Journal Article10.2352/ei.2024.36.7.iss-289
Joint Parameter Estimation for Event-Based Vision Sensor Characterization
Xiaozheng Mou,Rui Jiang,Xuegong Zhang,Menghan Guo,Bo Mu,Andreas Süss +5 more
TL;DR: A pixel-wise parameter estimation framework for EVS characterization based on an ODE pixel latency model and autoregressive noise model.
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Abstract: This paper proposes a pixel-wise parameter estimation framework for Event-based Vision Sensor (EVS) characterization.Using an ordinary differential equation (ODE) based pixel latency model and an autoregressive Monte-Carlo noise model, we first identify the representative parameters of EVS.The parameter estimation is then formulated as an optimization problem to minimize the measurement-prediction error for both pixel latency and event firing probability.Finally, the effectiveness and accuracy of the proposed framework are verified by comparison of synthetic and measured event response latency as well as firing probability as function of temporal contrast (so-called S-curves).
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Figures
Figure 4. Pixel response latency vs. luminance as function of CC, IBSF and CTH. 
Figure 5. Loss curves of the proposed objective function with one free parameter and the other two fixed. 
Figure 3. Simplified log-amplifier schematic a) and SF schematic b) and their respective small signal circuits in c) and d), respectively. 
Figure 8. S-curves comparison between the measurement and our model using the estimated parameters for 200 sampled pixels. 
Figure 6. Pixel-wise distributions of estimated parameters CC, IBSF, CTH. The parameters here are normalized to the expected value. 
Figure 7. Pixel latency comparison between the measurement and our model using the estimated parameters under different illuminances and temporal contrast for two sampled pixels.
References
Event-based Vision: A Survey
Guillermo Gallego,Tobi Delbruck,Garrick Orchard,Chiara Bartolozzi,Brian Taba,Andrea Censi,Stefan Leutenegger,Andrew J. Davison,Jörg Conradt,Kostas Daniilidis,Davide Scaramuzza +10 more
TL;DR: This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras.
Event-Based Vision: A Survey
TL;DR: Event cameras as discussed by the authors are bio-inspired sensors that differ from conventional frame cameras: instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes.
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5.10 A 1280×720 Back-Illuminated Stacked Temporal Contrast Event-Based Vision Sensor with 4.86µm Pixels, 1.066GEPS Readout, Programmable Event-Rate Controller and Compressive Data-Formatting Pipeline
Thomas Finateu,Atsumi Niwa,Daniel Matolin,Koya Tsuchimoto,Andrea Mascheroni,Etienne Reynaud,Pooria Mostafalu,Frederick Brady,Ludovic Chotard,Florian LeGoff,Hirotsugu Takahashi,Hayato Wakabayashi,Yusuke Oike,Christoph Posch +13 more
- 01 Feb 2020
TL;DR: Event-based vision sensors pixel-individually detect temporal contrast exceeding a preset relative threshold to follow the temporal evolution of relative light changes and to define sampling points for frame-free pixel-level measurement of absolute intensity.
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Video to Events: Recycling Video Datasets for Event Cameras
Daniel Gehrig,Mathias Gehrig,Javier Hidalgo-Carrio,Davide Scaramuzza +3 more
- 14 Jun 2020
TL;DR: This paper presents a method that addresses these needs by converting any existing video dataset recorded with conventional cameras to synthetic event data, which unlocks the use of a virtually unlimited number of existing video datasets for training networks designed for real event data.
A 64x64 aer logarithmic temporal derivative silicon retina
P. Lichtsteiner,Tobi Delbruck +1 more
- 25 Jul 2005
TL;DR: In this article, an Address-Event Representation (AER) chip is proposed to generate events corresponding to changes in log intensity on a shared digital bus, where the resulting address-events are output asynchronously.