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-IPR(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.H.[Chun-Hua],
Shen, T.Z.[Ting-Zhi],
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, 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
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
Ghoul, K.[Khalid],
Berkane, M.[Mohamed],
Batouche, M.C.[Mohamed Chaouki],
A neural-based method for optical flow estimation using phase
correlation,
IJCVR(8), No. 5, 2018, pp. 526-542.
DOI Link
1810
BibRef
Yuan, W.[Wei],
Yuan, X.X.[Xiu-Xiao],
Xu, S.[Shu],
Gong, J.Y.[Jian-Ya],
Shibasaki, R.[Ryosuke],
Dense Image-Matching via Optical Flow Field Estimation and
Fast-Guided Filter Refinement,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link
1910
BibRef
Kim, S.R.[Seung-Ryong],
Min, D.B.[Dong-Bo],
Lin, S.[Stephen],
Sohn, K.H.[Kwang-Hoon],
Discrete-Continuous Transformation Matching for Dense Semantic
Correspondence,
PAMI(42), No. 1, January 2020, pp. 59-73.
IEEE DOI
1912
BibRef
Earlier:
DCTM: Discrete-Continuous Transformation Matching for Semantic Flow,
ICCV17(4539-4548)
IEEE DOI
1802
BibRef
And: A1, A2, A4, Only:
ANCC flow: Adaptive normalized cross-correlation with evolving
guidance aggregation for dense correspondence estimation,
ICIP16(3454-3458)
IEEE DOI
1610
Semantics, Optimization, Strain, Computational modeling,
Optical imaging, Labeling, Convolution,
interative inference.
affine transforms, cellular neural nets, edge detection,
filtering theory, image matching, image representation, Strain
BibRef
Kim, S.R.[Seung-Ryong],
Min, D.B.[Dong-Bo],
Lin, S.[Stephen],
Sohn, K.H.[Kwang-Hoon],
Dense Cross-Modal Correspondence Estimation With the Deep
Self-Correlation Descriptor,
PAMI(43), No. 7, July 2021, pp. 2345-2359.
IEEE DOI
2106
Strain, Lighting, Estimation, Benchmark testing, Imaging, Robustness,
Visualization, Cross-modal correspondence, pyramidal structure,
non-rigid deformation
BibRef
Jia, D.[Di],
Wang, K.[Kai],
Luo, S.L.[Shun-Li],
Liu, T.Y.[Tian-Yu],
Liu, Y.[Ying],
BRAFT: Recurrent All-Pairs Field Transforms for Optical Flow Based on
Correlation Blocks,
SPLetters(28), 2021, pp. 1575-1579.
IEEE DOI
2108
Correlation, Visualization, Feature extraction, Adaptive optics,
Optical imaging, Optical signal processing, Estimation,
region matching
See also Raft: Recurrent All-pairs Field Transforms for Optical Flow.
BibRef
Jahedi, A.[Azin],
Luz, M.[Maximilian],
Rivinius, M.[Marc],
Mehl, L.[Lukas],
Bruhn, A.[Andrés],
MS-RAFT+: High Resolution Multi-Scale RAFT,
IJCV(132), No. 5, May 2024, pp. 1835-1856.
Springer DOI
2405
Recurrent All-Pairs Field Transforms.
BibRef
Jiang, H.[Huaizu],
Learned-Miller, E.[Erik],
DCVNet: Dilated Cost Volume Networks for Fast Optical Flow,
WACV23(5139-5146)
IEEE DOI
2302
Solid modeling, Interpolation, Costs, Computational modeling,
Graphics processing units, Estimation,
Algorithms: Low-level and physics-based vision
BibRef
Zhao, S.Y.[Shi-Yu],
Zhao, L.[Long],
Zhang, Z.X.[Zhi-Xing],
Zhou, E.[Enyu],
Metaxas, D.N.[Dimitris N.],
Global Matching with Overlapping Attention for Optical Flow
Estimation,
CVPR22(17571-17580)
IEEE DOI
2210
Costs, Estimation, Benchmark testing, Feature extraction,
Pattern recognition, Task analysis, Low-level vision,
Scene analysis and understanding
BibRef
Buemi, A.[Antonio],
Spampinato, G.[Giuseppe],
Bruna, A.[Arcangelo],
d'Alto, V.[Viviana],
Densification of Sparse Optical Flow Using Edges Information,
CIAP22(III:254-264).
Springer DOI
2205
BibRef
Xu, H.F.[Hao-Fei],
Yang, J.L.[Jiao-Long],
Cai, J.F.[Jian-Fei],
Zhang, J.[Juyong],
Tong, X.[Xin],
High-Resolution Optical Flow from 1D Attention and Correlation,
ICCV21(10478-10487)
IEEE DOI
2203
Correlation, Image resolution, Costs, Image coding, Estimation,
Search problems, Motion and tracking, Video analysis and understanding
BibRef
Teed, Z.[Zachary],
Deng, J.[Jia],
Raft: Recurrent All-pairs Field Transforms for Optical Flow,
ECCV20(II:402-419).
Springer DOI
2011
Award, ECCV.
See also BRAFT: Recurrent All-Pairs Field Transforms for Optical Flow Based on Correlation Blocks.
BibRef
Teed, Z.[Zachary],
Deng, J.[Jia],
Tangent Space Backpropagation for 3D Transformation Groups,
CVPR21(10333-10342)
IEEE DOI
2111
Backpropagation, Manifolds,
Libraries, Task analysis
BibRef
Zhao, S.Y.[Sheng-Yu],
Sheng, Y.L.[Yi-Lun],
Dong, Y.[Yue],
Chang, E.I.C.[Eric I-Chao],
Xu, Y.[Yan],
MaskFlownet:
Asymmetric Feature Matching With Learnable Occlusion Mask,
CVPR20(6277-6286)
IEEE DOI
2008
Feature extraction, Estimation, Optical imaging, Convolution,
Biomedical optical imaging, Correlation, Laboratories
BibRef
Truong, P.,
Danelljan, M.,
Timofte, R.,
GLU-Net: Global-Local Universal Network for Dense Flow and
Correspondences,
CVPR20(6257-6267)
IEEE DOI
2008
Correlation, Semantics, Optical imaging, Image resolution,
Computer architecture, Task analysis, Geometrical optics
BibRef
Yu, S.,
Park, B.,
Park, J.,
Jeong, J.,
Joint Learning of Blind Video Denoising and Optical Flow Estimation,
NTIRE20(2099-2108)
IEEE DOI
2008
Videos, Noise reduction, Optical flow, Noise measurement, Estimation,
Training
BibRef
Ranjan, A.,
Janai, J.,
Geiger, A.,
Black, M.,
Attacking Optical Flow,
ICCV19(2404-2413)
IEEE DOI
2004
data visualisation, image classification, image denoising,
image sequences, learning (artificial intelligence),
Biomedical optical imaging
BibRef
Min, J.,
Lee, J.,
Ponce, J.,
Cho, M.,
Hyperpixel Flow:
Semantic Correspondence With Multi-Layer Neural Features,
ICCV19(3394-3403)
IEEE DOI
2004
convolutional neural nets, image classification, image matching,
image representation, real-time matching algorithm, SPair-71k,
Real-time systems
BibRef
Yokozuka, M.[Masashi],
Oishi, S.[Shuji],
Thompson, S.[Simon],
Banno, A.[Atsuhiko],
VITAMIN-E:
VIsual Tracking and MappINg With Extremely Dense Feature Points,
CVPR19(9633-9642).
IEEE DOI
2002
BibRef
Liu, P.P.[Peng-Peng],
King, I.[Irwin],
Lyu, M.R.[Michael R.],
Xu, J.[Jia],
Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and
Stereo Matching,
CVPR20(6647-6656)
IEEE DOI
2008
BibRef
Earlier: A1, A3, A2, A4:
SelFlow: Self-Supervised Learning of Optical Flow,
CVPR19(4566-4575).
IEEE DOI
2002
Optical imaging, Optical variables control, Geometrical optics,
Estimation, Adaptive optics, Cameras
BibRef
Phan, T.,
Trinh, D.,
Lamarque, D.,
Wolf, D.,
Daul, C.,
Dense Optical Flow for the Reconstruction of Weakly Textured and
Structured Surfaces: Application to Endoscopy,
ICIP19(310-314)
IEEE DOI
1910
3D mosaicing, structure from motion, dense optical flow, endoscopy,
monocular 3D reconstruction.
BibRef
Chin, W.,
Jhang, Z.,
Chen, H.,
Ito, K.,
Learning Dense Correspondences for Video Objects,
ICIP19(1297-1301)
IEEE DOI
1910
dense correspondence, visual descriptor, optical flow,
feature map aggregation, visual descriptor
BibRef
Melekhov, I.[Iaroslav],
Tiulpin, A.,
Sattler, T.,
Pollefeys, M.,
Rahtu, E.,
Kannala, J.H.[Ju-Ho],
DGC-Net: Dense Geometric Correspondence Network,
WACV19(1034-1042)
IEEE DOI
1904
cameras, convolutional neural nets, image sequences,
pose estimation, optical flow estimation task, ConvNets,
Cameras
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
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
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.B.[Antonio B.],
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.B.[Antonio B.],
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.B.[Antonio B.],
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 .