17.2 Optical Flow Field Computation and Analysis

Chapter Contents (Back)
Matching, Optical Flow. Optical Flow. See also Computation for Vector Fields, Flow Fields. See also Moving Image Coding, Compression: Using Vector Fields, Flow Fields.

17.2.1 Point Matching for Optical Flow Computation

Chapter Contents (Back)
Matching, Points. Optical Flow, Features.

Enkelmann, W., Kories, R., Nagel, H.H., and Zimmermann, G.,
An Experimental Investigation of Estimation Approaches for Optical Flow Fields,
MU88(189-226). Discussion of 2 optical flow techniques, feature extraction and match and differential or gradient approaches. The conclusion is that for both methods, certain features provide dominant contributions to the optical flow computation. BibRef 8800

Prager, J.M.[John M.], Arbib, M.A.[Michael A.],
Computing the Optic Flow: The MATCH Algorithm and Prediction,
CVGIP(24), No. 3, December 1983, pp. 271-304.
Elsevier DOI Using the smoothness of motion and almost rigid body constraints, the vector field (optic flow field) is computed. The algorithm also assumes that feature points are dense on the objects. Seems to produce results. The work is from 1980. See also Extracting and Labeling Boundary Segments in Natural Scenes. BibRef 8312

Buxton, B.F., Buxton, H.,
Monocular Depth Perception from Optical Flow by Space Time Signal Processing,
RoyalP(B-218), 1983, pp. 27-47. BibRef 8300

Bergmann, H.C.,
Analysis of Different Displacement Estimation Algorithms for Digital Television Signals,
ISPDSA83(215-234). BibRef 8300

Adiv, G.[Gilad],
Determining 3-D Motion and Structure from Optical Flow Generated by Several Moving Objects,
PAMI(7), No. 4, July 1985, pp. 384-401. BibRef 8507
Earlier: DARPA84(113-129). BibRef
Earlier:
Recovering 2-D Motion Parameters in Scenes Containing Multiple Moving Objects,
DARPA83(285-292). BibRef
And: COINSTR 83-11, May, 1983. BibRef
Earlier:
Recovering Motion Parameters in Scenes Containing Multiple Moving Objects,
CVPR83(399-400). Hough. Use of the generalized Hough transform to get the several moving objects. Segment objects based on 2D motion parameters. See also Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Flow Field. BibRef

Williams, L.R., and Hanson, A.R.,
Translating Optical Flow into Token Matches and Depth from Looming,
ICCV88(441-448).
IEEE DOI BibRef 8800
And: COINSTR 88-68, UMass. BibRef
And:
Translating Optical Flow into Token Matches,
DARPA88(970-980). BibRef
And:
Depth from Looming Structure,
DARPA88(1047-1051). Using optical flow to aid the matching of line tokens in motion pairs. BibRef

Castelow, D.A., Murray, D.W., Scott, G.L., Buxton, B.F.,
Matching Canny Edgels to Compute the Principal Components of Optic Flow,
IVC(6), No. 2, May 1988, pp. 129-136.
Elsevier DOI Relaxation algorithm at edges. BibRef 8805

Scott, G.L.,
'Four-Line' Method of Locally Estimating Optic Flow,
IVC(5), No. 2, May 1987, pp. 67-72.
Elsevier DOI BibRef 8705
Earlier:
Smoothing the Optic Flow Field Under Perspective Projection,
CVPR86(504-509). Treating the flow field as 3D vectors (the projection is what we see) seems to help. BibRef

Thompson, W.B.[William B.], and Kearney, J.K.[Joseph K.],
DIDA: Dynamic Image Disparity Analysis,
TRFinal report for July 1981 to December 1982. AFWAL-TR-83-10A35. The report on an analysis of the needs for disparity analysis systems to be useful. Mostly obvious conclusions (as expected in a report of this type to a government lab) but they implemented several alternative disparity systems. The major point is that various applications have different requirements for accuracy, speed, and density of coverage; and that different methods can give you some of these. The best seems to be the gradient based methods with proper post processing to account for the variations and constrain the output. BibRef 8107

Thompson, W.B., Madarasz, R.L., and Kearney, J.K.,
Temporal Constraints for Estimating Optic Flow,
PRIP82(252-255). Relaxation. Computing optic flow using the constraints that disparity changes slowly over space and over time. Temporal continuity is used in the relaxation process (by remembering the past disparity) to restrict future matches and resolve ambiguities. This leads to a substantial speed up successive frames. BibRef 8200

Thompson, W.B., and Barnard, S.T.,
Lower-level Estimates and Interpretation of Visual Motion,
Computer(14), No. 8, August 1981, pp. 20-28. Optical Flow, Gradient Based. How to use the low-level information in motion analysis - gradients of motion, analysis of feature points (tracking-matching) and a little on 3-D analysis. Optimization versus Hough transform for solutions. BibRef 8108

Fuh, C.S., and Maragos, P.,
Motion Displacement Extimation Using an Affine Model for Image Matching,
OptEng(30), No. 7, July 1991, pp. 881-887. BibRef 9107
Earlier:
Region-Based Optical Flow Estimation,
CVPR89(130-135).
IEEE DOI Determine the flow of primitive tokens (regions) by matching. Then smooth the field. Basic. BibRef

Fuh, C.S., Maragos, P., Vinlent, M.,
Visual Motion Correspondence by Region-Based Approaches,
ACCV93(784-789). Region features only. BibRef 9300

Duncan, J.H., and Chou, T.C.,
On the Detection of Motion and the Computation of Optical Flow,
PAMI(14), No. 3, March 1992, pp. 346-352.
IEEE DOI BibRef 9203
Earlier:
Temporal Edges: The Detection of Motion and the Computation of Optical Flow,
ICCV88(374-382).
IEEE DOI Use of spatio-temporal filter approach for optical flow. BibRef

Cornilleau-Pérès, V.[Valérie], Droulez, J.[Jacques],
Velocity-Based Correspondence in Stereokinetic Images,
CVGIP(58), No. 2, September 1993, pp. 137-146.
DOI Link BibRef 9309
Earlier:
Stereo correspondence from optic flow,
ECCV90(326-330).
Springer DOI 9004
Binocular flow as input for stereo correspondence. BibRef

Shapiro, V.[Vladimir], Backalov, I.[Ivailo], Kavardjiko, V.[Vassil],
Motion Analysis Via Interframe Point Correspondence Establishment,
IVC(13), No. 2, March 1995, pp. 111-118.
Elsevier DOI Global consistent point matches BibRef 9503

Huang, Y., Zhuang, X.H., Yang, C.S.,
Two Block-Based Motion Compensation Methods for Video Coding,
CirSysVideo(6), No. 1, February 1996, pp. 123-126.
IEEE Top Reference. BibRef 9602

Huang, Y.[Yan], Zhuang, X.H.[Xin-Hua],
An Adaptively Refined Block Matching Algorithm for Motion Compensated Video Coding,
CirSysVideo(5), No. 1, February 1995, pp. 56-59.
IEEE Top Reference. BibRef 9502
Earlier:
Motion-partitioned adaptive block matching for video compression,
ICIP95(I: 554-557).
IEEE DOI 9510
BibRef

Chaudhury, K., Mehrotra, R.,
A Trajectory-Based Computational Model for Optical-Flow Estimation,
RA(11), No. 5, October 1995, pp. 733-741. BibRef 9510

Bannehr, L., Rohn, M., Warnecke, G.,
A Functional Analytic Method to Derive Displacement Vector-Fields from Satellite Image Sequences,
JRS(17), No. 2, January 20 1996, pp. 383-392. BibRef 9601

Hang, H.M., Leonardi, R., Haskell, B.G., Schmidt, R.L., Bheda, H., Othmer, J.,
Digital HDTV Compression Using Parallel Motion-Compensated Transform Coders,
CirSysVideo(1), No. 2, June 1991, pp. 210-221.
IEEE Top Reference. BibRef 9106

Reusens, E.,
Joint Optimization of Representation Model and Frame Segmentation for Generic Video Compression,
SP(46), No. 1, September 1995, pp. 105-117. BibRef 9509

Pan, J.N., Shi, Y.Q., Shu, C.Q.,
Correlation Feedback Technique in Optical Flow Determination,
IP(7), No. 7, July 1998, pp. 1061-1067.
IEEE DOI 9807
See also Unified Optical-Flow Field Approach To Motion Analysis from a Sequence of Stereo Images. BibRef

Chen, J.L.[Jau-Ling], Chen, P.Y.[Pei-Yin],
An efficient gray search algorithm for the estimation of motion vectors,
SMC-C(31), No. 2, May 2001, pp. 242-248.
IEEE Top Reference. 0109
BibRef

Jou, J.M., Chen, P.Y., Sun, J.M.,
The Gray Prediction Search Algorithm for Block Motion Estimation,
CirSysVideo(9), No. 6, September 1999, pp. 843.
IEEE Top Reference. BibRef 9909

Imiya, A.[Atsushi], Iwawaki, K.[Keisuke],
Voting method for the detection of subpixel flow field,
PRL(24), No. 1-3, January 2003, pp. 197-214.
Elsevier DOI 0211
See also Voting method for planarity and motion detection. BibRef

Sun, C.M.[Chang-Ming],
Fast optical flow using 3D shortest path techniques,
IVC(20), No. 13-14, December 2002, pp. 981-991.
Elsevier DOI 0212
BibRef
Earlier:
Fast Optical Flow Using Cross Correlation and Shortest-Path Techniques,
DICTA99(143-148). Shortest-Path Techniques, fast correlation See also Circular shortest path in images. See also Fast Stereo Matching Using Rectangular Subregioning and 3D Maximum-Surface Techniques. BibRef

Tordoff, B.J.[Ben J.], Murray, D.W.,
Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors,
PAMI(27), No. 10, October 2005, pp. 1523-1535.
IEEE DOI 0509
BibRef
Earlier:
Guided Sampling and Consensus for Motion Estimation,
ECCV02(I: 82 ff.).
Springer DOI
HTML Version. 0205
Maximum-likelihood estimation by random sampling consensus Build on MLESAC ( See also MLESAC: A New Robust Estimator with Application to Estimating Image Geometry. ). BibRef

Roth, S.[Stefan], Black, M.J.[Michael J.],
On the Spatial Statistics of Optical Flow,
IJCV(74), No. 1, August 2007, pp. 33-50.
Springer DOI
PDF File. 0705
BibRef
Earlier: ICCV05(I: 42-49).
IEEE DOI 0510
Award, Marr Prize, HM. Training fields from range images and moving camera. BibRef

Roth, S.[Stefan], Black, M.J.[Michael J.],
Specular Flow and the Recovery of Surface Structure,
CVPR06(II: 1869-1876).
IEEE DOI 0606
BibRef

Ben-Ari, R.[Rami], Sochen, N.A.[Nir A.],
A Geometric Framework and a New Criterion in Optical Flow Modeling,
JMIV(33), No. 2, February 2009, pp. xx-yy.
Springer DOI 0903
BibRef
Earlier:
A General Framework and New Alignment Criterion for Dense Optical Flow,
CVPR06(I: 529-536).
IEEE DOI 0606
See also Geometric Approach for Regularization of the Data Term in Stereo-Vision, A. BibRef

Keren, D.[Daniel],
A Probabilistic Method for Point Matching in the Presence of Noise and Degeneracy,
JMIV(33), No. 3, March 2009, pp. xx-yy.
Springer DOI 0903
Bayesian method to eliminate noise. BibRef

Yang, G.[Gaobo], Chen, W.W.[Wei-Wei], Wang, X.J.[Xiao Jing], Zhang, Z.Y.[Zhao-Yang],
Dense Estimation of Optical Flow Field Within the Mpeg-2 Compressed Domain,
IJIG(9), No. 3, July 2009, pp. 435-448.
DOI Link 0911
BibRef

Yang, G.[Gaobo], Chen, W.W.[Wei-Wei], Zhou, Q.[Qiya], Zhang, Z.Y.[Zhao-Yang],
Optical flow approximation based motion object extraction for MPEG-2 video stream,
RealTimeIP(4), No. 4, November 2009, pp. xx-yy.
Springer DOI 0911
BibRef

Chen, W.[Wei], Mied, R.P.[Richard P.],
Optical flow estimation for motion-compensated compression,
IVC(31), No. 3, March 2013, pp. 275-289.
Elsevier DOI 1303
Optical flow; Optical flow determination; Optical flow estimation; Displacement estimation; Velocity estimation; Motion compensation; Motion-compensated prediction; Motion-compensated interpolation; Motion-Compensated Compression BibRef

Mozerov, M.G.,
Constrained Optical Flow Estimation as a Matching Problem,
IP(22), No. 5, May 2013, pp. 2044-2055.
IEEE DOI 1304
BibRef

Riffi, J.[Jamal], Mahraz, A.M.[Adnane Mohamed], Tairi, H.[Hamid],
Medical image registration based on fast and adaptive bidimensional empirical mode decomposition,
IET-PR(7), No. 6, August 2013, pp. 567-574.
DOI Link 1312
entropy BibRef

Mahraz, A.M.[Adnane Mohamed], Riffi, J.[Jamal], Tairi, H.[Hamid],
Motion estimation using the fast and adaptive bidimensional empirical mode decomposition,
RealTimeIP(9), No. 3, September 2014, pp. 491-501.
WWW Link. 1408
BibRef

Mahraz, A.M.[Adnane Mohamed], Riffi, J.[Jamal], Tairi, H.[Hamid],
High accuracy optical flow estimation based on PDE decomposition,
SIViP(9), No. 6, September 2015, pp. 1409-1418.
WWW Link. 1509
BibRef

Douini, Y.[Youssef], Riffi, J.[Jamal], Mahraz, A.M.[Adnane Mohamed], Tairi, H.[Hamid],
An image registration algorithm based on phase correlation and the classical Lucas-Kanade technique,
SIViP(11), No. 7, October 2017, pp. 1321-1328.
Springer DOI 1708
BibRef
And:
Solving sub-pixel image registration problems using phase correlation and Lucas-Kanade optical flow method,
ISCV17(1-5)
IEEE DOI 1710
subpixel image registration, Classification algorithms, BibRef

Bellamine, I., Tairi, H.,
Optical flow estimation based on the structure-texture image decomposition,
SIViP(9), No. 1 Supp, December 2015, pp. 193-201.
WWW Link. 1601
BibRef

Conze, P.H.[Pierre-Henri], Robert, P.[Philippe], Crivelli, T.[Tomás], Morin, L.[Luce],
Multi-reference combinatorial strategy towards longer long-term dense motion estimation,
CVIU(150), No. 1, 2016, pp. 66-80.
Elsevier DOI 1608
BibRef
Earlier: A1, A3, A2, A4:
Dense motion estimation between distant frames: Combinatorial multi-step integration and statistical selection,
ICIP13(3860-3864)
IEEE DOI 1402
Long-term motion estimation. dense point matching BibRef

Fitzner, D.[Daniel], Sester, M.[Monika],
Field Motion Estimation with a Geosensor Network,
IJGI(5), No. 10, 2016, pp. 175.
DOI Link 1610
For determining various geo values. BibRef

Zhang, C.[Chao], Shen, C.[Chunhua], Shen, T.[Tingzhi],
Unsupervised Feature Learning for Dense Correspondences Across Scenes,
IJCV(116), No. 1, January 2016, pp. 90-107.
Springer DOI 1601
dense pixel correspondences across scenes. Compare with: SIFT flow ( See also SIFT Flow: Dense Correspondence across Scenes and Its Applications. ). Coherency Sensitive Hashing ( See also Coherency Sensitive Hashing. Deformable Spatial Pyramid Matching ( See also Deformable Spatial Pyramid Matching for Fast Dense Correspondences. BibRef

Lu, J.B.[Jiang-Bo], Li, Y., Yang, H.S.[Hong-Sheng], Min, D.B.[Dong-Bo], Eng, W., Do, M.N.[Minh N.],
PatchMatch Filter: Edge-Aware Filtering Meets Randomized Search for Visual Correspondence,
PAMI(39), No. 9, September 2017, pp. 1866-1879.
IEEE DOI 1708
BibRef
Earlier: A1, A3, A4, A6, Only:
Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation,
CVPR13(1854-1861)
IEEE DOI 1309
Complexity theory, Computer vision, Electronic mail, Estimation, Labeling, Optical filters, Optical imaging, Approximate nearest neighbor, edge-aware filtering, optical flow, stereo, matching BibRef

Lin, W.Y.[Wen-Yan], Wang, F.[Fan], Cheng, M.M.[Ming-Ming], Yeung, S.K.[Sai-Kit], Torr, P.H.S.[Philip H.S.], Do, M.N.[Minh N.], Lu, J.B.[Jiang-Bo],
CODE: Coherence Based Decision Boundaries for Feature Correspondence,
PAMI(40), No. 1, January 2018, pp. 34-47.
IEEE DOI 1712
Coherence, Computational modeling, Mathematical model, Noise measurement, Optical imaging, Pattern matching, Robustness, wide-baseline matching BibRef

Braux-Zin, J.[Jim], Dupont, R.[Romain], Bartoli, A.E.[Adrien E.], Tamaazousti, M.[Mohamed],
EasyFlow: Increasing the Convergence Basin of Variational Image Matching with a Feature-Based Cost,
IET-CV(11), No. 2, March 2017, pp. 122-134.
DOI Link 1703
BibRef

Ryu, S.C.[Seung-Chul], Kim, S.R.[Seung-Ryong], Sohn, K.H.[Kwang-Hoon],
LAT: Local area transform for cross modal correspondence matching,
PR(63), No. 1, 2017, pp. 218-228.
Elsevier DOI 1612
Cross-modal correspondence matching BibRef

Kim, S.R.[Seung-Ryong], Ryu, S.C.[Seung-Chul], Ham, B.[Bumsub], Kim, J.Y.[Junh-Yung], Sohn, K.H.[Kwang-Hoon],
Local self-similarity frequency descriptor for multispectral feature matching,
ICIP14(5746-5750)
IEEE DOI 1502
Correlation BibRef

Jeong, S., Kim, S.R.[Seung-Ryong], Ham, B.S.[Bum-Sub], Sohn, K.H.[Kwang-Hoon],
Convolutional cost aggregation for robust stereo matching,
ICIP17(2523-2527)
IEEE DOI 1803
Aggregates, Color, Convolution, Image color analysis, Kernel, Robustness, convolutional neural networks, cost aggregation, stereo matching BibRef

Kim, S.R.[Seung-Ryong], Min, D.B.[Dong-Bo], Ham, B.S.[Bum-Sub], Jeon, S.[Sangryul], Lin, S.[Stephen], Sohn, K.H.[Kwang-Hoon],
FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence,
CVPR17(616-625)
IEEE DOI 1711
Cascading style sheets, Convolutional codes, Estimation, Robustness, Semantics, Training BibRef

Kim, S.R.[Seung-Ryong], Yoo, H.[Hunjae], Ryu, S.C.[Seung-Chul], Ham, B.[Bumsub], Sohn, K.H.[Kwang-Hoon],
ABFT: Anisotropic binary feature transform based on structure tensor space,
ICIP13(2920-2923)
IEEE DOI 1402
Feature matching; anisotropic; binary feature; structure tensor BibRef

Kim, S.R.[Seung-Ryong], Min, D.B.[Dong-Bo], Ham, B.[Bumsub], Do, M.N.[Minh N.], Sohn, K.H.[Kwang-Hoon],
DASC: Robust Dense Descriptor for Multi-Modal and Multi-Spectral Correspondence Estimation,
PAMI(39), No. 9, September 2017, pp. 1712-1729.
IEEE DOI 1708
Benchmark testing, Image edge detection, Imaging, Optimization, Pattern analysis, Robustness, Dense correspondence, descriptor, edge-aware filtering, multi-modal, multi-spectral BibRef

Kim, S.R.[Seung-Ryong], Min, D.B.[Dong-Bo], Ham, B.[Bumsub], Ryu, S.[Seungchul], Do, M.N.[Minh N.], Sohn, K.H.[Kwang-Hoon],
DASC: Dense adaptive self-correlation descriptor for multi-modal and multi-spectral correspondence,
CVPR15(2103-2112)
IEEE DOI 1510
BibRef

Jeon, S.[Sangryul], Kim, S.R.[Seung-Ryong], Min, D.B.[Dong-Bo], Sohn, K.H.[Kwang-Hoon],
PARN: Pyramidal Affine Regression Networks for Dense Semantic Correspondence,
ECCV18(VI: 355-371).
Springer DOI 1810
BibRef

Son, J.[Jongin], Kim, S.R.[Seung-Ryong], Sohn, K.H.[Kwang-Hoon],
Fast affine-invariant image matching based on global Bhattacharyya measure with adaptive tree,
ICIP15(3190-3194)
IEEE DOI 1512
ASIFT BibRef

Park, K.[Kihong], Kim, S.R.[Seung-Ryong], Ryu, S.[Seungchul], Sohn, K.H.[Kwang-Hoon],
Randomized Global Transformation Approach for Dense Correspondence,
BMVC15(xx-yy).
DOI Link 1601
BibRef

Kim, S.R.[Seung-Ryong], Min, D.B.[Dong-Bo], Lin, S.[Stephen], Sohn, K.H.[Kwang-Hoon],
Deep Self-correlation Descriptor for Dense Cross-Modal Correspondence,
ECCV16(VIII: 679-695).
Springer DOI 1611
BibRef

Rahmani, F.[Farzaneh], Zargari, F.[Farzad], Ghanbari, M.[Mohammad],
Improving the robustness of motion vector temporal descriptor,
IET-IPR(12), No. 1, January 2018, pp. 98-104.
DOI Link 1712
due to different resolutions of data. BibRef


Kim, S.R.[Seung-Ryong], Min, D.B.[Dong-Bo], Lin, S.[Stephen], Sohn, K.H.[Kwang-Hoon],
DCTM: Discrete-Continuous Transformation Matching for Semantic Flow,
ICCV17(4539-4548)
IEEE DOI 1802
affine transforms, cellular neural nets, edge detection, filtering theory, image matching, image representation, Strain BibRef

Maier, J.[Josef], Humenberger, M.[Martin], Murschitz, M.[Markus], Zendel, O.[Oliver], Vincze, M.[Markus],
Guided Matching Based on Statistical Optical Flow for Fast and Robust Correspondence Analysis,
ECCV16(VII: 101-117).
Springer DOI 1611
BibRef

Caetano, C., dos Santos, J.A., Schwartz, W.R.,
Optical Flow Co-occurrence Matrices: A novel spatiotemporal feature descriptor,
ICPR16(1947-1952)
IEEE DOI 1705
Computer vision, Feature extraction, Image motion analysis, Optical distortion, Optical imaging, Optical variables measurement, Spatiotemporal phenomena BibRef

Bai, M.[Min], Urtasun, R.[Raquel], Bai, M., Urtasun, R.,
Deep Watershed Transform for Instance Segmentation,
CVPR17(2858-2866)
IEEE DOI 1711
Feature extraction, Image segmentation, Neural networks, Proposals, Semantics, Transforms BibRef

Bai, M.[Min], Luo, W.J.[Wen-Jie], Kundu, K.[Kaustav], Urtasun, R.[Raquel],
Exploiting Semantic Information and Deep Matching for Optical Flow,
ECCV16(VI: 154-170).
Springer DOI 1611
BibRef

Kim, S., Min, D., Sohn, K.,
ANCC flow: Adaptive normalized cross-correlation with evolving guidance aggregation for dense correspondence estimation,
ICIP16(3454-3458)
IEEE DOI 1610
Computational modeling BibRef

Xiang, J., Li, Z., Blaauw, D., Kim, H.S., Chakrabarti, C.,
Low complexity optical flow using neighbor-guided semi-global matching,
ICIP16(4483-4487)
IEEE DOI 1610
Complexity theory BibRef

Schuster, R., Bailer, C., Wasenmüller, O., Stricker, D.,
FlowFields++: Accurate Optical Flow Correspondences Meet Robust Interpolation,
ICIP18(1463-1467)
IEEE DOI 1809
Interpolation, Robustness, Optical imaging, Machine learning, Image edge detection, Adaptive optics, Optimization, Interpolation, Optical Flow BibRef

Schuster, R., Wasenmuller, O., Kuschk, G., Bailer, C., Stricker, D.,
SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences,
WACV18(1056-1065)
IEEE DOI 1806
image matching, image motion analysis, image segmentation, image sequences, interpolation, iterative methods, minimisation, BibRef

Bailer, C., Varanasi, K., Stricker, D.,
CNN-Based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss,
CVPR17(2710-2719)
IEEE DOI 1711
Estimation, Fasteners, Image resolution, Optical imaging, Optical losses, Robustness, Training BibRef

Baghaie, A.[Ahmadreza], d'Souza, R.M.[Roshan M.], Yu, Z.Y.[Ze-Yun],
Dense Correspondence and Optical Flow Estimation Using Gabor, Schmid and Steerable Descriptors,
ISVC15(I: 406-415).
Springer DOI 1601
BibRef

Michielin, F., Calvagno, G., Sartor, P., Emmerich, T., Unruh, C., Erdler, O.,
A parallel true motion estimation method based on binarized cross correlation,
ICIP14(1198-1202)
IEEE DOI 1502
Computational efficiency BibRef

Yang, H.S.[Hong-Sheng], Lin, W.Y.[Wen-Yan], Lu, J.B.[Jiang-Bo],
DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences,
CVPR14(3406-3413)
IEEE DOI 1409
BibRef

Hontani, H.[Hidekata], Oishi, G.[Go], Kitagawa, T.[Tomohiro],
Local Estimation of High Velocity Optical Flow with Correlation Image Sensor,
ECCV14(III: 235-249).
Springer DOI 1408
BibRef

Qiu, W.C.[Wei-Chao], Wang, X.G.[Xing-Gang], Bai, X.[Xiang], Yuille, A.L.[Alan L.], Tu, Z.W.[Zhuo-Wen],
Scale-Space SIFT flow,
WACV14(1112-1119)
IEEE DOI 1406
Computer vision BibRef

Hermann, S.[Simon],
Evaluation of Scan-Line Optimization for 3D Medical Image Registration,
CVPR14(3073-3080)
IEEE DOI 1409
BibRef

Hermann, S.[Simon], Werner, R.[René],
High Accuracy Optical Flow for 3D Medical Image Registration Using the Census Cost Function,
PSIVT13(23-35).
Springer DOI 1402
BibRef
And:
TV-L1-Based 3D Medical Image Registration with the Census Cost Function,
PSIVT13(149-161).
Springer DOI 1402
BibRef

Kim, T.H.[Tae Hyun], Lee, H.S.[Hee Seok], Lee, K.M.[Kyoung Mu],
Optical Flow via Locally Adaptive Fusion of Complementary Data Costs,
ICCV13(3344-3351)
IEEE DOI 1403
BibRef

Kato, T.[Tomoya], Itoh, H.[Hayato], Imiya, A.[Atsushi],
Optical Flow Computation with Locally Quadratic Assumption,
CAIP15(I:223-234).
Springer DOI 1511
BibRef

Itoh, H.[Hayato], Inagaki, S.[Shun], Fan, M.Y.[Ming-Ying], Imiya, A.[Atsushi], Kawamoto, K.[Kazuhiko], Sakai, T.[Tomoya],
Local Affine Optical Flow Computation,
PSIVTWS13(203-215).
Springer DOI 1402
BibRef

Wang, B.T.[Bo-Tao], Zhu, Q.X.[Qing-Xiang], Xiong, H.K.[Hong-Kai],
Optimization of variational methods via motion-based weight selection and keypoint matching,
VCIP12(1-6).
IEEE DOI 1302
BibRef

Garrigues, M.[Matthieu], Manzanera, A.[Antoine],
Real Time Semi-dense Point Tracking,
ICIAR12(I: 245-252).
Springer DOI 1206
BibRef

Song, H.J.[Hua-Jun], Shen, M.L.[Mei-Li],
Optic Flow Target Tracking Method Based on Corner Detection,
CISP09(1-5).
IEEE DOI 0910
BibRef

Smith, T.M.A.[Timothy M. A.], Redmill, D.W.[David W.], Canagarajah, C.N.[C. Nishan], Bull, D.R.[David R.],
A framework for dense optical flow from multiple sparse hypotheses,
ICIP08(837-840).
IEEE DOI 0810
BibRef

Liu, C.[Ce], Yuen, J.[Jenny], Torralba, A.[Antonio],
SIFT Flow: Dense Correspondence across Scenes and Its Applications,
PAMI(33), No. 1, January 2011, pp. 978-994.
IEEE DOI 1104
Matching dense SIFT features between 2 views BibRef

Freeman, W.T.[William T.], Torralba, A.[Antonio], Yuen, J.[Jenny], Liu, C.[Ce],
SIFT Flow: Dense Correspondence across Scenes and its Applications R,
CSAIL(TR-2010-024). 2010-05-08
WWW Link. 1101
Matching dense SIFT features between 2 views BibRef

Liu, C.[Ce], Yuen, J.[Jenny], Torralba, A.[Antonio], Sivic, J.[Josef], Freeman, W.T.[William T.],
SIFT Flow: Dense Correspondence across Different Scenes,
ECCV08(III: 28-42).
Springer DOI 0810
BibRef

Tandjung, S.S., Soon, S.H.[Seah Hock], Kemao, Q.,
Block Motion Model for Optical Flow with Smoothness Prior Function,
ICARCV06(1-6).
IEEE DOI 0612
BibRef

Tandjung, S.S., Gunawan, T., Chong, M.,
Motion Estimation Using Adaptive Blocksize Observation Model and Efficient Multiscale Regularization,
ICIP00(Vol II: 566-569).
IEEE DOI 0008
BibRef

Chapter on Optical Flow Field Computations and Use continues in
Optical Flow -- Matching Using Areas .


Last update:Oct 15, 2018 at 09:19:25