Technical report of a DMD-based Characterization Method for Vision Sensors
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913720279498752 |
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| author | Meng, Yapeng Wang, Taoyi Lin, Yihan |
| author_facet | Meng, Yapeng Wang, Taoyi Lin, Yihan |
| 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_03781 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Technical report of a DMD-based Characterization Method for Vision Sensors Meng, Yapeng Wang, Taoyi Lin, Yihan Image and Video Processing 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. |
| title | Technical report of a DMD-based Characterization Method for Vision Sensors |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2503.03781 |