ArticleFrontiers in neuroscience2018
A Noise Filtering Algorithm for Event-Based Asynchronous Change Detection Image Sensors on TrueNorth and Its Implementation on TrueNorth.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Sequential analysis and its applications to neuromorphic engineering.Frontiers in neuroscience · 2025Article
- Event Stream Denoising Method Based on Spatio-Temporal Density and Time Sequence Analysis.Sensors (Basel, Switzerland) · 2024Article
- Adaptive Unsupervised Learning-Based 3D Spatiotemporal Filter for Event-Driven Cameras.Research (Washington, D.C.) · 2024Article
- Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review.Frontiers in neural circuits · 2021Review
- Low-Power Dynamic Object Detection and Classification With Freely Moving Event Cameras.Frontiers in neuroscience · 2020Article
- Less Data Same Information for Event-Based Sensors: A Bioinspired Filtering and Data Reduction Algorithm.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
3 authors.
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Abstract
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 outperforms the traditional nearest neighbor noise filter in achieving higher signal to noise ratio (~10 dB higher) and retaining the events related to signal (~3X more). In addition, for our envisioned application of object tracking and classification under some parameter settings, it can also generate some of the missing events in the spatial neighborhood of the signal for all classes of moving objects in the data which are unattainable using the nearest neighbor filter.
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