Please use this identifier to cite or link to this item: https://doi.org/10.3389/fnins.2015.00437
Title: Converting static image datasets to spiking neuromorphic datasets using saccades
Authors: Orchard, G 
Jayawant, A
Cohen, G.K
Thakor, N 
Keywords: Article
artifact reduction
artificial neural network
computer
computer program
image processing
mathematical analysis
motion
neuromorphic vision
saccadic eye movement
sensor
simulation
spike wave
Issue Date: 2015
Citation: Orchard, G, Jayawant, A, Cohen, G.K, Thakor, N (2015). Converting static image datasets to spiking neuromorphic datasets using saccades. Frontiers in Neuroscience 9 (NOV) : 437. ScholarBank@NUS Repository. https://doi.org/10.3389/fnins.2015.00437
Rights: Attribution 4.0 International
Abstract: Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specifically for dataset creation rather than collecting and labeling existing data. The task is further complicated by a desire to simultaneously provide traditional frame-based recordings to allow for direct comparison with traditional Computer Vision algorithms. Here we propose a method for converting existing Computer Vision static image datasets into Neuromorphic Vision datasets using an actuated pan-tilt camera platform. Moving the sensor rather than the scene or image is a more biologically realistic approach to sensing and eliminates timing artifacts introduced by monitor updates when simulating motion on a computer monitor. We present conversion of two popular image datasets (MNIST and Caltech101) which have played important roles in the development of Computer Vision, and we provide performance metrics on these datasets using spike-based recognition algorithms. This work contributes datasets for future use in the field, as well as results from spike-based algorithms against which future works can compare. Furthermore, by converting datasets already popular in Computer Vision, we enable more direct comparison with frame-based approaches. © 2015 Orchard, Jayawant, Cohen and Thakor.
Source Title: Frontiers in Neuroscience
URI: https://scholarbank.nus.edu.sg/handle/10635/183592
ISSN: 16624548
DOI: 10.3389/fnins.2015.00437
Rights: Attribution 4.0 International
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