1 research outputs found
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
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 labelling 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.Comment: 10 pages, 6 figures in Frontiers in Neuromorphic Engineering, special
topic on Benchmarks and Challenges for Neuromorphic Engineering, 2015 (under
review