A receding-horizon MLE recovers Neural-ODE parameters and event thresholds from event camera data by modeling events as a history-dependent marked point process.
Continuous-time intensity estimation using event cameras,
2 Pith papers cite this work. Polarity classification is still indexing.
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A dual-modal frame-event approach delivers real-time high-frame-rate binarization for silhouettes in dynamic scenes on CPU-only hardware by exploiting neuromorphic event data to reduce motion blur.
citing papers explorer
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Receding-Horizon Maximum-Likelihood Estimation of Neural-ODE Dynamics and Thresholds from Event Cameras
A receding-horizon MLE recovers Neural-ODE parameters and event thresholds from event camera data by modeling events as a history-dependent marked point process.
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See Silhouettes in Motion with Neuromorphic Vision
A dual-modal frame-event approach delivers real-time high-frame-rate binarization for silhouettes in dynamic scenes on CPU-only hardware by exploiting neuromorphic event data to reduce motion blur.