arXiv Computer Vision
On the Theory of Bias Tuning in Event Cameras
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Modelling dynamic systems transfer functions from events in computational neuromorphic imaging
The paper investigates how to model dynamic system transfer functions for event‑based vision sensors by analyzing the correspondence between temporal probes and the resulting event streams in a linear shift‑invariant optical system. Using a first‑order filter pixel model and three temporal probes—a step, a linear ramp, and an exponential ramp—the authors derive the inverse chain for different event statistics and compare the results to the system’s modulation transfer function. The study finds that the step probe is highly sensitive to threshold mismatch but robust to photon shot‑noise, whereas linear and exponential probes maintain accurate signal inference even under significant mismatch, highlighting their potential for dynamic point spread function estimation in computational neuromorphic imaging.
arXiv Computer Vision
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Rate as a Feature, Not a Nuisance: Curriculum FiLM Training for Event-Based Graph Neural Networks
arXiv:2610.26933v1 Announce Type: new Abstract: An event camera emits events at a rate given by the scene motion, object texture, illumination, its own contrast thresholds, and other settings. Across...
Hugging Face Trending Papers
Sep 24
Modelling dynamic systems transfer functions from events in computational neuromorphic imaging
The paper investigates how to model dynamic system transfer functions for event-based vision sensors. It analyzes three temporal probes—step, linear ramp, and exponential ramp—within a linear shift‑invariant optical system to determine which probe best matches a specified modulation transfer function. The study finds that step probes are vulnerable to threshold mismatch but robust to photon shot‑noise, while ramp probes maintain accuracy even with high mismatch, and it proposes dynamic point spread functions as part of a unified forward model for computational neuromorphic imaging.
Hugging Face Trending Papers
Oct 14
Event Cameras versus Frame Cameras for Synthetic-Aperture Imaging: A Simulation Study
Synthetic-aperture imaging (SAI) removes occluders such as foliage by integrating many views of a scene along a camera path. It has so far been used mainly with frame cameras. We ask whether an event...
arXiv Computer Vision
Sep 18
An Event Preserving Velocity Invariant Representation for Event Cameras
The paper introduces SCARF, a real‑time velocity‑invariant representation for event cameras that preserves raw events while handling fast motion, stationary scenes, and independently moving objects. Unlike previous methods that convert events into image‑like forms and lose temporal detail, SCARF maintains temporal information and achieves state‑of‑the‑art performance in computational efficiency and representation quality.
arXiv Computer Vision
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Event Cameras versus Frame Cameras for Synthetic-Aperture Imaging: A Simulation Study
arXiv:2610.20309v1 Announce Type: new Abstract: Synthetic-aperture imaging (SAI) removes occluders such as foliage by integrating many views of a scene along a camera path. It has so far been used ma...