基于时空密度和时间序列分析的事件流否定方法
Haiyan Jiang1, Xiaoshuang Wang1, Wei Tang1
1College of Intelligent Equipment, Shandong University of Science and Technology, Tai'an 271000, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
这项研究引入了一种新的算法,用于减少事件摄像头数据中的噪声. 该方法有效地过噪音并产生清晰的事件框架,改善神经模拟传感器的数据质量.
科学领域:
- 神经形态工程的神经形态工程
- 计算机视觉 计算机视觉
- 信号处理 信号处理
背景情况:
- 事件摄像机提供高动态范围,时间分辨率和低功耗,模仿人类视网膜成像.
- 由于硬件/软件因素,事件流往往含有显著的噪音,使传统的无声化方法变得无效.
- 强大的降噪对于利用事件摄像机功能至关重要.
研究的目的:
- 开发一个事件流降噪和可视化算法,适应各种噪音类型.
- 为了提高从噪音事件流中生成的事件框架的清晰度和连贯性.
- 为了解决现有的事件摄像头数据无声化技术的局限性.
主要方法:
- 一种两阶段的过方法:对背景/添加 (BA) 噪声进行初始基于时空密度的过,然后进行细.
- 精细过采用事件像素及其邻居的时间序列分析来消除热噪声.
- 一个可视化算法适应地重叠事件基于密度差异的框架连贯性.
主要成果:
- 拟议的算法有效地减少了来自现实世界的场景和公共数据集的事件流中的噪音.
- 清晰和连贯的事件框架被成功地生成,即使在不同的事件密度和噪音水平下.
- 实验验证证证实了算法的稳定性和有效性在denoising和可视化.
结论:
- 开发的算法提供了一种有效的解决方案,用于降低事件摄像头数据中的噪声.
- 该方法提高了事件流的质量,使得下游应用程序更可靠.
- 这项工作有助于在各种条件下实践使用事件摄像机.
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