5 accepted papers
Event-based sensors (EBS) are a promising new technology for star tracking due to their low latency and power efficiency, but prior work has thus far been evaluated exclusively in simulation with simplified signal models. We propose a novel algorithm for event-based star tracking, grounded in an an…
The time and resource costs of obtaining pristine training data in machine learning are high. In signal recovery tasks, Noise2Noise proposed by Lehtinen et al. aims to reduce the data cost by learning the regression over two noisy measurements corresponding to the same latent variable. Close examina…
We propose event-based visual microphone (EBVM), a passive electro-optical technique for capturing audio signals remotely using an event camera. The event-based camera records local deformations of a surface induced by the sound propagation by observing the changes in specular reflections at each pi…
This paper presents a novel method for labeling real-world neuromorphic camera sensor data by calculating the likelihood of generating an event at each pixel within a short time window, which we refer to as "event probability mask" or EPM. Its applications include (i) objective benchmarking of event…
This paper describes a study aimed at comparing the real image sensor noise distribution to the models of noise often assumed in image denoising designs. Quantile analysis in pixel, wavelet, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-G…