Spike-TBR: a Noise Resilient Neuromorphic Event Representation

• URL: http://arxiv.org/abs/2506.04817v2
• Date: Thu, 12 Jun 2025 09:39:54 GMT
• Title: Spike-TBR: a Noise Resilient Neuromorphic Event Representation
• Authors: Gabriele Magrini, Federico Becattini, Luca Cultrera, Lorenzo Berlincioni, Pietro Pala, Alberto Del Bimbo,
• Abstract summary: We propose Spike-TBR, a novel event-based encoding strategy based on Temporal Binary Representation (TBR)<n>Spike-TBR combines the frame-based advantages with the noise-filtering capabilities of spiking neural networks, creating a more robust representation of event streams.<n>We evaluate four variants of Spike-TBR, each using different spiking neurons, across multiple datasets, demonstrating superior performance in noise-affected scenarios.
• Score: 24.5708895410029
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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