基于事件的以背景为导向的Schlieren
IEEE transactions on pattern analysis and machine intelligence
|October 30, 2023
概括
这项研究引入了一种使用事件和摄像头进行空气对流成像的新方法,克服了传统技术的局限性. 这种方法可以在黑暗条件下获得清晰的图像,并进行详细的慢动作分析.
科学领域:
- 光学物理学的光学物理学
- 流体动力学 流体动力学
- 计算机视觉 计算机视觉
背景情况:
- 施莱伦成像可视化透明的媒体流没有播种.
- 传统方法需要高分辨率的摄像头,限制照明和计算.
- 事件摄像机提供高动态范围,时间分辨率和数据效率.
研究的目的:
- 开发一种新的技术,使用事件和摄像头来感知空气对流.
- 提供第一个理论分析,将事件数据和施莱伦成像连接起来.
- 克服传统的基于框架的schlieren方法的局限性.
主要方法:
- 作为一个变量优化问题制定.
- 结合了线性事件生成模型与物理动机的参数化.
- 估计了空气密度的时间导数.
主要成果:
- 使用事件摄像头实现了与基于的光流技术相似的结果.
- 在黑暗条件下启用成像,而基于的schlieren则失败.
- 通过利用事件摄像机的优势,促进了慢动作分析.
结论:
- 开创了流体动力学事件摄像机的新应用堆.
- 展示了事件摄像机在增强schlieren成像方面的潜力.
- 发布了源代码和第一个高质量的框架/事件数据集.
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