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Found 6 papers, 0 papers with code
Re-Interpreting the Step-Response Probability Curve to Extract Fundamental Physical Parameters of Event-based Vision Sensors
Further, we demonstrate that with correct interpretation, fundamental physical parameters such as dark current and RMS noise can be accurately inferred from a collection of S-curves, leading to more accurate parameterization for high-fidelity EVS simulations.
Optimal biasing and physical limits of DVS event noise
Under dim lighting conditions, the output of Dynamic Vision Sensor (DVS) event cameras is strongly affected by noise.
Exploiting Alternating DVS Shot Noise Event Pair Statistics to Reduce Background Activity
Dynamic Vision Sensors (DVS) record "events" corresponding to pixel-level brightness changes, resulting in data-efficient representation of a dynamic visual scene.
Utility and Feasibility of a Center Surround Event Camera
Standard dynamic vision sensor (DVS) event cameras output a stream of spatially-independent log-intensity brightness change events so they cannot suppress spatial redundancy.
Unraveling the paradox of intensity-dependent DVS pixel noise
While measurements of the logarithmic photoreceptor predict that the photoreceptor is approximately a first-order system with RMS noise voltage independent of the photocurrent, DVS output shows higher noise event rates at low light intensity.
Feedback control of event cameras
Dynamic vision sensor event cameras produce a variable data rate stream of brightness change events.
