Chen, T.W.[Tsu Wang], and
Lin, W.C.[Wei Chung],
Chen, C.T.,
Artificial Neural Networks for 3-D Motion Analysis I: Rigid Motion,
TNN(6), No. 6, November 1995, pp. 1386-1393.
BibRef
9511
And:
Artificial Neural Networks for 3-D Motion Analysis II: Nonrigid Motion,
TNN(6), No. 6, November 1995, pp. 1394-1401.
See also Neural-Network Approach to CSG-Based 3-D Object Recognition, A.
BibRef
Wang, R.Y.,
A Network Model for the Optic Flow Computation of the MST Neurons,
NeurNet(9), No. 3, April 1996, pp. 411-426.
9605
BibRef
Gray, W.S.,
Nabet, B.,
Volterra Series Analysis and Synthesis of a Neural Network for Velocity
Estimation,
SMC-B(29), No. 2, April 1999, pp. 190.
BibRef
9904
Mayer, N.[Nikolaus],
Ilg, E.[Eddy],
Fischer, P.[Philipp],
Hazirbas, C.[Caner],
Cremers, D.[Daniel],
Dosovitskiy, A.[Alexey],
Brox, T.[Thomas],
What Makes Good Synthetic Training Data for Learning Disparity and
Optical Flow Estimation?,
IJCV(126), No. 9, September 2018, pp. 942-960.
Springer DOI
1809
BibRef
Dosovitskiy, A.,
Fischery, P.,
Ilg, E.,
Hausser, P.,
Hazirbas, C.,
Golkov, V.,
van de Smagt, P.,
Cremers, D.,
Brox, T.,
FlowNet: Learning Optical Flow with Convolutional Networks,
ICCV15(2758-2766)
IEEE DOI
1602
Computer architecture
BibRef
Ilg, E.[Eddy],
Saikia, T.[Tonmoy],
Keuper, M.[Margret],
Brox, T.[Thomas],
Occlusions, Motion and Depth Boundaries with a Generic Network for
Disparity, Optical Flow or Scene Flow Estimation,
ECCV18(XII: 626-643).
Springer DOI
1810
BibRef
Schrodi, S.[Simon],
Saikia, T.[Tonmoy],
Brox, T.[Thomas],
Towards Understanding Adversarial Robustness of Optical Flow Networks,
CVPR22(8906-8914)
IEEE DOI
2210
Codes, Computational modeling, Estimation, Network architecture,
Apertures, Robustness, Motion and tracking, Adversarial attack and defense
BibRef
Ilg, E.[Eddy],
Mayer, N.[Nikolaus],
Saikia, T.[Tonmoy],
Keuper, M.[Margret],
Dosovitskiy, A.[Alexey],
Brox, T.[Thomas],
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks,
CVPR17(1647-1655)
IEEE DOI
1711
Adaptive optics, Computer architecture, Estimation,
Optical imaging, Schedules, Stacking, Training
BibRef
Wannenwetsch, A.S.,
Keuper, M.,
Roth, S.,
ProbFlow: Joint Optical Flow and Uncertainty Estimation,
ICCV17(1182-1191)
IEEE DOI
1802
Bayes methods, entropy, image sequences, motion estimation,
energy minimization approach, energy-based formulations,
Uncertainty
BibRef
Makansi, O.[Osama],
Ilg, E.[Eddy],
Cicek, O.[Ozgun],
Brox, T.[Thomas],
Overcoming Limitations of Mixture Density Networks: A Sampling and
Fitting Framework for Multimodal Future Prediction,
CVPR19(7137-7146).
IEEE DOI
2002
BibRef
Ilg, E.[Eddy],
Çiçek, Ö.[Özgün],
Galesso, S.[Silvio],
Klein, A.[Aaron],
Makansi, O.[Osama],
Hutter, F.[Frank],
Brox, T.[Thomas],
Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow,
ECCV18(VII: 677-693).
Springer DOI
1810
BibRef
Paredes-Vallés, F.[Federico],
Scheper, K.Y.W.[Kirk Y. W.],
de Croon, G.C.H.E.[Guido C.H.E.],
Unsupervised Learning of a Hierarchical Spiking Neural Network for
Optical Flow Estimation: From Events to Global Motion Perception,
PAMI(42), No. 8, August 2020, pp. 2051-2064.
IEEE DOI
2007
Neurons, Visualization, Biomedical optical imaging,
Optical sensors, Biological system modeling,
unsupervised learning
BibRef
Hui, T.W.[Tak-Wai],
Tang, X.[Xiaoou],
Loy, C.C.[Chen Change],
A Lightweight Optical Flow CNN: Revisiting Data Fidelity and
Regularization,
PAMI(43), No. 8, August 2021, pp. 2555-2569.
IEEE DOI
2107
BibRef
Earlier:
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical
Flow Estimation,
CVPR18(8981-8989)
IEEE DOI
1812
Optical imaging, Adaptive optics, Estimation, Optical computing,
Convolutional codes, Optical network units,
and warping.
Estimation, Feature extraction, Convolution,
Optical filters, Optical fiber networks
BibRef
Hui, T.W.[Tak-Wai],
Loy, C.C.[Chen Change],
LiteFlownet3: Resolving Correspondence Ambiguity for More Accurate
Optical Flow Estimation,
ECCV20(XX:169-184).
Springer DOI
2011
BibRef
Che, T.T.[Tong-Tong],
Zheng, Y.J.[Yuan-Jie],
Yang, Y.S.[Yun-Shuai],
Hou, S.J.[Su-Juan],
Jia, W.K.[Wei-Kuan],
Yang, J.[Jie],
Gong, C.[Chen],
SDOF-GAN: Symmetric Dense Optical Flow Estimation With Generative
Adversarial Networks,
IP(30), 2021, pp. 6036-6049.
IEEE DOI
2107
Estimation, Optical imaging, Adaptive optics,
Generative adversarial networks, Brightness,
semi-supervised learning
BibRef
Zheng, Y.J.[Yuan-Jie],
Sui, X.D.[Xiao-Dan],
Jiang, Y.[Yanyun],
Che, T.T.[Tong-Tong],
Zhang, S.T.[Shao-Ting],
Yang, J.[Jie],
Li, H.S.[Hong-Sheng],
SymReg-GAN: Symmetric Image Registration With Generative Adversarial
Networks,
PAMI(44), No. 9, September 2022, pp. 5631-5646.
IEEE DOI
2208
Image registration, Generative adversarial networks, Generators,
Estimation, Training, Magnetic resonance imaging, Image resolution,
multimodal image registration
BibRef
Liu, S.C.[Shuai-Cheng],
Luo, K.M.[Kun-Ming],
Ye, N.J.[Nian-Jin],
Wang, C.[Chuan],
Wang, J.[Jue],
Zeng, B.[Bing],
OIFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised
Learning,
IP(30), 2021, pp. 6420-6433.
IEEE DOI
2107
Optical imaging, Feature extraction, Optical variables control,
Estimation, Optical fiber networks, Adaptive optics,
unsupervised learning
BibRef
Liu, S.C.[Shuai-Cheng],
Luo, K.M.[Kun-Ming],
Luo, A.[Ao],
Wang, C.[Chuan],
Meng, F.M.[Fan-Man],
Zeng, B.[Bing],
ASFlow: Unsupervised Optical Flow Learning With Adaptive Pyramid
Sampling,
CirSysVideo(32), No. 7, July 2022, pp. 4282-4295.
IEEE DOI
2207
Optical imaging, Optical variables control, Optical losses,
Feature extraction, Interpolation, Adaptive systems,
pyramid downsampling
BibRef
Luo, K.M.[Kun-Ming],
Wang, C.[Chuan],
Liu, S.C.[Shuai-Cheng],
Fan, H.Q.[Hao-Qiang],
Wang, J.[Jue],
Sun, J.[Jian],
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning,
CVPR21(1045-1054)
IEEE DOI
2111
Optical losses, Interpolation, Estimation,
Benchmark testing, Pattern recognition, Optical flow
BibRef
Zhao, R.[Rui],
Xiong, R.Q.[Rui-Qin],
Ding, Z.[Ziluo],
Fan, X.P.[Xiao-Peng],
Zhang, J.[Jian],
Huang, T.J.[Tie-Jun],
MRDFlow: Unsupervised Optical Flow Estimation Network With
Multi-Scale Recurrent Decoder,
CirSysVideo(32), No. 7, July 2022, pp. 4639-4652.
IEEE DOI
2207
Optical flow, Decoding, Estimation, Optical losses,
Image motion analysis, Correlation, recurrent decoder
BibRef
Liu, P.P.[Peng-Peng],
Lyu, M.R.[Michael R.],
King, I.[Irwin],
Xu, J.[Jia],
Learning by Distillation:
A Self-Supervised Learning Framework for Optical Flow Estimation,
PAMI(44), No. 9, September 2022, pp. 5026-5041.
IEEE DOI
2208
Optical imaging, Predictive models, Optical variables control,
Estimation, Optical computing, Training, Data models, Optical flow,
and stereo matching
BibRef
de Jong, D.B.[David B.],
Paredes-Vallés, F.[Federico],
de Croon, G.C.H.E.[Guido C. H. E.],
How Do Neural Networks Estimate Optical Flow?
A Neuropsychology-Inspired Study,
PAMI(44), No. 11, November 2022, pp. 8290-8305.
IEEE DOI
2210
Optical imaging, Optical fiber networks, Optical sensors,
Optical computing, Estimation, Biomedical optical imaging, neuropsychology
BibRef
Lin, X.H.[Xiu-Hong],
Yang, C.H.[Chen-Hui],
Bian, X.S.[Xue-Sheng],
Liu, W.Q.[Wei-Quan],
Wang, C.[Cheng],
EAGAN: Event-based attention generative adversarial networks for
optical flow and depth estimation,
IET-CV(16), No. 7, 2022, pp. 581-595.
DOI Link
2210
BibRef
Chi, C.[Cheng],
Hao, T.Y.[Tian-Yu],
Wang, Q.J.[Qing-Jie],
Guo, P.[Peng],
Yang, X.[Xin],
Subspace-PnP: A Geometric Constraint Loss for Mutual Assistance of
Depth and Optical Flow Estimation,
IJCV(130), No. 12, December 2022, pp. 3054-3069.
Springer DOI
2211
BibRef
Chi, C.[Cheng],
Wang, Q.J.[Qing-Jie],
Hao, T.Y.[Tian-Yu],
Guo, P.[Peng],
Yang, X.[Xin],
Feature-Level Collaboration: Joint Unsupervised Learning of Optical
Flow, Stereo Depth and Camera Motion,
CVPR21(2463-2473)
IEEE DOI
2111
Optical losses, Motion estimation,
Collaboration, Cameras, Feature extraction, Task analysis
BibRef
Savian, S.[Stefano],
Elahi, M.[Mehdi],
Janes, A.A.[Andrea A.],
Tillo, T.[Tammam],
Benchmarking equivariance for Deep Learning based optical flow
estimators,
SP:IC(111), 2023, pp. 116892.
Elsevier DOI
2301
Optical flow, Deep Learning, Equivariance, Benchmark
BibRef
Kong, L.T.[Ling-Tong],
Yang, J.[Jie],
MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual
Knowledge Distillation,
CirSysVideo(33), No. 2, February 2023, pp. 677-688.
IEEE DOI
2302
Reliability, Knowledge engineering, Costs, Estimation,
Biomedical optical imaging, Task analysis, Optical flow,
real time
BibRef
Xiang, X.Z.[Xue-Zhi],
Abdein, R.[Rokia],
Lv, N.[Ning],
Yang, J.[Jie],
Self-Supervised Learning of Scene Flow with Occlusion Handling
Through Feature Masking,
PR(139), 2023, pp. 109487.
Elsevier DOI
2304
BibRef
Earlier: A2, A1, A3, Only:
Self-Supervised Learning of Optical Flow, Depth, Camera Pose and
Rigidity Segmentation with Occlusion Handling,
ICIP22(6-10)
IEEE DOI
2211
Optical flow, Depth estimation, Camera pose,
Rigidity segmentation, Occlusion handling, Deformable decoder.
Training, Integrated optics, Image motion analysis, Correlation,
Self-supervised learning, Cameras, Optical flow estimation
BibRef
Min, C.[Chaerin],
Kim, T.[Taehyun],
Lim, J.W.[Jong-Woo],
Meta-Learning for Adaptation of Deep Optical Flow Networks,
WACV23(2144-2153)
IEEE DOI
2302
Training, Optical losses, Neural networks, Estimation, Training data,
Reliability, Labeling, and algorithms (including transfer)
BibRef
Schelling, M.[Michael],
Hermosilla, P.[Pedro],
Ropinski, T.[Timo],
Weakly-Supervised Optical Flow Estimation for Time-of-Flight,
WACV23(2134-2143)
IEEE DOI
2302
Training, Optical losses, Optical variables measurement,
Motion compensation, Motion measurement, 3D computer vision
BibRef
Savian, S.[Stefano],
Morerio, P.[Pietro],
del Bue, A.[Alessio],
Janes, A.A.[Andrea A.],
Tillo, T.[Tammam],
Towards Equivariant Optical Flow Estimation with Deep Learning,
WACV23(5077-5086)
IEEE DOI
2302
Training, Measurement, Deep learning, Optical losses, Codes,
Computational modeling
BibRef
Chen, Y.H.[Yong-Hu],
Zhu, D.C.[Dong-Chen],
Shi, W.J.[Wen-Jun],
Zhang, G.H.[Guang-Hui],
Zhang, T.[Tianyu],
Zhang, X.L.[Xiao-Lin],
Li, J.[Jiamao],
MFCFlow: A Motion Feature Compensated Multi-Frame Recurrent Network
for Optical Flow Estimation,
WACV23(5057-5066)
IEEE DOI
2302
Optical filters, Geometry, Matched filters, Correlation,
Computational modeling, Estimation, Coherence,
Low-level and physics-based vision
BibRef
Chong, X.Y.[Xiao-Ya],
Zhou, N.[Niyun],
Li, Q.[Qing],
Leung, H.[Howard],
NoiseFlow: Learning Optical Flow from Low SNR Cryo-EM Movie,
ICPR22(3471-3477)
IEEE DOI
2212
Deep learning, Correlation, Computational modeling, Stacking,
Noise reduction, Estimation, Motion pictures
BibRef
Bhandari, K.[Keshav],
Duan, B.[Bin],
Liu, G.[Gaowen],
Latapie, H.[Hugo],
Zong, Z.L.[Zi-Liang],
Yan, Y.[Yan],
Learning Omnidirectional Flow in 360°
Video via Siamese Representation,
ECCV22(VIII:557-574).
Springer DOI
2211
BibRef
Chang, C.P.[Chih-Peng],
Chen, P.Y.[Peng-Yu],
Ho, Y.H.[Yung-Han],
Peng, W.H.[Wen-Hsiao],
Deep Incremental Optical Flow Coding For Learned Video Compression,
ICIP22(3988-3992)
IEEE DOI
2211
Image coding, Bit rate, Video compression, Motion compensation,
Encoding, Video codecs, Optical flow, Video Coding, Double Warping
BibRef
Im, W.B.[Woo-Bin],
Lee, S.[Sebin],
Yoon, S.E.[Sung-Eui],
Semi-supervised Learning of Optical Flow by Flow Supervisor,
ECCV22(XXXV:302-318).
Springer DOI
2211
BibRef
Li, Y.H.[Yi-Heng],
Barnes, C.[Connelly],
Huang, K.[Kun],
Zhang, F.L.[Fang-Lue],
Deep 360° Optical Flow Estimation Based on Multi-projection Fusion,
ECCV22(XXXV:336-352).
Springer DOI
2211
BibRef
Yuan, S.[Shuai],
Sun, X.[Xian],
Kim, H.[Hannah],
Yu, S.Z.[Shu-Zhi],
Tomasi, C.[Carlo],
Optical Flow Training Under Limited Label Budget via Active Learning,
ECCV22(XXII:410-427).
Springer DOI
2211
BibRef
Huang, Z.Y.[Zhao-Yang],
Shi, X.Y.[Xiao-Yu],
Zhang, C.[Chao],
Wang, Q.[Qiang],
Cheung, K.C.[Ka Chun],
Qin, H.W.[Hong-Wei],
Dai, J.F.[Ji-Feng],
Li, H.S.[Hong-Sheng],
FlowFormer: A Transformer Architecture for Optical Flow,
ECCV22(XVII:668-685).
Springer DOI
2211
BibRef
Le Guen, V.[Vincent],
Rambour, C.[Clément],
Thome, N.[Nicolas],
Complementing Brightness Constancy with Deep Networks for Optical Flow
Prediction,
ECCV22(XXI:121-138).
Springer DOI
2211
BibRef
Sun, D.Q.[De-Qing],
Herrmann, C.[Charles],
Reda, F.[Fitsum],
Rubinstein, M.[Michael],
Fleet, D.J.[David J.],
Freeman, W.T.[William T.],
Disentangling Architecture and Training for Optical Flow,
ECCV22(XXII:165-182).
Springer DOI
2211
BibRef
Xu, H.F.[Hao-Fei],
Zhang, J.[Jing],
Cai, J.F.[Jian-Fei],
Rezatofighi, H.[Hamid],
Tao, D.C.[Da-Cheng],
GMFlow: Learning Optical Flow via Global Matching,
CVPR22(8111-8120)
IEEE DOI
2210
Image motion analysis, Correlation, Costs, Pipelines, Estimation,
Transformers, Motion and tracking, Low-level vision
BibRef
Luo, A.[Ao],
Yang, F.[Fan],
Li, X.[Xin],
Liu, S.C.[Shuai-Cheng],
Learning Optical Flow with Kernel Patch Attention,
CVPR22(8896-8905)
IEEE DOI
2210
Image motion analysis, Motion estimation, Benchmark testing,
Pattern recognition, Kernel, Task analysis, Motion and tracking,
Video analysis and understanding
BibRef
Bai, S.J.[Shao-Jie],
Geng, Z.Y.[Zheng-Yang],
Savani, Y.[Yash],
Kolter, J.Z.[J. Zico],
Deep Equilibrium Optical Flow Estimation,
CVPR22(610-620)
IEEE DOI
2210
Training, Degradation, Image motion analysis,
Computational modeling, Memory management, Estimation, Machine learning
BibRef
Hu, L.W.[Li-Wen],
Zhao, R.[Rui],
Ding, Z.[Ziluo],
Ma, L.[Lei],
Shi, B.X.[Bo-Xin],
Xiong, R.Q.[Rui-Qin],
Huang, T.J.[Tie-Jun],
Optical Flow Estimation for Spiking Camera,
CVPR22(17823-17832)
IEEE DOI
2210
Deep learning, Training, Photography, Tracking, Pipelines, Estimation,
Cameras, Computational photography, Motion and tracking
BibRef
Ammar, A.[Anis],
Chebbah, A.[Amani],
Fredj, H.B.[Hana Ben],
Souani, C.[Chokri],
Comparative Study of latest CNN based Optical Flow Estimation,
ISCV22(1-6)
IEEE DOI
2208
Deep learning, Systematics, Image processing,
Motion estimation, Estimation, Signal processing algorithms,
deep learning
BibRef
Zhang, F.H.[Fei-Hu],
Woodford, O.J.[Oliver J.],
Prisacariu, V.[Victor],
Torr, P.H.S.[Philip H. S.],
Separable Flow: Learning Motion Cost Volumes for Optical Flow
Estimation,
ICCV21(10787-10797)
IEEE DOI
2203
Knowledge engineering, Image motion analysis, Costs, Correlation,
Estimation, Benchmark testing, Motion and tracking,
BibRef
Li, H.P.[Hai-Peng],
Luo, K.M.[Kun-Ming],
Liu, S.C.[Shuai-Cheng],
GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning,
ICCV21(12849-12858)
IEEE DOI
2203
Optical fibers, Measurement, Rain, Codes, Fuses, Brightness,
Motion and tracking, Datasets and evaluation,
Vision + other modalities
BibRef
Jeny, A.A.[Afsana Ahsan],
Islam, M.B.[Md Baharul],
Aydin, T.[Tarkan],
DeepPyNet: A Deep Feature Pyramid Network for Optical Flow Estimation,
IVCNZ21(1-6)
IEEE DOI
2201
Visualization, Image motion analysis, Correlation, Refining,
Neural networks, Estimation, Feature extraction,
Iterative recurrent unit
BibRef
Sun, D.[Deqing],
Vlasic, D.[Daniel],
Herrmann, C.[Charles],
Jampani, V.[Varun],
Krainin, M.[Michael],
Chang, H.[Huiwen],
Zabih, R.[Ramin],
Freeman, W.T.[William T.],
Liu, C.[Ce],
AutoFlow: Learning a Better Training Set for Optical Flow,
CVPR21(10088-10097)
IEEE DOI
2111
Training, Adaptation models, Technological innovation,
Solid modeling, Shape, Training data
BibRef
Poggi, M.[Matteo],
Aleotti, F.[Filippo],
Mattoccia, S.[Stefano],
Sensor-Guided Optical Flow,
ICCV21(7888-7898)
IEEE DOI
2203
Geometry, Image motion analysis, Optical variables measurement,
Prediction algorithms, Sensors, Reliability,
Vision for robotics and autonomous vehicles
BibRef
Aleotti, F.[Filippo],
Poggi, M.[Matteo],
Mattoccia, S.[Stefano],
Learning optical flow from still images,
CVPR21(15196-15206)
IEEE DOI
2111
Training, Computational modeling, Training data,
Estimation, Data models, Pattern recognition
BibRef
Jiao, Y.[Yang],
Shi, G.M.[Guang-Ming],
Tran, T.D.[Trac D.],
Optical Flow Estimation Via Motion Feature Recovery,
ICIP21(2558-2562)
IEEE DOI
2201
Solid modeling, Adaptive learning, Adaptation models, Correlation,
Estimation, Benchmark testing, CNN, Optical Flow, Cost Volume,
Motion Consistency
BibRef
Jiao, Y.[Yang],
Tran, T.D.[Trac D.],
Shi, G.M.[Guang-Ming],
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised
Joint Learning of Optical Flow, Depth, Camera Pose and Motion
Segmentation,
CVPR21(5534-5543)
IEEE DOI
2111
Couplings, Image motion analysis,
Motion segmentation, Estimation, Benchmark testing, Cameras
BibRef
Jiang, S.H.[Shi-Hao],
Lu, Y.[Yao],
Li, H.D.[Hong-Dong],
Hartley, R.I.[Richard I.],
Learning Optical Flow from a Few Matches,
CVPR21(16587-16595)
IEEE DOI
2111
Training, Solid modeling, Correlation, Limiting,
Computational modeling, Memory management, Estimation
BibRef
Artizzu, C.O.[Charles-Olivier],
Zhang, H.Z.[Hao-Zhou],
Allibert, G.[Guillaume],
Demonceaux, C.[Cédric],
OmniFlowNet: a Perspective Neural Network Adaptation for Optical Flow
Estimation in Omnidirectional Images,
ICPR21(2657-2662)
IEEE DOI
2105
Training, Convolution, Image processing, Neural networks, Estimation,
Optical distortion, Distortion
BibRef
Yu, S.J.[Suihan-Jin],
Zhang, Y.M.[You-Min],
Wang, C.[Chen],
Bai, X.[Xiao],
Zhang, L.[Liang],
Hancock, E.R.[Edwin R.],
HMFlow: Hybrid Matching Optical Flow Network for Small and
Fast-Moving Objects,
ICPR21(1197-1204)
IEEE DOI
2105
Image resolution, Estimation, Search problems, Pattern recognition,
Task analysis, Optical flow
BibRef
Li, Z.Y.[Zhuo-Yan],
Shen, J.W.[Jia-Wei],
Liu, R.[Ruitao],
A Lightweight Network to Learn Optical Flow from Event Data,
ICPR21(1-7)
IEEE DOI
2105
Image motion analysis, Laplace equations,
Data integrity, Neural networks, Estimation, Computer architecture,
CNN
BibRef
Lee, C.[Chankyu],
Kosta, A.K.[Adarsh Kumar],
Zhu, A.Z.[Alex Zihao],
Chaney, K.[Kenneth],
Daniilidis, K.[Kostas],
Roy, K.[Kaushik],
Spike-flownet: Event-based Optical Flow Estimation with
Energy-efficient Hybrid Neural Networks,
ECCV20(XXIX: 366-382).
Springer DOI
2010
BibRef
Xie, S.,
Lai, P.K.,
Laganiere, R.,
Lang, J.,
Effective Convolutional Neural Network Layers in Flow Estimation for
Omni-Directional Images,
3DV19(671-680)
IEEE DOI
1911
Estimation, Optical imaging, Convolution, Optical computing,
Adaptive optics, Optical fiber networks, Neural networks, neural network
BibRef
Lu, Y.[Yao],
Valmadre, J.[Jack],
Wang, H.[Heng],
Kannala, J.H.[Ju-Ho],
Harandi, M.[Mehrtash],
Torr, P.H.S.[Philip H. S.],
Devon: Deformable Volume Network for Learning Optical Flow,
WACV20(2694-2702)
IEEE DOI
2006
BibRef
Earlier:
OpticalFlow18(VI:673-677).
Springer DOI
1905
Optical imaging, Optical computing, Optical distortion,
Neural networks, Optical fiber networks, Optical propagation, Estimation
BibRef
Yang, Y.C.[Yan-Chao],
Soatto, S.[Stefano],
Conditional Prior Networks for Optical Flow,
ECCV18(XV: 282-298).
Springer DOI
1810
BibRef
Chapter on Optical Flow Field Computations and Use continues in
Scene Flow, Depth Image Flow, RGB-D .