Data Augmentation in Temporal and Polar Domains for Event-Based Learning. (arXiv:2207.11659v2 [cs.CV] UPDATED)

Event cameras are inherently suitable for spikingneural networks (SNNS) and
have great potential in challenging scenesdue to the advantages of bionics,
asynchrony, high dynamic range, and no motion blur.However, novel data
augmentations designed for event properties are required to process the
unconventional output of these cameras in order to unlock their potential.In
this paper, we explore the extraordinary influence of brightness variations due
to event properties. Along the way, two novel data augmentation methods,
lemph[EventInvert) and lemph(EventDrift) (EventID), are proposedto …

arxiv augmentation data event polar temporal