Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.03781 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- This technical report presents a novel DMD-based characterization method for vision sensors, particularly neuromorphic sensors such as event-based vision sensors (EVS) and Tianmouc, a complementary vision sensor. Traditional image sensor characterization standards, such as EMVA1288, are unsuitable for BVS due to their dynamic response characteristics. To address this, we propose a high-speed, high-precision testing system using a Digital Micromirror Device (DMD) to modulate spatial and temporal light intensity. This approach enables quantitative analysis of key parameters such as event latency, signal-to-noise ratio (SNR), and dynamic range (DR) under controlled conditions. Our method provides a standardized and reproducible testing framework, overcoming the limitations of existing evaluation techniques for neuromorphic sensors. Furthermore, we discuss the potential of this method for large-scale BVS dataset generation and conversion, paving the way for more consistent benchmarking of bio-inspired vision technologies.