Event Camera Calibration,
2021
Online
WWW Link. And the workshop paper to appear.
PDF File.
2106
Toolbox to facilitate event camera calibration.
The framework uses neural network-based image reconstruction to enable
compatibility with any existing calibration toolbox.
BibRef
Barranco, F.[Francisco],
Fermüller, C.[Cornelia],
Aloimonos, Y.F.[Yi-Fannis],
Contour Motion Estimation for Asynchronous Event-Driven Cameras,
PIEEE(102), No. 10, October 2014, pp. 1537-1556.
IEEE DOI
1410
computer vision
BibRef
Barranco, F.[Francisco],
Teo, C.L.,
Fermüller, C.[Cornelia],
Aloimonos, Y.F.[Yi-Fannis],
Contour Detection and Characterization for Asynchronous Event Sensors,
ICCV15(486-494)
IEEE DOI
1602
Computer vision
BibRef
Liu, H.C.[Han-Chao],
Zhang, F.L.[Fang-Lue],
Marshall, D.[David],
Shi, L.P.[Lu-Ping],
Hu, S.M.[Shi-Min],
High-speed video generation with an event camera,
VC(33), No. 6-8, June 2017, pp. 749-759.
Springer DOI
1706
Only record events when the light on a pixel changes. Good for high speed
images, but incomplete data.
BibRef
Munda, G.[Gottfried],
Reinbacher, C.[Christian],
Pock, T.[Thomas],
Real-Time Intensity-Image Reconstruction for Event Cameras Using
Manifold Regularisation,
IJCV(126), No. 12, December 2018, pp. 1381-1393.
Springer DOI
1811
BibRef
Reinbacher, C.[Christian],
Graber, G.[Gottfried],
Pock, T.[Thomas],
Real-Time Intensity-Image Reconstruction for Event Cameras Using
Manifold Regularisation,
BMVC16(xx-yy).
DOI Link
1805
From per-pixel intensity changes, not intensity level.
The inverse of change detection.
BibRef
Gallego, G.[Guillermo],
Lund, J.E.A.[Jon E.A.],
Mueggler, E.[Elias],
Rebecq, H.[Henri],
Delbruck, T.[Tobi],
Scaramuzza, D.[Davide],
Event-Based, 6-DOF Camera Tracking from Photometric Depth Maps,
PAMI(40), No. 10, October 2018, pp. 2402-2412.
IEEE DOI
1809
Cameras, Standards, Tracking, Voltage control, Robot vision systems,
Event-based vision, pose tracking, dynamic vision sensor,
AR/VR
BibRef
Rebecq, H.[Henri],
Gallego, G.[Guillermo],
Scaramuzza, D.[Davide],
EMVS: Event-based Multi-View Stereo,
BMVC16(xx-yy).
HTML Version.
1805
Stereo with event (pixel change, not value) cameras.
BibRef
Rebecq, H.[Henri],
Ranftl, R.[René],
Koltun, V.[Vladlen],
Scaramuzza, D.[Davide],
High Speed and High Dynamic Range Video with an Event Camera,
PAMI(43), No. 6, June 2021, pp. 1964-1980.
IEEE DOI
2106
BibRef
Earlier:
Events-To-Video: Bringing Modern Computer Vision to Event Cameras,
CVPR19(3852-3861).
IEEE DOI
2002
Code, HDR.
Dataset, HDR.
Dataset, E2VID.
HTML Version. Image reconstruction, Cameras, Streaming media, Dynamic range,
Brightness, Heuristic algorithms,
high dynamic range
BibRef
Messikommer, N.[Nico],
Georgoulis, S.[Stamatios],
Gehrig, D.[Daniel],
Tulyakov, S.[Stepan],
Erbach, J.[Julius],
Bochicchio, A.[Alfredo],
Li, Y.Y.[Yuan-You],
Scaramuzza, D.[Davide],
Multi-Bracket High Dynamic Range Imaging with Event Cameras,
NTIRE22(546-556)
IEEE DOI
2210
Image resolution, Fuses, Pipelines, Dynamic range,
Cameras, Robustness
BibRef
Rebecq, H.[Henri],
Gallego, G.[Guillermo],
Mueggler, E.[Elias],
Scaramuzza, D.[Davide],
EMVS: Event-Based Multi-View Stereo: 3D Reconstruction with an Event
Camera in Real-Time,
IJCV(126), No. 12, December 2018, pp. 1394-1414.
Springer DOI
1811
BibRef
Peng, X.[Xin],
Wang, Y.[Yifu],
Gao, L.[Ling],
Kneip, L.[Laurent],
Globally-optimal Event Camera Motion Estimation,
ECCV20(XXVI:51-67).
Springer DOI
2011
BibRef
Zhou, Y.[Yi],
Gallego, G.[Guillermo],
Rebecq, H.[Henri],
Kneip, L.[Laurent],
Li, H.D.[Hong-Dong],
Scaramuzza, D.[Davide],
Semi-dense 3D Reconstruction with a Stereo Event Camera,
ECCV18(I: 242-258).
Springer DOI
1810
BibRef
Zebhi, S.[Saeedeh],
Al-Modarresi, S.M.T.,
Abootalebi, V.[Vahid],
Converting video classification problem to image classification with
global descriptors and pre-trained network,
IET-CV(14), No. 8, December 2020, pp. 614-624.
DOI Link
2012
Use a motion history image.
BibRef
Cadena, P.R.G.,
Qian, Y.,
Wang, C.,
Yang, M.,
SPADE-E2VID: Spatially-Adaptive Denormalization for Event-Based Video
Reconstruction,
IP(30), 2021, pp. 2488-2500.
IEEE DOI
2102
Image reconstruction, Cameras, Training, Image resolution,
Task analysis, Optical losses, Brightness, Image reconstruction,
sparse image
BibRef
Gallego, G.[Guillermo],
Delbrück, T.[Tobi],
Orchard, G.[Garrick],
Bartolozzi, C.[Chiara],
Taba, B.[Brian],
Censi, A.[Andrea],
Leutenegger, S.[Stefan],
Davison, A.J.[Andrew J.],
Conradt, J.[Jörg],
Daniilidis, K.[Kostas],
Scaramuzza, D.[Davide],
Event-Based Vision: A Survey,
PAMI(44), No. 1, January 2022, pp. 154-180.
IEEE DOI
2112
Cameras, Voltage control, Brightness, Robot vision systems, Retina,
Event cameras, bio-inspired vision, asynchronous sensor,
low power
BibRef
Pan, L.Y.[Li-Yuan],
Hartley, R.I.[Richard I.],
Scheerlinck, C.[Cedric],
Liu, M.M.[Miao-Miao],
Yu, X.[Xin],
Dai, Y.C.[Yu-Chao],
High Frame Rate Video Reconstruction Based on an Event Camera,
PAMI(44), No. 5, May 2022, pp. 2519-2533.
IEEE DOI
2204
BibRef
Earlier: A1, A3, A5, A2, A4, A6:
Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera,
CVPR19(6813-6822).
IEEE DOI
2002
Image reconstruction, Cameras, Image resolution, Data models,
Optimization, Image restoration, Lighting, Event camera (DAVIS),
fibonacci sequence
BibRef
Li, Z.[Zeyu],
Liu, Y.[Yong],
Zhou, F.[Feng],
Li, X.W.[Xiao-Wan],
Intensity/Inertial Integration-Aided Feature Tracking on Event
Cameras,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link
2205
BibRef
Iaboni, C.[Craig],
Patel, H.[Himanshu],
Lobo, D.[Deepan],
Choi, J.W.[Ji-Won],
Abichandani, P.[Pramod],
Where Are They Going? Clustering Event Camera Data to Detect and
Track Moving Objects,
Computer(55), No. 6, June 2022, pp. 90-94.
IEEE DOI
2206
Tutorial.
Use of event cameras with frame-based algorithms.
BibRef
Schiopu, I.[Ionut],
Bilcu, R.C.[Radu Ciprian],
Lossless Compression of Event Camera Frames,
SPLetters(29), 2022, pp. 1779-1783.
IEEE DOI
2208
Cameras, Electromagnetic interference, Symbols, Image coding,
Indexes, Encoding, Context modeling, Lossless coding, event camera
BibRef
Wang, L.[Lin],
Kim, T.K.[Tae-Kyun],
Yoon, K.J.[Kuk-Jin],
Joint Framework for Single Image Reconstruction and Super-Resolution
With an Event Camera,
PAMI(44), No. 11, November 2022, pp. 7657-7673.
IEEE DOI
2210
Image reconstruction, Superresolution, Spatial resolution,
Streaming media, Task analysis, Training, Event-based vision,
adversarial learning
BibRef
Uddin, S.M.N.[S. M. Nadim],
Ahmed, S.H.[Soikat Hasan],
Jung, Y.J.[Yong Ju],
Unsupervised Deep Event Stereo for Depth Estimation,
CirSysVideo(32), No. 11, November 2022, pp. 7489-7504.
IEEE DOI
2211
Cameras, Estimation, Image matching, Correlation, Training, Lighting,
Image reconstruction, Event camera, stereo matching,
unsupervised deep learning
BibRef
Guo, S.[Shasha],
Delbruck, T.[Tobi],
Low Cost and Latency Event Camera Background Activity Denoising,
PAMI(45), No. 1, January 2023, pp. 785-795.
IEEE DOI
2212
Voltage control, Noise reduction, Cameras, Automobiles,
Noise measurement, Brightness, Vision sensors, ROC
BibRef
Deng, Y.J.[Yong-Jian],
Chen, H.[Hao],
Li, Y.F.[You-Fu],
MVF-Net: A Multi-View Fusion Network for Event-Based Object
Classification,
CirSysVideo(32), No. 12, December 2022, pp. 8275-8284.
IEEE DOI
2212
Cameras, Task analysis, Feature extraction, Data models,
Streaming media, Power demand, Event data, multi-view, attention,
object categorization
BibRef
Baldwin, R.W.[R. Wes],
Liu, R.[Ruixu],
Almatrafi, M.[Mohammed],
Asari, V.[Vijayan],
Hirakawa, K.[Keigo],
Time-Ordered Recent Event (TORE) Volumes for Event Cameras,
PAMI(45), No. 2, February 2023, pp. 2519-2532.
IEEE DOI
2301
Cameras, Voltage control, Retina, Timing,
Task analysis, Noise reduction, Dynamic vision sensor, denoising
BibRef
Chen, Z.W.[Zhi-Wen],
Wu, J.J.[Jin-Jian],
Hou, J.H.[Jun-Hui],
Li, L.[Leida],
Dong, W.S.[Wei-Sheng],
Shi, G.M.[Guang-Ming],
ECSNet: Spatio-Temporal Feature Learning for Event Camera,
CirSysVideo(33), No. 2, February 2023, pp. 701-712.
IEEE DOI
2302
Feature extraction, Cameras, Task analysis, Cloud computing,
Representation learning, Data mining, Brightness, Event camera,
action recognition
BibRef
Jia, Z.X.[Ze-Xi],
You, K.[Kaichao],
He, W.H.[Wei-Hua],
Tian, Y.[Yang],
Feng, Y.X.[Yong-Xiang],
Wang, Y.[Yaoyuan],
Jia, X.[Xu],
Lou, Y.H.[Yi-Hang],
Zhang, J.Y.[Jing-Yi],
Li, G.Q.[Guo-Qi],
Zhang, Z.[Ziyang],
Event-Based Semantic Segmentation With Posterior Attention,
IP(32), 2023, pp. 1829-1842.
IEEE DOI
2303
Cameras, Semantic segmentation, Transformers, Task analysis,
Semantics, Standards, Image segmentation, event camera, attention mechanism
BibRef
Huang, Z.[Ze],
Sun, L.[Li],
Zhao, C.[Cheng],
Li, S.[Song],
Su, S.[Songzhi],
EventPoint: Self-Supervised Interest Point Detection and Description
for Event-based Camera,
WACV23(5385-5394)
IEEE DOI
2302
Knowledge engineering, Representation learning, Power demand,
Annotations, Neural networks, Detectors, Self-supervised learning, Robotics
BibRef
Lin, S.N.[Song-Nan],
Ma, Y.[Ye],
Guo, Z.H.[Zhen-Hua],
Wen, B.[Bihan],
DVS-Voltmeter:
Stochastic Process-Based Event Simulator for Dynamic Vision Sensors,
ECCV22(VII:578-593).
Springer DOI
2211
BibRef
Sun, L.H.[Lin-Hui],
Zhang, Y.F.[Yi-Fan],
Cheng, K.[Ke],
Cheng, J.[Jian],
Lu, H.Q.[Han-Qing],
MENet: A Memory-Based Network with Dual-Branch for Efficient Event
Stream Processing,
ECCV22(XXIV:214-234).
Springer DOI
2211
BibRef
Zhang, D.H.[De-Hao],
Ding, Q.K.[Qian-Kun],
Duan, P.Q.[Pei-Qi],
Zhou, C.[Chu],
Shi, B.X.[Bo-Xin],
Data Association Between Event Streams and Intensity Frames Under
Diverse Baselines,
ECCV22(VII:72-90).
Springer DOI
2211
BibRef
Wan, Z.Y.[Zeng-Yu],
Wang, Y.[Yang],
Tan, G.C.[Gan-Chao],
Cao, Y.[Yang],
Zha, Z.J.[Zheng-Jun],
S2N: Suppression-Strengthen Network for Event-Based Recognition Under
Variant Illuminations,
ECCV22(III:716-733).
Springer DOI
2211
BibRef
Cho, H.[Hoonhee],
Yoon, K.J.[Kuk-Jin],
Selection and Cross Similarity for Event-Image Deep Stereo,
ECCV22(XXXII:470-486).
Springer DOI
2211
BibRef
Barchid, S.[Sami],
Mennesson, J.[José],
Djéraba, C.[Chaabane],
Bina-Rep Event Frames:
A Simple and Effective Representation for Event-Based Cameras,
ICIP22(3998-4002)
IEEE DOI
2211
Sensitivity, Image recognition, Neuromorphics, Streaming media,
Cameras, Robustness, Convolutional neural networks, Event Cameras,
Object Recognition
BibRef
Wang, Z.[Zuowen],
Hu, Y.[Yuhuang],
Liu, S.C.[Shih-Chii],
Exploiting Spatial Sparsity for Event Cameras with Visual
Transformers,
ICIP22(411-415)
IEEE DOI
2211
Visualization, Pipelines, Brightness, Transformers, Cameras,
Event cameras, Spatial sparsity, Reduced computation, Visual transformers
BibRef
Delbruck, T.[Tobi],
Li, C.[Chenghan],
Graca, R.[Rui],
Mcreynolds, B.[Brian],
Utility and Feasibility of a Center Surround Event Camera,
ICIP22(381-385)
IEEE DOI
2211
Resistors, Switches, Vision sensors, Retina, Cameras, Transconductors,
Biology, pixel, neuromorphic
BibRef
Liao, W.[Wei],
Zhang, X.[Xiang],
Yu, L.[Lei],
Lin, S.J.[Shi-Jie],
Yang, W.[Wen],
Qiao, N.[Ning],
Synthetic Aperture Imaging with Events and Frames,
CVPR22(17714-17723)
IEEE DOI
2210
Photography, Visualization, Codes, Lighting, Apertures,
Feature extraction, Computational photography, Low-level vision
BibRef
Kim, J.[Junho],
Hwang, I.[Inwoo],
Kim, Y.M.[Young Min],
Ev-TTA: Test-Time Adaptation for Event-Based Object Recognition,
CVPR22(17724-17733)
IEEE DOI
2210
Training, Lighting, Performance gain, Prediction algorithms, Cameras,
Loss measurement, Classification algorithms,
Transfer/low-shot/long-tail learning
BibRef
Zhang, K.X.[Kai-Xuan],
Che, K.W.[Kai-Wei],
Zhang, J.G.[Jian-Guo],
Cheng, J.[Jie],
Zhang, Z.[Ziyang],
Guo, Q.H.[Qing-Hai],
Leng, L.[Luziwei],
Discrete time convolution for fast event-based stereo,
CVPR22(8666-8676)
IEEE DOI
2210
Convolution, Biological system modeling, Computational modeling,
Estimation, Feature extraction, Encoding, Data models, Vision+X
BibRef
Nam, Y.[Yeongwoo],
Mostafavi, M.[Mohammad],
Yoon, K.J.[Kuk-Jin],
Choi, J.H.[Jong-Hyun],
Stereo Depth from Events Cameras: Concentrate and Focus on the Future,
CVPR22(6104-6113)
IEEE DOI
2210
Training, Art, Tensors, Robot vision systems, Estimation,
Streaming media, Low-level vision,
Robot vision
BibRef
Deng, Y.J.[Yong-Jian],
Chen, H.[Hao],
Liu, H.[Hai],
Li, Y.F.[You-Fu],
A Voxel Graph CNN for Object Classification with Event Cameras,
CVPR22(1162-1171)
IEEE DOI
2210
Learning systems, Power demand, Limiting, Computational modeling,
Semantics, Robot vision systems, Cameras, Recognition: detection,
Robot vision
BibRef
He, W.H.[Wei-Hua],
You, K.[Kaichao],
Qiao, Z.D.[Zhen-Dong],
Jia, X.[Xu],
Zhang, Z.[Ziyang],
Wang, W.H.[Wen-Hui],
Lu, H.C.[Hu-Chuan],
Wang, Y.[Yaoyuan],
Liao, J.X.[Jian-Xing],
TimeReplayer: Unlocking the Potential of Event Cameras for Video
Interpolation,
CVPR22(17783-17792)
IEEE DOI
2210
Training, Interpolation, Extrapolation, Costs, Training data, Cameras,
Recording, Computational photography, Low-level vision
BibRef
Han, J.[Jin],
Yang, Y.X.[Yi-Xin],
Zhou, C.[Chu],
Xu, C.[Chao],
Shi, B.X.[Bo-Xin],
EvIntSR-Net: Event Guided Multiple Latent Frames Reconstruction and
Super-resolution,
ICCV21(4862-4871)
IEEE DOI
2203
Bridges, Fuses, Superresolution, Streaming media, Dynamic range,
Cameras, Spatial resolution, Low-level and physics-based vision,
Computational photography
BibRef
Yao, M.[Man],
Gao, H.[Huanhuan],
Zhao, G.S.[Guang-She],
Wang, D.H.[Ding-Heng],
Lin, Y.[Yihan],
Yang, Z.[Zhaoxu],
Li, G.Q.[Guo-Qi],
Temporal-wise Attention Spiking Neural Networks for Event Streams
Classification,
ICCV21(10201-10210)
IEEE DOI
2203
Training, Computational modeling, Gesture recognition,
Streaming media, Feature extraction, Brain modeling, Task analysis,
Recognition and classification
BibRef
Zhang, J.Q.[Ji-Qing],
Yang, X.[Xin],
Fu, Y.[Yingkai],
Wei, X.P.[Xiao-Peng],
Yin, B.C.[Bao-Cai],
Dong, B.[Bo],
Object Tracking by Jointly Exploiting Frame and Event Domain,
ICCV21(13023-13032)
IEEE DOI
2203
Visualization, Adaptation models, Fuses, Dynamic range, Cameras,
Object tracking, Motion and tracking,
BibRef
Li, Y.J.[Yi-Jin],
Zhou, H.[Han],
Yang, B.B.[Bang-Bang],
Zhang, Y.[Ye],
Cui, Z.P.[Zhao-Peng],
Bao, H.J.[Hu-Jun],
Zhang, G.F.[Guo-Feng],
Graph-based Asynchronous Event Processing for Rapid Object
Recognition,
ICCV21(914-923)
IEEE DOI
2203
Convolution, Streaming media, Cameras, Prediction algorithms,
Windows, Object recognition, Computational complexity,
BibRef
Wang, L.[Lin],
Chae, Y.J.[Yu-Jeong],
Yoon, K.J.[Kuk-Jin],
Dual Transfer Learning for Event-based End-task Prediction via
Pluggable Event to Image Translation,
ICCV21(2115-2125)
IEEE DOI
2203
Representation learning, Electrical impedance tomography,
Visualization, Image segmentation, Motion segmentation,
Vision + other modalities
BibRef
Mostafavi, S.I.M.[S.I. Mohammad],
Yoon, K.J.[Kuk-Jin],
Choi, J.H.[Jong-Hyun],
Event-Intensity Stereo: Estimating Depth by the Best of Both Worlds,
ICCV21(4238-4247)
IEEE DOI
2203
Power demand, Computer network reliability, Neural networks,
Estimation, Dynamic range, Benchmark testing,
3D from multiview and other sensors
BibRef
Li, S.Q.[Si-Qi],
Feng, Y.T.[Yu-Tong],
Li, Y.P.[Yi-Peng],
Jiang, Y.[Yu],
Zou, C.Q.[Chang-Qing],
Gao, Y.[Yue],
Event Stream Super-Resolution via Spatiotemporal Constraint Learning,
ICCV21(4460-4469)
IEEE DOI
2203
Learning systems, Superresolution, Neural networks,
Streaming media, Cameras, Real-time systems,
BibRef
Wang, Z.W.[Zi-Wei],
Ng, Y.[Yonhon],
Scheerlinck, C.[Cedric],
Mahony, R.[Robert],
An Asynchronous Kalman Filter for Hybrid Event Cameras,
ICCV21(438-447)
IEEE DOI
2203
Visualization, Uncertainty, Pipelines, Lighting, Dynamic range,
Sensor fusion, Cameras, Vision + other modalities,
BibRef
Gu, C.[Cheng],
Learned-Miller, E.G.[Erik G.],
Sheldon, D.[Daniel],
Gallego, G.[Guillermo],
Bideau, P.[Pia],
The Spatio-Temporal Poisson Point Process:
A Simple Model for the Alignment of Event Camera Data,
ICCV21(13475-13484)
IEEE DOI
2203
Visualization, Tracking, Computational modeling,
Motion segmentation, Brightness, Cameras, Motion and tracking,
Vision for robotics and autonomous vehicles
BibRef
Zhao, J.[Jing],
Xie, J.[Jiyu],
Xiong, R.Q.[Rui-Qin],
Zhang, J.[Jian],
Yu, Z.F.[Zhao-Fei],
Huang, T.J.[Tie-Jun],
Super Resolve Dynamic Scene from Continuous Spike Streams,
ICCV21(2513-2522)
IEEE DOI
2203
Visualization, Image resolution, Dynamics, Superresolution,
Streaming media, Reconstruction algorithms, Cameras,
Low-level and physics-based vision
BibRef
Zhao, J.[Jing],
Xiong, R.Q.[Rui-Qin],
Liu, H.F.[Hang-Fan],
Zhang, J.[Jian],
Huang, T.J.[Tie-Jun],
Spk2ImgNet:
Learning to Reconstruct Dynamic Scene from Continuous Spike Stream,
CVPR21(11991-12000)
IEEE DOI
2111
Correlation, Dynamics, Neural networks,
Streaming media, Reconstruction algorithms, Cameras
BibRef
Hu, Y.H.[Yu-Huang],
Liu, S.C.[Shih-Chii],
Delbruck, T.[Tobi],
v2e: From Video Frames to Realistic DVS Events,
EventVision21(1312-1321)
IEEE DOI
2109
Dynamic Vision Sensor.
Create event-camera data.
Training, Visualization, Lighting, Vision sensors, Tools,
Cameras, Pattern recognition
BibRef
Peveri, F.[Francesca],
Testa, S.[Simone],
Sabatini, S.P.[Silvio P.],
A Cortically-inspired Architecture for Event-based Visual Motion
Processing: From Design Principles to Real-world Applications,
EventVision21(1395-1402)
IEEE DOI
2109
Visualization, Neuromorphics, Neurons, Neural networks,
Detectors, Spatial filters
BibRef
Nunes, U.M.[Urbano Miguel],
Demiris, Y.F.[Yi-Fannis],
Live Demonstration: Incremental Motion Estimation for Event-based
Cameras by Dispersion Minimisation,
EventVision21(1322-1323)
IEEE DOI
2109
Portable computers, Motion estimation, Cameras,
Minimization, Pattern recognition, Motion measurement
BibRef
Delbruck, T.[Tobi],
Graca, R.[Rui],
Paluch, M.[Marcin],
Feedback control of event cameras,
EventVision21(1324-1332)
IEEE DOI
2109
Current measurement, Bandwidth,
Production, Vision sensors, Cameras
BibRef
Duwek, H.C.[Hadar Cohen],
Shalumov, A.[Albert],
Tsur, E.E.[Elishai Ezra],
Image Reconstruction from Neuromorphic Event Cameras using
Laplacian-Prediction and Poisson Integration with Spiking and
Artificial Neural Networks,
EventVision21(1333-1341)
IEEE DOI
2109
Visualization, Laplace equations, Neuromorphics, Pipelines, Cameras,
Sensors, Pattern recognition
BibRef
Jiao, J.H.[Jian-Hao],
Huang, H.Y.[Huai-Yang],
Li, L.[Liang],
He, Z.J.[Zhi-Jian],
Zhu, Y.L.[Yi-Long],
Liu, M.[Ming],
Comparing Representations in Tracking for Event Camera-based SLAM,
EventVision21(1369-1376)
IEEE DOI
2109
Tracking loops, Simultaneous localization and mapping, Tracking, Trajectory
BibRef
Nehvi, J.[Jalees],
Golyanik, V.[Vladislav],
Mueller, F.[Franziska],
Seidel, H.P.[Hans-Peter],
Elgharib, M.[Mohamed],
Theobalt, C.[Christian],
Differentiable Event Stream Simulator for Non-Rigid 3D Tracking,
EventVision21(1302-1311)
IEEE DOI
2109
Training, Surface reconstruction,
Supervised learning, Gesture recognition, Trajectory
BibRef
Muglikar, M.[Manasi],
Gehrig, M.[Mathias],
Gehrig, D.[Daniel],
Scaramuzza, D.[Davide],
How to Calibrate Your Event Camera,
EventVision21(1403-1409)
IEEE DOI
2109
Computational modeling, Robot vision systems, Cameras, Distortion,
Calibration, Sensors, Pattern recognition
BibRef
Zhang, L.M.[Li-Meng],
Zhang, H.G.[Hong-Guang],
Zhu, C.Y.[Chen-Yang],
Guo, S.S.[Sha-Sha],
Chen, J.[Jihua],
Wang, L.[Lei],
Fine-grained Video Deblurring with Event Camera,
MMMod21(I:352-364).
Springer DOI
2106
BibRef
Kostadinov, D.[Dimche],
Scaramuzza, D.[Davide],
Unsupervised Feature Learning for Event Data:
Direct vs. Inverse Problem Formulation,
ICPR21(5981-5987)
IEEE DOI
2105
Inverse problems, Dynamic range, Cameras,
Encoding, Object recognition
BibRef
Zhao, J.,
Xiong, R.,
Zhao, R.,
Wang, J.,
Ma, S.,
Huang, T.,
Motion Estimation for Spike Camera Data Sequence via Spike Interval
Analysis,
VCIP20(371-374)
IEEE DOI
2102
Cameras, Motion estimation, Trajectory, Image reconstruction,
Data models, Estimation, Dynamics, motion analysis,
motion estimation
BibRef
Zhang, S.[Song],
Zhang, Y.[Yu],
Jiang, Z.[Zhe],
Zou, D.Q.[Dong-Qing],
Ren, J.[Jimmy],
Zhou, B.[Bin],
Learning to See in the Dark with Events,
ECCV20(XVIII:666-682).
Springer DOI
2012
BibRef
Wang, B.S.[Bi-Shan],
He, J.W.[Jing-Wei],
Yu, L.[Lei],
Xia, G.S.[Gui-Song],
Yang, W.[Wen],
Event Enhanced High-quality Image Recovery,
ECCV20(XIII:155-171).
Springer DOI
2011
BibRef
Stoffregen, T.[Timo],
Scheerlinck, C.[Cedric],
Scaramuzza, D.[Davide],
Drummond, T.[Tom],
Barnes, N.[Nick],
Kleeman, L.[Lindsay],
Mahony, R.[Robert],
Reducing the Sim-to-real Gap for Event Cameras,
ECCV20(XXVII:534-549).
Springer DOI
2011
BibRef
Harrigan, S.,
Coleman, S.,
Kerr, D.,
Yogarajah, P.,
Fang, Z.,
Wu, C.,
Post-Stimulus Time-Dependent Event Descriptor,
ICIP20(385-389)
IEEE DOI
2011
Support vector machines, Vision sensors, Lattices,
Feature extraction, Machine learning
BibRef
Su, B.,
Yu, L.,
Yang, W.,
Event-Based High Frame-Rate Video Reconstruction With A Novel
Cycle-Event Network,
ICIP20(86-90)
IEEE DOI
2011
Image reconstruction, Cameras, Generators, Logic gates, Training,
Generative adversarial networks, Streaming media, Event camera,
GAN
BibRef
Jiang, M.,
Liu, Z.,
Wang, B.,
Yu, L.,
Yang, W.,
Robust Intensity Image Reconstruciton Based On Event Cameras,
ICIP20(968-972)
IEEE DOI
2011
Cameras, Image reconstruction, Streaming media,
Reconstruction algorithms, Mathematical model, Brightness,
Motion blur
BibRef
Liu, D.,
Parra, Á.,
Chin, T.,
Globally Optimal Contrast Maximisation for Event-Based Motion
Estimation,
CVPR20(6348-6357)
IEEE DOI
2008
Upper bound, Estimation, Cameras, Streaming media,
Robot vision systems, Motion estimation, Kernel
BibRef
Gehrig, D.,
Gehrig, M.,
Hidalgo-Carrió, J.,
Scaramuzza, D.,
Video to Events: Recycling Video Datasets for Event Cameras,
CVPR20(3583-3592)
IEEE DOI
PDF File.
2008
Code, Event Camera.
WWW Link. ESIM: Event camera simulator:
WWW Link. Video:
WWW Link. Cameras, Sensors, Semantics, Standards, Brightness, Task analysis,
Machine learning
BibRef
Baldwin, R.W.[R. Wes],
Almatrafi, M.[Mohammed],
Asari, V.[Vijayan],
Hirakawa, K.[Keigo],
Event Probability Mask (EPM) and Event Denoising Convolutional Neural
Network (EDnCNN) for Neuromorphic Cameras,
CVPR20(1698-1707)
IEEE DOI
2008
Cameras, Voltage control, Noise reduction, Neuromorphics, Hardware,
Benchmark testing, Noise measurement
BibRef
Tulyakov, S.[Stepan],
Fleuret, F.[Francois],
Kiefel, M.[Martin],
Gehler, P.[Peter],
Hirsch, M.[Michael],
Learning an Event Sequence Embedding for Dense Event-Based Deep
Stereo,
ICCV19(1527-1537)
IEEE DOI
2004
Event camera.
biomimetics, cameras, image representation,
image sensors, learning (artificial intelligence), Power demand
BibRef
Wang, Q.Y.[Qin-Yi],
Zhang, Y.X.[Ye-Xin],
Yuan, J.S.[Jun-Song],
Lu, Y.L.[Yi-Long],
Space-Time Event Clouds for Gesture Recognition:
From RGB Cameras to Event Cameras,
WACV19(1826-1835)
IEEE DOI
1904
Sense motion, event streams. space-time location of intensity change.
cameras, gesture recognition, image motion analysis, image sensors,
neural net architecture, individual event, space-time location,
Real-time systems
BibRef
Alzugaray, I.,
Chli, M.,
Asynchronous Multi-Hypothesis Tracking of Features with Event Cameras,
3DV19(269-278)
IEEE DOI
1911
BibRef
Earlier:
ACE: An Efficient Asynchronous Corner Tracker for Event Cameras,
3DV18(653-661)
IEEE DOI
1812
Tracking, Cameras, Feature extraction, Visualization,
Streaming media, Robot vision systems, SLAM, dvs, visual odometry,
visual tracking.
image motion analysis, image sequences,
efficient asynchronous corner tracker.
BibRef
Stoffregen, T.[Timo],
Kleeman, L.[Lindsay],
Event Cameras, Contrast Maximization and Reward Functions: An Analysis,
CVPR19(12292-12300).
IEEE DOI
2002
Event cameras asynchronously report timestamped changes in pixel intensity.
BibRef
Scheerlinck, C.[Cedric],
Barnes, N.[Nick],
Mahony, R.[Robert],
Continuous-Time Intensity Estimation Using Event Cameras,
ACCV18(V:308-324).
Springer DOI
1906
Asynchronous, data-driven measurements of local temporal contrast.
BibRef
Gehrig, D.[Daniel],
Loquercio, A.[Antonio],
Derpanis, K.[Konstantinos],
Scaramuzza, D.[Davide],
End-to-End Learning of Representations for Asynchronous Event-Based
Data,
ICCV19(5632-5642)
IEEE DOI
2004
Event camers: pixel changes.
cameras, convolutional neural nets,
image motion analysis, image representation, image sensors,
Spatiotemporal phenomena
BibRef
Gao, S.,
Guo, G.,
Chen, C.L.P.[C. L. Philip],
Event-Based Incremental Broad Learning System for Object
Classification,
CEFRL19(2989-2998)
IEEE DOI
2004
cameras, convolutional neural nets, image classification,
image sensors, learning (artificial intelligence),
event camera
BibRef
Gallego, G.,
Rebecq, H.,
Scaramuzza, D.,
A Unifying Contrast Maximization Framework for Event Cameras, with
Applications to Motion, Depth, and Optical Flow Estimation,
CVPR18(3867-3876)
IEEE DOI
1812
Trajectory, Estimation, Cameras, Optical imaging, Brightness,
Image edge detection
BibRef
Barua, S.,
Miyatani, Y.,
Veeraraghavan, A.,
Direct face detection and video reconstruction from event cameras,
WACV16(1-9)
IEEE DOI
1606
Cameras
BibRef
Kim, H.[Hanme],
Leutenegger, S.[Stefan],
Davison, A.J.[Andrew J.],
Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera,
ECCV16(VI: 349-364).
Springer DOI
1611
Award, ECCV.
BibRef
Kim, H.[Hanme],
Handa, A.[Ankur],
Benosman, R.[Ryad],
Ieng, S.H.[Sio-Hoi],
Davison, A.J.[Andrew J.],
Simultaneous Mosaicing and Tracking with an Event Camera,
BMVC14(xx-yy).
HTML Version.
1410
BibRef
Gobron, S.[Stéphane],
Ahn, J.H.[Jung-Hyun],
Garcia, D.[David],
Silvestre, Q.[Quentin],
Thalmann, D.[Daniel],
Boulic, R.[Ronan],
An Event-Based Architecture to Manage Virtual Human Non-Verbal
Communication in 3D Chatting Environment,
AMDO12(58-68).
Springer DOI
1208
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Differencing Papers -- Ramesh Jain .