The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Mapping from Frame-Driven to Frame-Free Event-Driven Vision Systems by Low-Rate Rate Coding and Coincidence Processing--Application to Feedforward ConvNets
Open accessAbstract
Event-driven visual sensors have attracted interest from a number of different research communities. They provide visual information in quite a different way from conventional video systems consisting of sequences of still images rendered at a given "frame rate." Event-driven vision sensors take inspiration from biology. Each pixel sends out an event (spike) when it senses something meaningful is happening, without any notion of a frame. A special type of event-driven sensor is the so-called dynamic vision sensor (DVS) where each pixel computes relative changes of light or "temporal contrast." The sensor output consists of a continuous flow of pixel events that represent the moving objects in the scene. Pixel events become available with microsecond delays with respect to "reality." These events can be processed "as they flow" by a cascade of event (convolution) processors. As a result, input and output event flows are practically coincident in time, and objects can be recognized as soon as the sensor provides enough meaningful events. In this paper, we present a methodology for mapping from a properly trained neural network in a conventional frame-driven representation to an event-driven representation. The method is illustrated by studying event-driven convolutional neural networks (ConvNet) trained to recognize rotating human silhouettes or high speed poker card symbols. The event-driven ConvNet is fed with recordings obtained from a real DVS camera. The event-driven ConvNet is simulated with a dedicated event-driven simulator and consists of a number of event-driven processing modules, the characteristics of which are obtained from individually manufactured hardware modules.
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Cites
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Wulfram Gerstner, B., & Kistler, W. M. (2002) · Cited by 2,028
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Overview of the SpiNNaker System Architecture
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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
Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing
Diehl, P. U., Neil, D., Binas, J., Cook, M., Liu, S.-C., & Pfeiffer, M. (2015) · 2015 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280696 · Cited by 991
Moradi, S., Qiao, N., Stefanini, F., & Indiveri, G. (2018) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2017.2759700 · Cited by 650
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
Real-time classification and sensor fusion with a spiking deep belief network
O'Connor, P., Neil, D., Liu, S.-C., Delbruck, T., & Pfeiffer, M. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00178 · Cited by 408
Metadata
- Authors
- Perez-Carrasco, J. A., Zhao, B., Serrano, C., Acha, B., Serrano-Gotarredona, T., Chen, S., & Linares-Barranco, B.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2013-11
- Volume
- 35
- Issue
- 11
- Pages
- 2706-2719
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W1963689209
- Cited by
- 324
- References
- 42
- FWCI
- 9.59
- Type
- journal article
- Language
- en
- Open access
- green
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- The paper
- Perez-Carrasco, J. A., Zhao, B., Serrano, C., Acha, B., Serrano-Gotarredona, T., Chen, S., & Linares-Barranco, B. (2013). Mapping from Frame-Driven to Frame-Free Event-Driven Vision Systems by Low-Rate Rate Coding and Coincidence Processing--Application to Feedforward ConvNets. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2013.71
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