18.3.1.1 Event Camera

Chapter Contents (Back)
Event Camera. Motion, Differencing. Reports events of significant pixel intensity changes.

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.[Yiannis],
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.[Yiannis],
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

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


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, Computer architecture, Detectors, Spatial filters BibRef

Nunes, U.M.[Urbano Miguel], Demiris, Y.[Yiannis],
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.[Limeng], Zhang, H.G.[Hong-Guang], Zhu, C.Y.[Chen-Yang], Guo, S.[Shasha], 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, Computer architecture, 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, Computer Vision 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

Pan, L.Y.[Li-Yuan], Scheerlinck, C.[Cedric], Yu, X.[Xin], Hartley, R.[Richard], Liu, M.M.[Miao-Miao], Dai, Y.[Yuchao],
Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera,
CVPR19(6813-6822).
IEEE DOI 2002
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, computer vision, 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, Computer vision 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
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 .


Last update:Sep 19, 2021 at 21:11:01