A Noise Filtering Algorithm for Event-Based Asynchronous Change Detection Image Sensors on TrueNorth and Its Implementation on TrueNorth - Padala, Vandana; Basu, Arindam; Orchard, Garrick (Frontiers in Neuroscience, 2018)
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 Time-based Image Sensor on a neuromorphic processor, the IBM TrueNorth Neurosynaptic System. The significant contribution of this work is that it demonstrates our proposed filtering algorithm not only outperforms the traditional nearest neighbour neighbour noise filter in achieving higher signal to noise ratio ( ~10 dB higher) and retaining the events related to signal (~3X more). In addition, for some parameters, it can also generate some of the missing events in the spatial neighbourhood of the signal for all classes of moving objects in the data which are unattainable using the nearest neighbour filter.