The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Event-Based Visual Flow
Abstract
This paper introduces a new methodology to compute dense visual flow using the precise timings of spikes from an asynchronous event-based retina. Biological retinas, and their artificial counterparts, are totally asynchronous and data-driven and rely on a paradigm of light acquisition radically different from most of the currently used frame-grabber technologies. This paper introduces a framework to estimate visual flow from the local properties of events' spatiotemporal space. We will show that precise visual flow orientation and amplitude can be estimated using a local differential approach on the surface defined by coactive events. Experimental results are presented; they show the method adequacy with high data sparseness and temporal resolution of event-based acquisition that allows the computation of motion flow with microsecond accuracy and at very low computational cost.
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Cites
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Horn, B. K. P., & Schunck, B. G. (1981) · Artificial Intelligence · doi:10.1016/0004-3702(81)90024-2 · Cited by 10,312
Shi, J., & Tomasi (1994) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR-94 · doi:10.1109/cvpr.1994.323794 · Cited by 7,724 · cited 1×
“It relies on a preprocessing of incoming images to select features [34] on which the motion visual motion flow is then estimated.”
MethodHorn, B. K., & Schunck, B. G. (1981) · SPIE Proceedings · doi:10.1117/12.965761 · Cited by 7,394
Performance of optical flow techniques
Barron, J. L., Fleet, D. J., & Beauchemin, S. S. (1994) · International Journal of Computer Vision · doi:10.1007/bf01420984 · Cited by 4,305
A 128$\times$128 120 dB 15 $\mu$s Latency Asynchronous Temporal Contrast Vision Sensor
Lichtsteiner, P., Posch, C., & Delbruck, T. (2008) · IEEE Journal of Solid-State Circuits · doi:10.1109/jssc.2007.914337 · Cited by 2,485
Determination of displacements using an improved digital correlation method
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A computational framework and an algorithm for the measurement of visual motion
Anandan, P. (1989) · International Journal of Computer Vision · doi:10.1007/bf00158167 · Cited by 1,073
Bialek, W., Rieke, F., de Ruyter van Steveninck, R. R., & Warland, D. (1991) · Science · doi:10.1126/science.2063199 · Cited by 992
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Cited by
Gallego, G., Delbruck, T., Orchard, G., Bartolozzi, C., Taba, B., Censi, A., Leutenegger, S., Davison, A. J., Conradt, J., Daniilidis, K., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3008413 · Cited by 2,566
Self-citationEV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras
Zhu, A., Yuan, L., Chaney, K., & Daniilidis, K. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.062 · Cited by 470
Events-To-Video: Bringing Modern Computer Vision to Event Cameras
Rebecq, H., Ranftl, R., Koltun, V., & Scaramuzza, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00398 · Cited by 420
Gallego, G., Rebecq, H., & Scaramuzza, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00407 · Cited by 341
Real-Time Intensity-Image Reconstruction for Event Cameras Using Manifold Regularisation
Munda, G., Reinbacher, C., & Pock, T. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1106-2 · Cited by 181
Metadata
- Authors
- Benosman, R., Clercq, C., Lagorce, X., Ieng, S.-H., & Bartolozzi, C.
- Venue
- IEEE Transactions on Neural Networks and Learning Systems
- Published
- 2014-02
- Volume
- 25
- Issue
- 2
- Pages
- 407-417
- ISSN
- 2162-237X (ISSN-L)
- 2162-2388
- OpenAlex
- W1975697167
- Cited by
- 419
- References
- 33
- FWCI
- 17.84
- Type
- journal article
- Language
- en
- Open access
- closed
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- Benosman, R., Clercq, C., Lagorce, X., Ieng, S.-H., & Bartolozzi, C. (2014). Event-Based Visual Flow. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2013.2273537
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
