Event cameras offer significant advantages over traditional frame-based sensors, including higher temporal resolution, lower latency and dynamic range. However, efficiently converting event streams into formats compatible with standard computer vision pipelines remains a challenging problem, particularly in the presence of noise. In this paper, we propose Spike-TBR, a novel event-based encoding strategy based on Temporal Binary Representation (TBR), addressing its vulnerability to noise by integrating spiking neurons. Spike-TBR combines the frame-based advantages of TBR with the noise-filtering capabilities of spiking neural networks, creating a more robust representation of event streams. We evaluate four variants of Spike-TBR, each using different spiking neurons, across multiple datasets, demonstrating superior performance in noise-affected scenarios while improving the results on clean data. Our method bridges the gap between spike-based and frame-based processing, offering a simple noise-resilient solution for event-driven vision applications.
@article{arxiv.2506.04817,
title = {Spike-TBR: a Noise Resilient Neuromorphic Event Representation},
author = {Gabriele Magrini and Federico Becattini and Luca Cultrera and Lorenzo Berlincioni and Pietro Pala and Alberto Del Bimbo},
journal= {arXiv preprint arXiv:2506.04817},
year = {2025}
}