féraud, G. Aubert, and M. Barlaud Deterministic edge-preserving regularization in computed imaging IEEE Trans. Image Process. 6 2 1997 298 311
59. Z. Chen, H. Jin, Z. Lin, S. Cohen, Y. Wu, Large displacement optical flow from nearest neighbor fields, in: Computer Vision and Pattern Recognition (CVPR), 2013, pp. 2443-2450.
60. T.M. Chin, W.C. Karl, and A.S. Willsky Probabilistic and sequential computation of optical flow using temporal coherence IEEE Trans. Image Process. 3 6 1994 773 788
61. P. Chou, and C. Brown The theory and practice of Bayesian image labeling Int. J. Comput. Vision 4 3 1990 185 210
62. P.G. Ciarlet The Finite Element Method for Elliptic Problems 1978 Elsevier
63. I. Cohen, Nonlinear variational method for optical flow computation, in: Proceedings of the 8th Scandinavian Conference on Image Analysis, 1993, pp. 523-530.
64. T. Cooke, Two applications of graph-cuts to image processing, in: Digital Image Computing: Techniques and Applications (DICTA), 2008, pp. 498-504.
65. T. Corpetti, É. Mémin, and P. Pérez Dense estimation of fluid flows IEEE Trans. Pattern Anal. Mach. Intell. 24 3 2002 365 380
66. D. Cremers, and S. Soatto Motion competition: a variational approach to piecewise parametric motion segmentation Int. J. Comput. Vision 62 3 2005 249 265
67. A. Crétual, and F. Chaumette Visual servoing based on image motion Int. J. Robotics Res. 20 11 2001 857 877
68. A. Criminisi, P. Pérez, and K. Toyama Region filling and object removal by exemplar-based image inpainting IEEE Trans. Image Process. 13 9 2004 1200 1212
69. T. Crivelli, B. Cernuschi-Frias, P. Bouthemy, and J.-F. Yao Motion textures: modeling, classification, and segmentation using mixed-state Markov random fields SIAM J. Imag. Sci. 6 4 2013 2484 2520
70. R. Cutler, M. Turk, View-based interpretation of real-time optical flow for gesture recognition, in: International Conference on Automatic Face and Gesture Recognition, 1998, pp. 416-416.
71. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: Computer Vision and Pattern Recognition (CVPR), vol. 1, 2005, pp. 886-893.
72. J. Delon, and B. Rougé Small baseline stereovision J. Math. Imag. Vision 28 3 2007 209 223
73. J. Delpiano, J. Jara, J. Scheer, O.A. Ramírez, J. Ruiz-del Solar, and S. Härtel Performance of optical flow techniques for motion analysis of fluorescent point signals in confocal microscopy Mach. Vis. Appl. 23 4 2012 675 689
74. O. Demetz, M. Stoll, S. Volz, J. Weickert, A. Bruhn, Learning brightness transfer functions for the joint recovery of illumination changes and optical flow, in: European Conference on Computer Vision (ECCV), Zürich, September 2014, pp. 455-471.
75. P. Dérian, P. Héas, C. Herzet, É. Mémin, Wavelet-based fluid motion estimation, in: Scale Space and Variational Methods in Computer Vision (SSVM), 2011, pp. 737-748.
76. P. Dérian, P. Héas, C. Herzet, and E. Memin Wavelets and optical flow motion estimation Numer. Math.: Theory Methods Appl. 6 1 2013 117 137
77. R. Deriche, P. Kornprobst, and G. Aubert Optical-flow estimation while preserving its discontinuities: a variational approach Recent Developments in Computer Vision LNCS vol. 1035 1996 Springer 69 80
78. M. Drulea, S. Nedevschi, Total variation regularization of local-global optical flow, in: Intelligent Transportation Systems Conference (ITSC), 2011, pp. 318-323.
79. M. Drulea, and S. Nedevschi Motion estimation using the correlation transform IEEE Trans. Image Process. 22 8 2013 3260 3270
80. R. Dupont, N. Paragios, R. Keriven, and P. Fuchs Extraction of layers of similar motion through combinatorial techniques Energy Minimization Methods in Computer Vision and Pattern Recognition 2005 Springer 220 234
81. W. Enkelmann Investigations of multigrid algorithms for the estimation of optical flow fields in image sequences Comput. Vision Graph. Image Process. 43 2 1988 150 177
82. W. Enkelmann Obstacle detection by evaluation of optical flow fields from image sequences Image Vis. Comput. 9 3 1991 160 168
83. G. Facciolo, N. Limare, and E. Meinhardt Integral images for block matching Image Process. On Line 2013
84. S. Fazekas, T. Amiaz, D. Chetverikov, and N. Kiryati Dynamic texture detection based on motion analysis Int. J. Comput. Vision 82 1 2009 48 63
85. S. Fazekas, D. Chetverikov, Normal versus complete flow in dynamic texture recognition: a comparative study, in: Workshop on Texture Analysis and Synthesis at International Conference on Computer Vision (ICCV), Beijin, China, 2005, pp. 37-42.
86. A. Fix, A. Gruber, E. Boros, R. Zabih, A graph cut algorithm for higher-order Markov random fields, in: International Conference on Computer Vision (ICCV), 2011, pp. 1020-1027.
87. D.J. Fleet, M.J. Black, Y. Yacoob, and A.D. Jepson Design and use of linear models for image motion analysis Int. J. Comput. Vision 36 3 2000 171 193
88. D. Fortun, P. Bouthemy, C. Kervrann, Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow, preprint arXiv:1407.5759.
89. D. Fortun, P. Bouthemy, P. Paul-Gilloteaux, and C. Kervrann Aggregation of patch-based estimations for illumination-invariant optical flow in live cell imaging International Symposium on Biomedical Imaging (ISBI) 2013 IEEE 660 663
90. D. Fortun, C. Chen, P. Paul-Gilloteaux, F. Waharte, J. Salamero, C. Kervrann, Correlation and variational approaches for motion and diffusion estimation in fluorescence imaging, in: European Signal Processing Conference (EUSIPCO), September 2013, pp. 660-663.
91. D.R. Fulkerson Flows in Networks 1962 Princeton University Press
92. R. Gal, N. Kiryati, and N. Sochen Progress in the restoration of image sequences degraded by atmospheric turbulence Pattern Recogn. Lett. 14 2014 8 14
93. R. Garg, A. Roussos, and L. Agapito Robust trajectory-space TV-L1 optical flow for non-rigid sequences Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR) 2011 Springer 300 314
94. A. Geiger, P. Lenz, R. Urtasun, Are we ready for autonomous driving? The KITTI vision benchmark suite, in: Computer Vision and Pattern Recognition (CVPR), 2012, pp. 3354-3361.
95. M. Gelgon, P. Bouthemy, and T. Dubois A region tracking method with failure detection for an interactive video indexing environment Visual Information and Information Systems 1999 Springer 261 269
96. D. Geman, and G. Reynolds Constrained restoration and the recovery of discontinuities IEEE Trans. Pattern Anal. Mach. Intell. 14 3 1992 367 383
97. S. Geman, and D. Geman Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images IEEE Trans. Pattern Anal. Mach. Intell. 6 1984 721 741
98. A. Giachetti, M. Campani, and V. Torre The use of optical flow for road navigation IEEE Trans. Robotics Autom. 14 1 1998 34 48
99. B. Glocker, T. Heibel, N. Navab, P. Kohli, C. Rother, Triangleflow: optical flow with triangulation-based higher-order likelihoods, in: European Conference on Computer Vision (ECCV), 2010, pp. 272-285.
100. B. Glocker, N. Komodakis, N. Paragios, G. Tziritas, and N. Navab Inter and intra-modal deformable registration: continuous deformations meet efficient optimal linear programming Information Processing in Medical Imaging (IPMI) 2007 Springer 408 420
101. B. Glocker, N. Paragios, N. Komodakis, G. Tziritas, N. Navab, Optical flow estimation with uncertainties through dynamic MRFs, in: Computer Vision and Pattern Recognition (CVPR), Anchorage, Alaska, June 2008, pp. 1-8.
102. A.V. Goldberg, and R.E. Tarjan A new approach to the maximum-flow problem J. ACM (JACM) 35 4 1988 921 940
103. P. Golland, and A.M. Bruckstein Motion from color Comput. Vis. Image Underst. 68 3 1997 346 362
104. S. Grauer-Gray, C. Kambhamettu, Hierarchical belief propagation to reduce search space using cuda for stereo and motion estimation, in: Workshop on Applications of Computer Vision (WACV), 2009, pp. 1-8.
105. S. Grewenig, J. Weickert, A. Bruhn, From box filtering to fast explicit diffusion, in: DAGM Symposium on Pattern Recognition, 2010, pp. 533-542.
106. M.D. Grossberg, and S.K. Nayar Modeling the space of camera response functions IEEE Trans. Pattern Anal. Mach. Intell. 26 10 2004 1272 1282
107. P. Gwosdek, H. Zimmer, S. Grewenig, A. Bruhn, J. Weickert, A highly efficient GPU implementation for variational optic flow based on the Euler-Lagrange framework, in: Trends and Topics in Computer Vision, 2012, pp. 372-383.
108. D. Hafner, O. Demetz, J. Weickert, Why is the census transform good for robust optic flow computation?, in: Scale Space and Variational Methods in Computer Vision (SSVM), 2013, pp. 210-221.
109. N. Hata, A. Nabavi, W.M. Wells III, S.K. Warfield, R. Kikinis, P.M. Black, and F.A. Jolesz Three-dimensional optical flow method for measurement of volumetric brain deformation from intraoperative mr images J. Comput. Assist. Tomogr. 24 4 2000 531 538
110. H.W. Haussecker, and D.J. Fleet Computing optical flow with physical models of brightness variation IEEE Trans. Pattern Anal. Mach. Intell. 23 6 2001 661 673
111. K. He, J. Sun, X. Tang, Guided image filtering, in: European Conference on Computer Vision (ECCV), 2010, pp. 1-14.
112. P. Héas, C. Herzet, and E. Memin Bayesian inference of models and hyperparameters for robust optical-flow estimation IEEE Trans. Image Process. 21 4 2012 1437 1451
113. P. Héas, E. Mémin, N. Papadakis, and A. Szantai Layered estimation of atmospheric mesoscale dynamics from satellite imagery Trans. Geosci. Remote Sens. 45 12 2007 4087 4104
114. P. Heise, S. Klose, B. Jensen, A. Knoll, PM-Huber: patchmatch with Huber regularization for stereo matching, in: International Conference on Computer Vision (ICCV), 2013.
115. D. Heitz, E. Mémin, and C. Schnörr Variational fluid flow measurements from image sequences: synopsis and perspectives Experim. Fluids 48 3 2010 369 393
116. F. Heitz, and P. Bouthemy Multimodal estimation of discontinuous optical flow using Markov random fields IEEE Trans. Pattern Anal. Mach. Intell. 15 12 1993 1217 1232
117. F. Heitz, P. P eÍrez, and P. Bouthemy Multiscale minimization of global energy functions in some visual recovery problems CVGIP: Image Understand. 59 1 1994 125 134
118. S. Hermann, and R. Klette Hierarchical scan-line dynamic programming for optical flow using semi-global matching Workshops of Asian Conference Computer Vision 2013 Springer 556 567
119. P.W. Holland, and R.E. Welsch Robust regression using iteratively reweighted least-squares Commun. Stat.-Theory Methods 6 9 1977 813 827
120. B. Horn, and B. Schunck Determining optical flow Artif. Intell. 17 1-3 1981 185 203
121. M. Hornácek, F. Besse, J. Kautz, A. Fitzgibbon, C. Rother, Highly overparameterized optical flow using PatchMatch belief propagation, in: European Conference on Computer Vision (ECCV), LNCS, Part III, Zürich, vol. 8689, September 2014, pp. 220-234.
122. A. Hosni, C. Rhemann, M. Bleyer, C. Rother, and M. Gelautz Fast cost-volume filtering for visual correspondence and beyond IEEE Trans. Pattern Anal. Mach. Intell. 35 2 2013 504 511
123. W. Hu, N. Xie, L. Li, X. Zeng, and S. Maybank A survey on visual content-based video indexing and retrieval IEEE Trans. Syst. Man Cybern. 41 6 2011 797 819
124. T.-C. Huang, C.-K. Chang, C.-H. Liao, and Y.-J. Ho Quantification of blood flow in internal cerebral artery by optical flow method on digital subtraction angiography in comparison with time-of-flight magnetic resonance angiography PloS one 8 1 2013 54678
125. P.J. Huber Robust Statistics 1981 Springer
126. A. Humayun, O. Mac Aodha, G.J. Brostow, Learning to find occlusion regions, in: Computer Vision and Pattern Recognition (CVPR), 2011, pp. 2161-2168.
127. S. Ince, and J. Konrad Occlusion-aware optical flow estimation IEEE Trans. Image Process. 17 8 2008 1443 1451
128. H. Ishikawa, Higher-order clique reduction in binary graph cut, in: Computer Vision and Pattern Recognition (CVPR), 2009, pp. 2993-3000.
129. N. Jacobson, Y. Freund, and T. Nguyen An online learning approach to occlusion boundary detection IEEE Trans. Image Process. 21 1 2012 252 261
130. M. Jain, H. Jégou, P. Bouthemy, Better exploiting motion for better action recognition, in: Computer Vision and Pattern Recognition (CVPR), Portland, Oregon, June 2013, pp. 2555-2562.
131. M. Jakubowski, and G. Pastuszak Block-based motion estimation algorithms a survey Opto-Electron. Rev. 21 1 2013 86 102
132. P.-M. Jodoin, and M. Mignotte Optical-flow based on an edge-avoidance procedure Comput. Vis. Image Underst. 113 4 2009 511 531
133. S. Ju, M. Black, A. Jepson, Skin and bones: multi-layer, locally affine, optical flow and regularization with transparency, in: Computer Vision and Pattern Recognition (CVPR), 1996, pp. 307-314.
134. E.M. Kalmoun, and L. Garrido Trust region versus line search for computing the optical flow Multiscale Model. Simul. 11 3 2013 890 906
135. E.M. Kalmoun, L. Garrido, and V. Caselles Line search multilevel optimization as computational methods for dense optical flow SIAM J. Imag. Sci. 4 2 2011 695 722
136. J.H. Kappes, B. Andres, F.A. Hamprecht, C. Schnörr, S. Nowozin, D. Batra, S. Kim, B.X. Kausler, J. Lellmann, N. Komodakis, et al., A comparative study of modern inference techniques for discrete energy minimization problems, in: Computer Vision and Pattern Recognition (CVPR), 2013.
137. C. Kervrann, J. Boulanger, T. Pécot, P. Pérez, and J. Salamero Multiscale neighborhood-wise decision fusion for redundancy detection in image pairs SIAM J. Multiscale Model. Simul. 9 4 2011 1829 1865
138. C. Kervrann, and F. Heitz Statistical deformable model-based segmentation of image motion Trans. Image Process. 8 4 1999 583 588
139. I.-H. Kim, Y.-C.M. Chen, D.L. Spector, R. Eils, and K. Rohr Nonrigid registration of 2-d and 3-d dynamic cell nuclei images for improved classification of subcellular particle motion IEEE Trans. Image Process. 20 4 2011 1011 1022
140. T.H. Kim, H.S. Lee, K.M. Lee, Optical flow via locally adaptive fusion of complementary data costs, in: International Conference on Computer Vision (ICCV), 2013.
141. Y. Kim, A. Martínez, A. Kak, A local approach for robust optical flow estimation under varying illumination, in: British Machine Vision Conference (BMVC), 2004, pp. 1-10.
142. Y.-H. Kim, A.M. Martínez, and A.C. Kak Robust motion estimation under varying illumination Image Vis. Comput. 23 4 2005 365 375
143. N. Kiryati, T.R. Raviv, Y. Ivanchenko, S. Rochel, Real-time abnormal motion detection in surveillance video, in: International Conference on Pattern Recognition (ICPR), 2008, pp. 1-4.
144. 3 & beyond: move making algorithms for solving higher order functions IEEE Trans. Pattern Anal. Mach. Intell. 31 9 2009 1645 1656
145. P. Kohli, and P.H. Torr Dynamic graph cuts for efficient inference in Markov random fields IEEE Trans. Pattern Anal. Mach. Intell. 29 12 2007 2079 2088
146. D.L. Kolin, and P.W. Wiseman Advances in image correlation spectroscopy: measuring number densities, aggregation states, and dynamics of fluorescently labeled macromolecules in cells Cell Biochem. Biophys. 49 3 2007 141 164
147. V. Kolmogorov Convergent tree-reweighted message passing for energy minimization IEEE Trans. Pattern Anal. Mach. Intell. 28 10 2006 1568 1583
148. V. Kolmogorov, and C. Rother Minimizing nonsubmodular functions with graph cuts-a review IEEE Trans. Pattern Anal. Mach. Intell. 29 7 2007 1274 1279
149. V. Kolmogorov, R. Zabih, Computing visual correspondences with occlusion using graph cuts, in: IEEE Int. Conference on Computer Vision (ICCV), July 2001.
150. N. Komodakis, N. Paragios, Beyond pairwise energies: efficient optimization for higher-order MRFs, in: Computer Vision and Pattern Recognition (CVPR), 2009, pp. 2985-2992.
151. N. Komodakis, and G. Tziritas Image completion using efficient belief propagation via priority scheduling and dynamic pruning IEEE Trans. Image Process. 16 11 2007 2649 2661
152. N. Komodakis, G. Tziritas, and N. Paragios Performance vs computational efficiency for optimizing single and dynamic MRFs: setting the state of the art with primal-dual strategies Comput. Vis. Image Underst. 112 1 2008 14 29
153. J. Konrad, and E. Dubois Bayesian estimation of motion vector fields IEEE Trans. Pattern Anal. Mach. Intell. 14 9 1992 910 927
154. P. Krähenbühl, V. Koltun, Efficient nonlocal regularization for optical flow, in: European Conference on Computer Vision (ECCV), 2012, pp. 356-369.
155. K. Krajsek, R. Mester, On the equivalence of variational and statistical differential motion estimation, in: Southwest Symposium on Image Analysis and Interpretation, 1996, pp. 11-15.
156. K. Krajsek, and R. Mester Bayesian model selection for optical flow estimation Pattern Recogn. 4713 2007 142 151
157. F.R. Kschischang, B.J. Frey, and H.-A. Loeliger Factor graphs and the sum-product algorithm IEEE Trans. Inform. Theory 47 2 2001 498 519
158. N. Kumar, L. Zhang, S. Nayar, What is a good nearest neighbors algorithm for finding similar patches in images?, in: European Conference on Computer Vision (ECCV), 2008, pp. 364-378.
159. S.-H. Lai Robust image matching under partial occlusion and spatially varying illumination change Comput. Vis. Image Underst. 78 1 2000 84 98
160. K. Lee, D. Kwon, I. Yun, S. Lee, Optical flow estimation with adaptive convolution kernel prior on discrete framework, in: Computer Vision and Pattern Recognition (CVPR), San Fransisco, June 2010, pp. 2504-2511.
161. V. Lempitsky, C. Rother, S. Roth, and A. Blake Fusion moves for Markov random field optimization IEEE Trans. Pattern Anal. Mach. Intell. 32 8 2010 1392 1405
162. M. Leordeanu, A. Zanfir, C. Sminchisescu, Locally affine sparse-to-dense matching for motion and occlusion estimation, in: International Conference on Computer Vision (ICCV), 2013.
163. J. Lewis Fast normalized cross-correlation Vision Interface 1995 120 123
164. W. Li, D. Cosker, M. Brown, R. Tang, Optical flow estimation using Laplacian mesh energy, in: Computer Vision and Pattern Recognition (CVPR), June 2013, pp. 2435-2442.
165. C. Liu, Beyond Pixels: Exploring New Representations and Applications for Motion Analysis, PhD Thesis, Massachusetts Institute of Technology, 2009.
166. C. Liu, J. Yuen, and A. Torralba Sift flow: dense correspondence across scenes and its applications IEEE Trans. Pattern Anal. Mach. Intell. 33 5 2011 978 994
167. K. Liu, S.S. Lienkamp, A. Shindo, J.B. Wallingford, G. Walz, O. Ronneberger, Optical flow guided cell segmentation and tracking in developing tissue, in: International Symposium on Biomedical Imaging (ISBI), 2014, pp. 298-301.
168. T. Liu, and L. Shen Fluid flow and optical flow J. Fluid Mech. 614 2008 253 291
169. D.G. Lowe Distinctive image features from scale-invariant keypoints Int. J. Comput. Vision 60 2 2004 91 110
170. B. Lucas, T. Kanade, An iterative image registration technique with an application to stereo vision, in: International Joint Conference on Artificial Intelligence, 1981, pp. 674-679.
171. J. Luo, and E.E. Konofagou A fast normalized cross-correlation calculation method for motion estimation IEE Trans. Ultrason. Ferroelectr. Frequency Control 57 6 2010 1347 1357
172. Z. Ma, K. He, Y. Wei, J. Sun, E. Wu, Constant time weighted median filtering for stereo matching and beyond, in: International Conference on Computer Vision (ICCV), 2013.
173. M. Maurizot, P. Bouthemy, B. Delyon, A. Juditski, J.M. Odobez, Determination of singular points in 2d deformable flow fields, in: International Conference on Image Processing (ICIP), vol. 3, 1995, pp. 488-491.
174. E. Mémin, and P. Pérez Dense estimation and object-based segmentation of the optical flow with robust techniques IEEE Trans. Image Process. 7 5 1998 703 719
175. E. Mémin, and P. Pérez Hierarchical estimation and segmentation of dense motion fields Int. J. Comput. Vision 46 2 2002 129 155
176. Y. Mileva, A. Bruhn, and J. Weickert Illumination-robust variational optical flow with photometric invariants Pattern Recogn. 2007 152 162
177. A. Mitiche, and J.K. Aggarwal Computer Vision Analysis of Image Motion by Variational Methods 2014 Springer
178. A. Mitiche, and P. Bouthemy Computation and analysis of image motion: a synopsis of current problems and methods Int. J. Comput. Vision 19 1 1996 29 55
179. M.A. Mohamed, B. Mertsching, TV-L1 optical flow estimation with image details recovering based on modified census transform, in: Advances in Visual Computing, 2012, pp. 482-491.
180. J. Molnár, D. Chetverikov, and S. Fazekas Illumination-robust variational optical flow using cross-correlation Comput. Vis. Image Underst. 114 10 2010 1104 1114
181. M. Mozerov Constrained optical flow estimation as a matching problem IEEE Trans. Image Process. 22 5 2013 2044 2055
182. T. Müller, C. Rabe, J. Rannacher, U. Franke, and R. Mester Illumination-robust dense optical flow using census signatures Pattern Recogn. LNCS vol. 6835 2011 Springer 236 245
183. T. Muller, J. Rannacher, C. Rabe, U. Franke, Feature-and depth-supported modified total variation optical flow for 3d motion field estimation in real scenes, in: Computer Vision and Pattern Recognition (CVPR), 2011, pp. 1193-1200.
184. D. Mumford, and J. Shah Optimal approximations by piecewise smooth functions and associated variational problems Commun. Pure Appl. Math. 42 5 1989 577 685
185. D.W. Murray, and B.F. Buxton Scene segmentation from visual motion using global optimization IEEE Trans. Pattern Anal. Mach. Intell. 9 2 1987 220 228
186. H. Nagel, and W. Enkelmann An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences IEEE Trans. Pattern Anal. Mach. Intell. 8 5 1986 565 593
187. H.-H. Nagel. Extending the oriented smoothness constraint into the temporal domain and the estimation of derivatives of optical flow, in: European Conference on Computer Vision (ECCV), 1990, pp. 139-148.
188. S. Negahdaripour Revised definition of optical flow: integration of radiometric and geometric cues for dynamic scene analysis IEEE Trans. Pattern Anal. Mach. Intell. 20 9 1998 961 979
189. L. Ng, V. Solo, Errors-in-variables modelling in optical flow problems, in: International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 5, 1998, pp. 2773-2776.
190. C. Nieuwenhuis, D. Kondermann, and C.S. Garbe Complex motion models for simple optical flow estimation Pattern Recogn. LNCS vol. 6376 2010 Springer 141 150
191. T. Nir, A.M. Bruckstein, and R. Kimmel Over-parameterized variational optical flow Int. J. Comput. Vision 76 2 2008 205 216
192. P. Ochs, Y. Chen, T. Brox, and T. Pock iPiano: inertial proximal algorithm for non-convex optimization SIAM J. Imag. Sci. 7 2 2013 1388 1419
193. J.M. Odobez, and P. Bouthemy Robust multiresolution estimation of parametric motion models J. Visual Commun. Image Represent. 6 4 1995 348 365
194. J.M. Odobez, and P. Bouthem Separation of moving regions from background in an image sequence acquired with a mobile camera H.-H. Li, S. Sun, H. Derin, Video Data Compression for Multimedia Computing Series in Engineering and Computer Science vol. 378 1997 Springer 283 311 (Chapter 8)
195. J.M. Odobez, and P. Bouthemy Direct incremental model-based image motion segmentation for video analysis Signal Process. 66 2 1998 143 155
196. G. Panin, Mutual information for multi-modal, discontinuity-preserving image registration, in: Advances in Visual Computing, 2012, pp. 70-81.
197. N. Papadakis, R. Yildizoglu, J.-F. Aujol, and V. Caselles High-dimension multilabel problems: convex or nonconvex relaxation? SIAM J. Imag. Sci. 6 4 2013 2603 2639
198. N. Papenberg, A. Bruhn, T. Brox, S. Didas, and J. Weickert Highly accurate optic flow computation with theoretically justified warping Int. J. Comput. Vision 67 2 2006 141 158
199. N. Paragios, and R. Deriche Geodesic active regions and level set methods for motion estimation and tracking Comput. Vis. Image Underst. 97 3 2005 259 282
200. J. Pearl Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference 1988 Morgan Kaufman
201. M. Pingault, E. Bruno, and D. Pellerin A robust multisacle B-spline function decomposition for estimating motion transparency IEEE Trans. Image Process. 12 11 2003 1416 1426
202. G. Piriou, P. Bouthemy, and J.-F. Yao Recognition of dynamic video contents with global probabilistic models of visual motion IEEE Trans. Image Process. 15 11 2006 3417 3430
203. L. Pizarro, J. Delpiano, P. Aljabar, J. Ruiz-del Solar, D. Rueckert, Towards dense motion estimation in light and electron microscopy, in: International Symposium on Biological Imaging (ISBI), 2011, pp. 1939-1942.
204. T. Pock, M. Pock, and H. Bischof Algorithmic differentiation: application to variational problems in computer vision IEEE Trans. Pattern Anal. Mach. Intell. 29 7 2007 1180 1193
205. R. Ranftl, S. Gehrig, T. Pock, H. Bischof, Pushing the limits of stereo using variational stereo estimation, in: Intelligent Vehicles Symposium (IV), 2012, pp. 401-407.
206. H.A. Rashwan, M.A. García, and D. Puig Variational optical flow estimation based on stick tensor voting IEEE Trans. Image Process. 22 7 2013 589 2599
207. T. Riklin-Raviv, N. Sochen, and N. Kiryati Shape-based mutual segmentation Int. J. Comput. Vision 79 3 2008 231 245
208. S. Roth, and M. Black On the spatial statistics of optical flow Int. J. Comput. Vision 74 1 2007 33 50
209. C. Rother, V. Kolmogorov, V. Lempitsky, M. Szummer, Optimizing binary MRFs via extended roof duality, in: Computer Vision and Pattern Recognition (CVPR), 2007, pp. 1-8.
210. L.I. Rudin, S. Osher, and E. Fatemi Nonlinear total variation based noise removal algorithms Physica D 60 1 1992 259 268
211. D. Rueckert, L.I. Sonoda, C. Hayes, D.L. Hill, M.O. Leach, and D.J. Hawkes Nonrigid registration using free-form deformations: application to breast MR images IEEE Trans. Pattern Anal. Mach. Intell. 18 8 1999 712 721
212. J.A. Schnabel, D. Rueckert, M. Quist, J.M. Blackall, A.D. Castellano-Smith, T. Hartkens, G.P. Penney, W.A. Hall, H. Liu, and C.L. Truwit A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2001 Springer 573 581
213. C. Schnörr, W. Peckar, Motion-based identification of deformable templates, in: International Conference Computer Analysis of Images and Patterns (CAIP), Prague, 1995, pp. 122-129.
214. T. Schoenemann, D. Cremers, Near real-time motion segmentation using graph cuts, in: DAGM Symposium on Pattern Recognition, 2006, pp. 455-464.
215. T. Senst, V. Eiselen, and T. Sikora Robust local optical flow for feature tracking IEEE Trans. Circ. Syst. Video Technol. 22 9 2012 1377 1387
216. A. Shekhovtsov, I. Kovtun, and V. Hlaváč Efficient MRF deformation model for non-rigid image matching Comput. Vis. Image Underst. 112 1 2008 91 99
217. X. Shen, Y. Wu, Sparsity model for robust optical flow estimation at motion discontinuities, in: Computer Vision and Pattern Recognition (CVPR), 2010, pp. 2456-2463.
218. J. Shi, C. Tomasi, Good features to track, in: Computer Vision and Pattern Recognition (CVPR), 1994, pp. 593-600.
219. W. Shi, X. Zhuang, L. Pizarro, W. Bai, H. Wang, K.-P. Tung, P. Edwards, D. Rueckert, Registration using sparse free-form deformations, in: Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2012, pp. 659-666.
220. D. Shulman, J.-Y. Herve, Regularization of discontinuous flow fields, in: Workshop on Visual Motion, 1989, pp. 81-86.
221. E.P. Simoncelli, E.H. Adelson, D.J. Heeger, Probability distributions of optical flow, in: Computer Vision and Pattern Recognition (CVPR), 1991, pp. 310-315.
222. S.N. Sinha, J.-M. Frahm, M. Pollefeys, and Y. Genc Feature tracking and matching in video using programmable graphics hardware Mach. Vis. Appl. 22 1 2011 207 217
223. P. Smith, T. Drummond, and R. Cipolla Layered motion segmentation and depth ordering by tracking edges IEEE Trans. Pattern Anal. Mach. Intell. 26 4 2004 479 494
224. A. Sotiras, C. Davatzikos, and N. Paragios Deformable medical image registration: a survey IEEE Trans. Med. Imag. 32 7 2013 1153 1190
225. A.N. Stein, and M. Hebert Occlusion boundaries from motion: low-level detection and mid-level reasoning Int. J. Comput. Vision 82 3 2009 325 357
226. F. Stein Efficient computation of optical flow using the census transform Pattern Recognition LNCS vol. 3175 2004 Springer 79 86
227. F. Steinbrucker, T. Pock, D. Cremers, Advanced data terms for variational optic flow estimation, in: Vision, Modeling, and Visualization Workshop, 2009.
228. C. Stiller, and J. Konrad Estimating motion in image sequences IEEE Signal Process. Mag. 16 4 1999 70 91
229. C. Strecha, R. Fransens, and L. Van Gool A probabilistic approach to large displacement optical flow and occlusion detection Statistical Methods in Video Processing 2004 Springer 71 82
230. C.-W. Su, H.-Y. Liao, H.-R. Tyan, C.-W. Lin, D.-Y. Chen, and K.-C. Fan Motion flow-based video retrieval IEEE Trans. Multimedia 9 6 2007 1193 1201
231. D. Sun, S. Roth, M. Black, Secrets of optical flow estimation and their principles, in: Computer Vision and Pattern Recognition (CVPR), San Fransisco, June 2010, pp. 2432-2439.
232. D. Sun, S. Roth, J. Lewis, M. Black, Learning optical flow, in: European Conference on Computer Vision (ECCV), 2008, pp. 83-97.
233. D. Sun, E. Sudderth, M. Black, Layered image motion with explicit occlusions, temporal consistency, and depth ordering, in: Advances in Neural Information Processing Systems (NIPS), Vancouver, December 2010, pp. 2226-2234.
234. D. Sun, E.B. Sudderth, M.J. Black, Layered segmentation and optical flow estimation over time, in: Computer Vision and Pattern Recognition (CVPR), 2012, pp. 1768-1775.
235. Z. Sun, G. Bebis, and R. Miller On-road vehicle detection: a review IEEE Trans. Pattern Anal. Mach. Intell. 28 5 2006 694 711
236. R. Szeliski, and H.-Y. Shum Motion estimation with quadtree splines IEEE Trans. Pattern Anal. Mach. Intell. 18 12 1996 1199 1210
237. R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. Rother A comparative study of energy minimization methods for markov random fields with smoothness-based priors IEEE Trans. Pattern Anal. Mach. Intell. 30 6 2008 1068 1080
238. M. Tao, J. Bai, P. Kohli, S. Paris, Simpleflow: a non-iterative, sublinear optical flow algorithm, in: Computer Graphics Forum, vol. 31, 2012, pp. 345-353.
239. C.-H. Teng, S.-H. Lai, Y.-S. Chen, and W.-H. Hsu Accurate optical flow computation under non-uniform brightness variations Comput. Vis. Image Underst. 97 3 2005 315 346
240. M. Tistarelli Multiple constraints to compute optical flow IEEE Trans. Pattern Anal. Mach. Intell. 18 12 1996 1243 1250
241. Trobin, T. Pock, D. Cremers, H. Bischof, An unbiased second-order prior for high-accuracy motion estimation, in: DAGM Symposium on Pattern Recognition, 2008, pp. 396-405.
242. W. Trobin, T. Pock, D. Cremers, H. Bischof, Continuous energy minimization via repeated binary fusion, in: European Conference on Computer Vision (ECCV), Marseille, France, October 2008, pp. 677-690.
243. D. Tzovaras, M.G. Strintzis, and H. Sahinoglou Evaluation of multiresolution block matching techniques for motion and disparity estimation Signal Process.: Image Commun. 6 1 1994 59 67
244. M. Unger, M. Werlberger, T. Pock, H. Bischof, Joint motion estimation and segmentation of complex scenes with label costs and occlusion modeling, in: Computer Vision and Pattern Recognition (CVPR), 2012, pp. 1878-1885.
245. S. Uras, F. Girosi, A. Verri, and V. Torre A computational approach to motion perception Biol. Cybern. 60 2 1988 79 87
246. J. van de Weijer, T. Gevers, Robust optical flow from photometric invariants, in: International Conference on Image Processing (ICIP), vol. 3, 2004, pp. 1835-1838.
247. C. Vásquez, A. Mitiche, and R. Laganière Joint multiresolution segmentation and parametric estimation of image motion by basis function representation and level set evolution IEEE Trans. Pattern Anal. Mach. Intell. 28 5 2006 782 793
248. S. Vedula, S. Baker, P. Rander, R. Collins, and T. Kanade Three-dimensional scene flow IEEE Int. Conference on Computer Vision (ICCV) vol. 2 1999 IEEE 722 729
249. O. Veksler, Efficient Graph-based Energy Minimization Methods in Computer Vision, PhD Thesis, Cornell University, 1999.
250. O. Veksler, Graph cut based optimization for MRFs with truncated convex priors, in: Computer Vision and Pattern Recognition (CVPR), 2007, pp. 1-8.
251. A. Verri, and T. Poggio Motion field and optical flow: qualitative properties IEEE Trans. Pattern Anal. Mach. Intell. 11 5 1989 490 498
252. C. Vogel, S. Roth, K. Schindler, An evaluation of data costs for optical flow, in: DAGM Symposium on Pattern Recognition, 2013, pp. 343-353.
253. S. Volz, A. Bruhn, L. Valgaerts, H. Zimmer, Modeling temporal coherence for optical flow, in: International Conference on Computer Vision (ICCV), 2011, pp. 1116-1123.
254. M.J. Wainwright, T.S. Jaakkola, and A.S. Willsky MAP estimation via agreement on trees: message-passing and linear programming IEEE Trans. Inform. Theory 51 11 2005 3697 3717
255. C. Wang, N. Komodakis, and N. Paragios Markov random field modeling, inference & learning in computer vision & image understanding: a survey Comput. Vis. Image Underst. 117 11 2013 1610 1627
256. H. Wang, A. Kläser, C. Schmid, and C.-L. Liu Dense trajectories and motion boundary descriptors for action recognition Int. J. Comput. Vision 103 1 2013 60 79
257. J. Wang, and E. Adelson Representing moving images with layers IEEE Trans. Image Process. 3 5 1994 625 638
258. Z. Wang, F. Wu, and Z. Hu MSLD: a robust descriptor for line matching Pattern Recogn. 42 5 2009 941 953
259. J. Weber, and J. Malik Robust computation of optical flow in a multi-scale differential framework Int. J. Comput. Vision 14 1 1995 67 81
260. A. Wedel, and D. Cremers Optical flow estimation Stereo Scene Flow for 3D Motion Analysis 2011 Springer 5 30 (Chapter 2)
261. A. Wedel, D. Cremers, T. Pock, H. Bischof, Structure-and motion-adaptive regularization for high accuracy optic flow, in: International Conference on Computer Vision (ICCV), Kyoto, Japan, October 2009, pp. 1663-1668.
262. A. Wedel, T. Pock, J. Braun, U. Franke, D. Cremers, Duality TV-L1 flow with fundamental matrix prior, in: Image and Vision Computing New-Zealand, 2008, pp. 1-6.
263. A. Wedel, T. Pock, C. Zach, H. Bischof, D. Cremers, An improved algorithm for TV-L1 optical flow, in: Statistical and Geometrical Approaches to Visual Motion Analysis, 2009, pp. 23-45.
264. J. Weickert, A. Bruhn, T. Brox, and N. Papenberg A survey on variational optic flow methods for small displacements Mathematical Models for Registration and Applications to Medical Imaging 2006 Springer 103 136
265. J. Weickert, J. Heers, C. Schnörr, K.J. Zuiderveld, O. Scherzer, and H. Siegfried Stiehl Fast parallel algorithms for a broad class of nonlinear variational diffusion approaches Real-Time Imag. 7 1 2001 31 45
266. J. Weickert, and C. Schnörr A theoretical framework for convex regularizers in PDE-based computation of image motion Int. J. Comput. Vision 45 3 2001 245 264
267. J. Weickert, and C. Schnörr Variational optic flow computation with a spatio-temporal smoothness constraint J. Math. Imag. Vision 14 3 2001 245 255
268. P. Weinzaepfel, J. Revaud, Z. Harchaoui, C. Schmid, Deepflow: large displacement optical flow with deep matching, in: International Conference on Computer Vision (ICCV), 2013.
269. M. Werlberger, T. Pock, H. Bischof, Motion estimation with non-local total variation regularization, in: Computer Vision and Pattern Recognition (CVPR), San Fransisco, June 2010, pp. 2464-2471.
270. M. Werlberger, T. Pock, M. Unger, H. Bischof, Optical flow guided TV-L1 video interpolation and restoration, in: Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2011, pp. 273-286.
271. M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers, H. Bischof, Anisotropic Huber-L1 optical flow, in: British Machine Vision Conference (BMVC), 2009.
272. M. Werlberger, M. Unger, T. Pock, H. Bischof, Efficient minimization of the non-local Potts model, in: Scale Space and Variational Methods in Computer Vision (SSVM), 2012, pp. 314-325.
273. O. Williams, M. Isard, J. MacCormick, Estimating disparity and occlusions in stereo video sequences, in: Computer Vision and Pattern Recognition (CVPR), June 2005.
274. Y.-T. Wu, T. Kanade, C.-C. Li, and J. Cohn Image registration using wavelet-based motion model Int. J. Comput. Vision 38 2 2000 129 152
275. M. Xavier, A. Lalande, P.M. Walker, F. Brunotte, and L. Legrand An adapted optical flow algorithm for robust quantification of cardiac wall motion from standard cine-mr examinations IEEE Trans. Inform. Technol. Biomed. 16 5 2012 859 868
276. J. Xiao, H. Cheng, H. Sawhney, C. Rao, M. Isnardi, Bilateral filtering-based optical flow estimation with occlusion detection, in: European Conference on Computer Vision (ECCV), 2006, pp. 211-224.
277. J. Xiao, and M. Shah Motion layer extraction in the presence of occlusions using graph cuts IEEE Trans. Pattern Anal. Mach. Intell. 27 10 2005 1644 1659
278. L. Xu, J. Chen, J. Jia, A segmentation-based variational model for accurate optical flow estimation, in: European Conference on Computer Vision (ECCV), Marseille, October 2008, pp. 671-684.
279. L. Xu, J. Jia, and Y. Matsushita Motion detail preserving optical flow estimation IEEE Trans. Pattern Anal. Mach. Intell. 34 9 2012 1744 1757
280. J. Yang, W. Yin, Y. Zhang, and Y. Wang A fast algorithm for edge-preserving variational multichannel image restoration SIAM J. Imag. Sci. 2 2 2009 569 592
281. K.-J. Yoon, and I.S. Kweon Adaptive support-weight approach for correspondence search IEEE Trans. Pattern Anal. Mach. Intell. 28 4 2006 650 656
282. G. Yu, and J.-M. Morel ASIFT: an algorithm for fully affine invariant comparison SIAM J. Imag. Sci. 2 2009 438 469
283. R. Zabih, J. Woodfill, Non-parametric local transforms for computing visual correspondence, in: European Conference on Computer Vision (ECCV), 1994, pp. 151-158.
284. C. Zach, D. Gallup, J.-M. Frahm, Fast gain-adaptive KLT tracking on the GPU, in: Computer Vision and Pattern Recognition Workshops (CVPRW), 2008, pp. 1-7.
285. C. Zach, T. Pock, H. Bischof, A duality based approach for realtime TV-L1 optical flow, in: DAGM symposium on Pattern Recognition, 2007, pp. 214-223.
286. H. Zimmer, A. Bruhn, and J. Weickert Optic flow in harmony Int. J. Comput. Vision 93 3 2011 1 21
287. C. Zitnick, N. Jojic, S. Kang, Consistent segmentation for optical flow estimation, in: International Conference on Computer Vision (ICCV), Beijing, October 2005, pp. 1308-1315.