Please use this identifier to cite or link to this item: https://doi.org/10.3389/fnins.2018.00118
DC FieldValue
dc.titleA noise filtering algorithm for event-based Asynchronous change detection image sensors on TrueNorth and its implementation on TrueNorth
dc.contributor.authorPadala, V
dc.contributor.authorBasu, A
dc.contributor.authorOrchard, G
dc.date.accessioned2020-10-30T02:09:15Z
dc.date.available2020-10-30T02:09:15Z
dc.date.issued2018
dc.identifier.citationPadala, V, Basu, A, Orchard, G (2018). A noise filtering algorithm for event-based Asynchronous change detection image sensors on TrueNorth and its implementation on TrueNorth. Frontiers in Neuroscience 12 (MAR) : 118. ScholarBank@NUS Repository. https://doi.org/10.3389/fnins.2018.00118
dc.identifier.issn16624548
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/182090
dc.description.abstractAsynchronous 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. © 2018 Padala, Basu and Orchard.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20201031
dc.subjectalgorithm
dc.subjectArticle
dc.subjectartificial neural network
dc.subjectdata processing
dc.subjecthuman
dc.subjectimage display
dc.subjectimage sensor
dc.subjectnoise filtering algorithm
dc.subjectnoise reduction
dc.subjectrefractory period
dc.subjectsignal noise ratio
dc.subjectsoftware
dc.subjectvideorecording
dc.typeArticle
dc.contributor.departmentTEMASEK LABORATORIES
dc.description.doi10.3389/fnins.2018.00118
dc.description.sourcetitleFrontiers in Neuroscience
dc.description.volume12
dc.description.issueMAR
dc.description.page118
Appears in Collections:Elements
Staff Publications

Show simple item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_3389_fnins_2018_00118.pdf3.18 MBAdobe PDF

OPEN

NoneView/Download

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons