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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.02432 |
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| _version_ | 1866913186466234368 |
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| author | Xie, Zichen Wang, Ken Xingze |
| author_facet | Xie, Zichen Wang, Ken Xingze |
| contents | We show a monotonic relationship between performances of various computer vision tasks versus degrees of coherence of illumination. We simulate partially coherent illumination using computational methods, propagate the lightwave to form images, and subsequently employ a deep neural network to perform object recognition and depth sensing tasks. In each controlled experiment, we discover that, increased coherent length leads to improved image entropy, as well as enhanced object recognition and depth sensing performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02432 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Partial Coherence for Object Recognition and Depth Sensing Xie, Zichen Wang, Ken Xingze Computer Vision and Pattern Recognition Optics We show a monotonic relationship between performances of various computer vision tasks versus degrees of coherence of illumination. We simulate partially coherent illumination using computational methods, propagate the lightwave to form images, and subsequently employ a deep neural network to perform object recognition and depth sensing tasks. In each controlled experiment, we discover that, increased coherent length leads to improved image entropy, as well as enhanced object recognition and depth sensing performance. |
| title | Partial Coherence for Object Recognition and Depth Sensing |
| topic | Computer Vision and Pattern Recognition Optics |
| url | https://arxiv.org/abs/2401.02432 |