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Optical flow; Bootstrap; Confidence measure; Motion estimation;
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Earlier:
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0701
Scale space; Optical flow; Segmentation; Filtering
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0701
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Motion estimation; Optical flow; Motion discontinuities; Fourier transform;
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Alba, A.[Alfonso],
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1106
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Earlier:
Optic Flow Using Multi-scale Anchor Points,
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Springer DOI
0909
Image processing; Optic flow; Multi-scale; Scale-space; Toppoints;
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Janssen, B.J.,
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1204
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A new combination of local and global constraints for optical flow
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IVCNZ08(1-6).
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0811
BibRef
Niu, Y.[Yan],
Dick, A.,
Brooks, M.J.,
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PAMI(35), No. 5, May 2013, pp. 1107-1120.
IEEE DOI
1304
Multi-technique, not multi-level.
per-pixel confidence for optical flow vectors for different algorithms.
Thus can choose different algorithms for different areas.
BibRef
Héas, P.[Patrick],
Herzet, C.[Cédric],
Mémin, E.[Etienne],
Heitz, D.[Dominique],
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1305
BibRef
Earlier: A1, A3, A4, A5, Only:
Bayesian selection of scaling laws for motion modeling in images,
ICCV09(971-978).
IEEE DOI
0909
multiscale regularization for optical flow. Turbulent motion.
BibRef
Sánchez Pérez, J.[Javier],
Meinhardt-Llopis, E.[Enric],
Facciolo, G.[Gabriele],
TV-L1 Optical Flow Estimation,
IPOL(2012), No. 2012, pp. xx-yy.
DOI Link
1309
Code, Optical Flow.
See also Algorithm for Total Variation Minimization and Applications, An.
See also Nonlinear total variation based noise removal algorithms.
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Pérez, J.S.[Javier Sánchez],
López, N.M.[Nelson Monzón],
Salgado de la Nuez, A.[Agustín],
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IPOL(2013), No. 2013, pp. 242-261.
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1311
Code, Optical Flow.
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Garcia-Dopico, A.[Antonio],
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Nieto, M.[Manuel],
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1404
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Garcia-Dopico, A.[Antonio],
Pedraza, J.L.[Jose Luis],
Nieto, M.[Manuel],
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Rodriguez, S.[Santiago],
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Zille, P.,
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Shao, L.[Liang],
Chen, X.[Xu],
Observation Model Based on Scale Interactions for Optical Flow
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IP(23), No. 8, August 2014, pp. 3281-3293.
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1408
image resolution
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Jara-Wilde, J.[Jorge],
Cerda, M.[Mauricio],
Delpiano, J.[José],
Härtel, S.[Steffen],
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IPOL(5), 2015, pp. 139-158.
DOI Link
1508
Code, Optical Flow.
See also Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods.
See also Variational Optical Flow Computation in Real Time.
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Pereira, D.R.[Danillo Roberto],
Delpiano, J.[Jose],
Papa, J.P.[João Paulo],
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1508
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Gibson, J.[Joel],
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WWW Link.
1602
BibRef
Earlier:
Sparse Regularization of TV-L1 Optical Flow,
ICISP14(460-467).
Springer DOI
1406
BibRef
Zhang, C.X.[Cong-Xuan],
Chen, Z.[Zhen],
Wang, M.R.[Ming-Run],
Li, M.[Ming],
Jiang, S.F.[Shao-Feng],
Robust Non-Local TV-L^1 Optical Flow Estimation With Occlusion
Detection,
IP(26), No. 8, August 2017, pp. 4055-4067.
IEEE DOI
1707
image filtering, image motion analysis, image sequences,
iterative methods, median filters, Charbonnier function,
Middlebury database test sequence, brightness constancy,
coarse-to-fine computing strategy, displacement motion,
robust nonlocal TV-L1 optical flow estimation, triangulation,
BibRef
Zhang, C.X.[Cong-Xuan],
Feng, C.[Cheng],
Chen, Z.[Zhen],
Hu, W.M.[Wei-Ming],
Li, M.[Ming],
Parallel multiscale context-based edge-preserving optical flow
estimation with occlusion detection,
SP:IC(101), 2022, pp. 116560.
Elsevier DOI
2201
Optical flow, Occlusion detection, Edge-preserving,
Parallel multiscale context, Convolutional neural network
BibRef
Hu, P.,
Wang, G.,
Tan, Y.,
Recurrent Spatial Pyramid CNN for Optical Flow Estimation,
MultMed(20), No. 10, October 2018, pp. 2814-2823.
IEEE DOI
1810
feedforward neural nets, image sequences,
optical flow estimation, spatial scale,
coarse-to-fine refinement
BibRef
Mühlhausen, M.,
Wöhler, L.,
Albuquerque, G.,
Magnor, M.,
Iterative Optical Flow Refinement for High Resolution Images,
ICIP19(1282-1286)
IEEE DOI
1910
optical flow, refinement, high resolution, panorama images
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Sun, D.Q.[De-Qing],
Yang, X.D.[Xiao-Dong],
Liu, M.Y.[Ming-Yu],
Kautz, J.[Jan],
Models Matter, So Does Training: An Empirical Study of CNNs for
Optical Flow Estimation,
PAMI(42), No. 6, June 2020, pp. 1408-1423.
IEEE DOI
2005
BibRef
Earlier:
PWC-Net:
CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume,
CVPR18(8934-8943)
IEEE DOI
1812
Optical imaging, Training, Adaptive optics, Estimation,
Optical signal processing, Network architecture, Protocols,
and convolutional neural network (CNN).
Optical imaging, Benchmark testing,
Computational modeling, Feature extraction.
BibRef
Mai, T.K.[Tan Khoa],
Gouiffès, M.[Michèle],
Bouchafa, S.[Samia],
Optical flow refinement using iterative propagation under colour,
proximity and flow reliability constraints,
IET-IPR(14), No. 8, 19 June 2020, pp. 1509-1519.
DOI Link
2005
BibRef
Raad, L.[Lara],
Oliver, M.[Maria],
Ballester, C.[Coloma],
Haro, G.[Gloria],
Meinhardt, E.[Enric],
On Anisotropic Optical Flow Inpainting Algorithms,
IPOL(10), 2020, pp. 78-104.
DOI Link
2007
Code, Optical Flow.
See also Motion Inpainting by an Image-Based Geodesic AMLE Method.
BibRef
Zhai, M.,
Xiang, X.,
Lv, N.,
Kong, X.,
El Saddik, A.[Abdulmotaleb],
An Object Context Integrated Network for Joint Learning of Depth and
Optical Flow,
IP(29), 2020, pp. 7807-7818.
IEEE DOI
2007
Joint learning, deep neural network, depth estimation,
optical flow estimation, object context
BibRef
Zhai, M.,
Xiang, X.,
Zhang, R.,
Lv, N.,
El Saddik, A.[Abdulmotaleb],
Optical Flow Estimation Using Dual Self-Attention Pyramid Networks,
CirSysVideo(30), No. 10, October 2020, pp. 3663-3674.
IEEE DOI
2010
Optical imaging, Estimation, Task analysis, Optical fiber networks,
Adaptive optics, Computational modeling, Adaptation models,
pyramid network
BibRef
Xiang, X.Z.[Xue-Zhi],
Abdein, R.[Rokia],
Lv, N.[Ning],
El Saddik, A.[Abdulmotaleb],
InvFlow: Involution and multi-scale interaction for unsupervised
learning of optical flow,
PR(145), 2024, pp. 109918.
Elsevier DOI
2311
Unsupervised optical flow estimation, Involution,
Feature interaction, Self-attention, Deformable convolution
BibRef
Xiang, X.,
Zhai, M.,
Zhang, R.,
Lv, N.,
El Saddik, A.[Abdulmotaleb],
Optical Flow Estimation Using Spatial-Channel Combinational
Attention-Based Pyramid Networks,
ICIP19(1272-1276)
IEEE DOI
1910
Optical flow estimation, channel attention, spatial attention,
spatial pyramid network, deep learning
BibRef
Ayyoubzadeh, S.M.[Seyed Mehdi],
Liu, W.T.[Wen-Tao],
Kezele, I.[Irina],
Yu, Y.H.[Yuan-Hao],
Wu, X.L.[Xiao-Lin],
Wang, Y.[Yang],
Jin, T.[Tang],
Test-Time Adaptation for Optical Flow Estimation Using Motion Vectors,
IP(32), 2023, pp. 4977-4988.
IEEE DOI
2310
BibRef
Zhu, Z.[Zifan],
An, Q.[Qing],
Huang, C.[Chen],
Huang, Z.H.[Zheng-Hua],
Huang, L.[Likun],
Fang, H.[Hao],
PDTE: Pyramidal deep Taylor expansion for optical flow estimation,
PRL(180), 2024, pp. 107-112.
Elsevier DOI
2404
Optical flow estimation, Pyramidal deep taylor expansion (PDTE),
Shape preservation, End-point-error (EPE)
BibRef
Jahedi, A.[Azin],
Luz, M.[Maximilian],
Rivinius, M.[Marc],
Bruhn, A.[Andrés],
CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine
Context-Guided Motion Reasoning,
WACV24(6885-6894)
IEEE DOI Code:
WWW Link.
2404
Codes, Computational modeling, Neural networks, Estimation,
Feature extraction, Transformers, Algorithms
BibRef
Shi, X.Y.[Xiao-Yu],
Huang, Z.Y.[Zhao-Yang],
Bian, W.K.[Wei-Kang],
Li, D.[Dasong],
Zhang, M.Y.[Man-Yuan],
Cheung, K.C.[Ka Chun],
See, S.[Simon],
Qin, H.W.[Hong-Wei],
Dai, J.F.[Ji-Feng],
Li, H.S.[Hong-Sheng],
VideoFlow: Exploiting Temporal Cues for Multi-frame Optical Flow
Estimation,
ICCV23(12435-12446)
IEEE DOI Code:
WWW Link.
2401
BibRef
Jeong, J.[Jisoo],
Cai, H.[Hong],
Garrepalli, R.[Risheek],
Porikli, F.M.[Fatih M.],
DistractFlow: Improving Optical Flow Estimation via Realistic
Distractions and Pseudo-Labeling,
CVPR23(13691-13700)
IEEE DOI
2309
BibRef
Jahedi, A.[Azin],
Mehl, L.[Lukas],
Rivinius, M.[Marc],
Bruhn, A.[Andrés],
Multi-Scale Raft: Combining Hierarchical Concepts for Learning-Based
Optical Flow Estimation,
ICIP22(1236-1240)
IEEE DOI
2211
Optical losses, Integrated optics, Costs, Neural networks,
Estimation, Optical fiber networks, Optical flow, Neural networks,
Multi-scale loss
BibRef
Long, L.[Libo],
Lang, J.[Jochen],
Detail Preserving Residual Feature Pyramid Modules for Optical Flow,
WACV22(3980-3988)
IEEE DOI
2202
Training, Schedules, Visualization, Estimation,
Computer architecture, Maintenance engineering, Motion Processing
BibRef
Hofinger, M.[Markus],
Bulò, S.R.[Samuel Rota],
Porzi, L.[Lorenzo],
Knapitsch, A.[Arno],
Pock, T.[Thomas],
Kontschieder, P.[Peter],
Improving Optical Flow on a Pyramid Level,
ECCV20(XXVIII:770-786).
Springer DOI
2011
BibRef
Sun, Z.F.[Ze-Feng],
Wang, H.[Hanli],
Yi, Y.[Yun],
Li, Q.Y.[Qin-Yu],
Structural Pyramid Network for Cascaded Optical Flow Estimation,
MMMod20(I:455-467).
Springer DOI
2003
BibRef
Salehifar, M.[Mehdi],
He, Y.[Yuwen],
Zhang, K.[Kai],
Zhang, L.[Li],
High-Precision Motion Vector Refinement for Bi-Directional Optical
Flow,
ICIP23(1445-1449)
IEEE DOI
2312
BibRef
Liu, H.B.[Hong-Bin],
Zhang, L.[Li],
Zhang, K.[Kai],
Chuang, H.C.[Hsiao Chiang],
Wang, Y.[Yue],
Xu, J.Z.[Ji-Zheng],
Two-Pass Bi-Directional Optical Flow Via Motion Vector Refinement,
ICIP19(1208-1211)
IEEE DOI
1910
bi-directional optical flow, motion vector refinement,
motion compensation, versatile video coding
BibRef
Stephenson, F.,
Breckon, T.P.,
Katramados, I.,
Degraf-Flow: Extending Degraf Features for Accurate and Efficient
Sparse-To-Dense Optical Flow Estimation,
ICIP19(1277-1281)
IEEE DOI
1910
optical flow, Dense Gradient Based Features, DeGraF,
automotive vision, feature points
BibRef
Zhang, C.,
Chen, T.,
Liu, H.,
Shen, Q.,
Ma, Z.,
Looking-Ahead: Neural Future Video Frame Prediction,
ICIP19(1975-1979)
IEEE DOI
1910
Optical flow, inpainting, deep neural networks, video frame prediction
BibRef
Maczyta, L.,
Bouthemy, P.,
Meur, O.L.,
Unsupervised Motion Saliency Map Estimation Based On Optical Flow
Inpainting,
ICIP19(4469-4473)
IEEE DOI
1910
Motion saliency, optical flow inpainting, video analysis
BibRef
Ren, Z.[Zhile],
Gallo, O.[Orazio],
Sun, D.[Deqing],
Yang, M.H.[Ming-Hsuan],
Sudderth, E.B.[Erik B.],
Kautz, J.[Jan],
A Fusion Approach for Multi-Frame Optical Flow Estimation,
WACV19(2077-2086)
IEEE DOI
1904
BibRef
Earlier:
A Simple and Effective Fusion Approach for Multi-frame Optical Flow
Estimation,
OpticalFlow18(VI:706-710).
Springer DOI
1905
image fusion, image sequences,
multiframe optical flow estimation,
Brightness
BibRef
Dai, J.,
Huang, S.,
Nguyen, T.,
Pyramid Structured Optical Flow Learning with Motion Cues,
ICIP18(3338-3342)
IEEE DOI
1809
Estimation, Training, Adaptive optics, Optical imaging,
Optical network units, Benchmark testing, Motion cue
BibRef
Vaquero, V.,
Ros, G.,
Moreno-Noguer, F.,
Lopez, A.M.,
Sanfeliu, A.,
Joint coarse-and-fine reasoning for deep optical flow,
ICIP17(2558-2562)
IEEE DOI
1803
Adaptive optics, Cognition, Estimation, Optical imaging, Semantics,
Task analysis, Training, classification, coarse-and-fine,
regression
BibRef
Ranjan, A.[Anurag],
Black, M.J.[Michael J.],
Optical Flow Estimation Using a Spatial Pyramid Network,
CVPR17(2720-2729)
IEEE DOI
1711
Adaptive optics, Biomedical optical imaging,
Computational modeling, Estimation, Neural networks,
Optical computing, Optical, imaging
BibRef
Choi, J.W.[Jong-Won],
Kim, H.W.[Hyeong-Woo],
Oh, T.H.[Tae-Hyun],
Kweon, I.S.[In So],
Balanced optical flow refinement by bidirectional constraint,
ICIP14(5477-5481)
IEEE DOI
1502
Adaptive optics
BibRef
Stoll, M.[Michael],
Volz, S.[Sebastian],
Bruhn, A.[Andres],
Joint trilateral filtering for multiframe optical flow,
ICIP13(3845-3849)
IEEE DOI
1402
Cross Bilateral Upsampling
BibRef
Singh, A.[Abhishek],
Ahuja, N.[Narendra],
Exploiting ramp structures for improving optical flow estimation,
ICPR12(2504-2507).
WWW Link.
1302
BibRef
Tepper, M.[Mariano],
Sapiro, G.[Guillermo],
Decoupled coarse-to-fine matching and nonlinear regularization for
efficient motion estimation,
ICIP12(1517-1520).
IEEE DOI
1302
BibRef
Mohamed, M.A.[Mahmoud A.],
Mertsching, B.[Baerbel],
TV-L1 Optical Flow Estimation with Image Details Recovering Based on
Modified Census Transform,
ISVC12(I: 482-491).
Springer DOI
1209
BibRef
Radow, G.[Georg],
Breuß, M.[Michael],
Variational Optical Flow: Warping and Interpolation Revisited,
CAIP19(I:409-420).
Springer DOI
1909
BibRef
Hoeltgen, L.[Laurent],
Setzer, S.[Simon],
Breuß, M.[Michael],
Intermediate Flow Field Filtering in Energy Based Optic Flow
Computations,
EMMCVPR11(315-328).
Springer DOI
1107
BibRef
Wartak, S.[Szymon],
Bors, A.G.[Adrian G.],
Optical Flow Estimation Using Diffusion Distances,
ICPR10(189-192).
IEEE DOI
1008
BibRef
Lei, C.[Cheng],
Yang, Y.H.[Yee-Hong],
Optical flow estimation on coarse-to-fine region-trees using discrete
optimization,
ICCV09(1562-1569).
IEEE DOI
0909
BibRef
Heindlmaier, M.[Michael],
Yu, L.[Lang],
Diepold, K.[Klaus],
The impact of nonlinear filtering and confidence information on optical
flow estimation in a Lucas and Kanade framework,
ICIP09(1593-1596).
IEEE DOI
0911
BibRef
Ring, D.,
Pitie, F.,
Feature-Assisted Sparse to Dense Motion Estimation Using Geodesic
Distances,
IMVIP09(7-12).
IEEE DOI
0909
BibRef
Chen, D.[Daniel],
Denman, S.[Simon],
Fookes, C.[Clinton],
Sridharan, S.[Sridha],
Accurate Silhouettes for Surveillance:
Improved Motion Segmentation Using Graph Cuts,
DICTA10(369-374).
IEEE DOI
1012
BibRef
Denman, S.[Simon],
Fookes, C.[Clinton],
Sridharan, S.[Sridha],
Improved Simultaneous Computation of Motion Detection and Optical Flow
for Object Tracking,
DICTA09(175-182).
IEEE DOI
0912
See also Multi-Modal Object Tracking using Dynamic Performance Metrics.
BibRef
Wilson, R.G.[Roland G.],
Bowen, A.[Adam],
Mullins, A.[Andrew],
Rajpoot, N.[Nasir],
Estimation of a 3D motion field from a multi-camera array using a
multiresolution Gaussian mixture model,
M2SFA208(xx-yy).
0810
BibRef
Wilson, R.,
Calway, A.D.,
Multiresolution Gaussian Mixture Models for Visual Motion Estimation,
ICIP01(II: 921-924).
IEEE DOI
0108
BibRef
Wilson, R.[Ronald],
MGMM: Multiresolution Gaussian Mixture Models for Computer Vision,
ICPR00(Vol I: 212-215).
IEEE DOI
0009
BibRef
Ho, H.T.[Huy Tho],
Goecke, R.[Roland],
Optical flow estimation using Fourier Mellin Transform,
CVPR08(1-8).
IEEE DOI
0806
BibRef
Koppel, D.[Dan],
Tsai, C.M.[Chang-Ming],
Wang, Y.F.[Yuan-Fang],
Regularizing optical-flow computation using tensor theory and complex
analysis,
Tensor08(1-6).
IEEE DOI
0806
BibRef
Li, J.[Jian],
Benton, C.P.[Christopher P.],
Nikolov, S.G.[Stavri G.],
Scott-Samuel, N.E.[Nicholas E.],
Adaptive Multiscale Optical Flow Estimation,
ICIP07(II: 509-512).
IEEE DOI
0709
BibRef
Alvino, C.,
Tannenbaum, A.,
Yezzi, A.J.,
Curry, C.,
Multigrid Computation of Rotationally Invariant Non-Linear Optical Flow,
ICIP05(III: 1296-1299).
IEEE DOI
0512
BibRef
Yang, L.X.[Li-Xin],
Sahli, H.,
A Nonlinear Multigrid Diffusion Model for Efficient Dense Optical Flow
estimation,
ICIP05(I: 149-152).
IEEE DOI
0512
BibRef
Li, M.,
Kambhamettu, C.,
Stone, M.,
A General Framework for 2D Multiframe and 3D Surface-to-surface Motion
Estimation,
BMVC04(xx-yy).
HTML Version.
0508
BibRef
Teng, C.H.[Chin-Hung],
Lai, S.H.[Shang-Hong],
Chen, Y.S.[Yung-Sheng],
Hsu, W.H.[Wen-Hsing],
An accurate and adaptive optical flow estimation algorithm,
ICIP04(III: 1839-1842).
IEEE DOI
0505
BibRef
Adachi, E.,
Horiguchi, S.,
Multi-resolutional optical flow estimation with local optimization,
ICIP02(II: 257-260).
IEEE DOI
0210
BibRef
Wang, H.Y.[Hai-Yan],
Ma, K.K.[Kai-Kuang],
Automatic video object segmentation via 3D structure tensor,
ICIP03(I: 153-156).
IEEE DOI
0312
BibRef
Earlier:
Accurate optical flow estimation using adaptive scale-space and 3d
structure tensor,
ICIP02(II: 301-304).
IEEE DOI
0210
BibRef
Steenstrup Pedersen, K.,
Nielsen, M.,
Computing optic flow by scale-space integration of normal flow,
ScaleSpace01(xx-yy).
0106
BibRef
Maas, R.[Robert],
ter Haar Romeny, B.M.[Bart M.],
Viergever, M.A.[Max A.],
A Multiscale Taylor Series Approaches to Optic Flow and Stereo:
A Generalization of Optic Flow Under the Aperture,
ScaleSpace99(519-524).
BibRef
9900
George, M.,
Tjahjadi, T.,
Multiresolution Optical Flow Estimation using Adaptive Shifting,
ICIP99(III:717-721).
IEEE Abstract.
BibRef
9900
Mendelsohn, J.[Jeffrey],
Simoncelli, E.[Eero],
Bajcsy, R.[Ruzena],
Discrete-time rigidity-constrained optical flow,
CAIP97(255-262).
Springer DOI
9709
HTML Version. And full paper:
PS File. Structure from optic flow.
BibRef
Johannesson, M., and
Gokstorp, M.,
Video-rate Pyramid Optical Flow Computations on the Linear
SIMD Array IVP,
CAMP95(xx).
BibRef
9500
Gokstorp, M.,
Danielsson, P.E.,
Velocity tuned generalized Sobel operators for multiresolution
computation of optical flow,
ICIP94(II: 765-769).
IEEE DOI
9411
BibRef
Colombo, C.,
del Bimbo, A.,
Santini, S.,
A Multilayer Massively Parallel Architecture for
Optical Flow Computation,
ICPR92(IV:209-213).
IEEE DOI
BibRef
9200
Bernard, C.,
Discrete Wavelet Analysis:
A New Framework for Fast Optic Flow Computation,
ECCV98(II: 354).
Springer DOI
BibRef
9800
Ríos, H.[Homero],
Computing image flow using a coarse-to-fine strategy for spatiotemporal
filters,
CAIP93(355-362).
Springer DOI
9309
BibRef
Kories, R.,
Rehfeld, N.,
Zimmermann, G.,
Towards Autonomous Convoy Driving: Recognizing the Starting Vehicle
in Front,
ICPR88(I: 531-535).
IEEE DOI
BibRef
8800
Kories, R., and
Zimmermann, G.,
A Versatile Method for the Estimation of Displacement
Vector Fields from Image Sequences,
Motion86(101-106).
See also Investigation of Multigrid Algorithms for the Estimation of Optical Flow Fields in Image Sequences.
BibRef
8600
Kories, R.,
Hecker, G.,
Zimmermann, G.,
On the Precision of a Feature Based Displacement Measurement,
ICPR86(1193-1196).
BibRef
8600
Zimmermann, G.,
Kories, R.,
Image Sequence Processing as an Aid for Three-Dimensional Display,
ICPR86(821-824).
BibRef
8600
Kories, R.,
Zimmermann, G.,
Motion Detection in Image Sequences: An Evaluation of Feature Detectors,
ICPR84(778-780).
BibRef
8400
And:
A Class of Stable Feature Extractors for Time-Varying Imagery,
ICPR84(919).
BibRef
Korn, A.F.[Axel F.],
Kories, R.,
Motion Analysis in Natural Scenes Picked up by a Moving Optical Sensor,
ICPR80(1251-1254).
BibRef
8000
Kories, R.,
Determination of Displacement Vector Fields for General Camera Motions,
PRIP81(115-117).
BibRef
8100
Bergeron, C., and
Dubois, E.,
Parametric Block Estimation of Motion and Application to Temporal
Interpolation of Video Sequences,
ICPR90(II: 140-146).
IEEE DOI
BibRef
9000
Glazer, F.,
Hierarchial Gradient-Based Motion Detection,
DARPA87(733-748).
BibRef
8700
Earlier:
Computing Optic Flow,
IJCAI81(644-647).
Gradient-based approaches only work with small motions, but is
extended by using a hierarchical approach. This seems in
keeping with the UMass approach.
BibRef
Bandyopadhyay, A.,
A Multiple Channel Model for Perception of Optical Flow,
CVWS84(78-82).
BibRef
8400
Burt, P.J.,
Yen, C.,
Xy, X.,
Local Correlation Measures for Motion Analysis: A Comparative Study,
PRIP82(269-274).
BibRef
8200
Burt, P.J.,
Yen, C.,
Xy, X.,
Multi-Resolution Flow - Through Motion Analysis,
CVPR83(246-252).
BibRef
8300
Chapter on Optical Flow Field Computations and Use continues in
Parallel Optic Flow Computation, Efficient Computation .