Converting static image datasets to spiking neuromorphic datasets using saccades

Garrick Orchard, Ajinkya Jayawant, Gregory K. Cohen, Nitish Thakor

    Research output: Contribution to journalArticlepeer-review

    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.
    Original languageEnglish
    Article number437
    Number of pages11
    JournalFrontiers in Neuroscience
    Volume9
    DOIs
    Publication statusPublished - 2015

    Keywords

    • computer vision
    • image processing
    • neuromorphics
    • saccadic eye movements

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