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A Noise Filtering Algorithm for Event-Based Asynchronous Change Detection Image Sensors on TrueNorth and Its Implementation on TrueNorth
2018
Frontiers in Neuroscience
Asynchronous event-based sensors, or "silicon retinae," are a new class of vision sensors inspired by biological vision systems. The output of these sensors often contains a significant number of noise events along with the signal. Filtering these noise events is a common preprocessing step before using the data for tasks such as tracking and classification. This paper presents a novel spiking neural network-based approach to filtering noise events from data captured by an Asynchronous
doi:10.3389/fnins.2018.00118
pmid:29556172
pmcid:PMC5844986
fatcat:n2wmgttmlvfwrjtbl3eqbs6ccq