论文标题
使用自调整阈值的动态视觉传感器的噪声过滤器
A Noise Filter for Dynamic Vision Sensors using Self-adjusting Threshold
论文作者
论文摘要
基于神经形态事件的动态视觉传感器（DVS）比基于框架的成像器具有更快的采样率和更高的动态范围。但是，它们对不需要的背景活动（BA）事件敏感。我们通过使用全球空间和时间信息而不是高斯卷积的本地信息，提出了一个很少的计算开销的新标准，用于定义真实事件和BA事件，这也可以用作过滤器。我们将过滤器表示为GF。我们在三个数据集上演示了GF，每个数据集由不同输出大小的不同DV记录。实验结果表明，与基线过滤器相比，我们的过滤器产生最清晰的帧并快速运行。
Neuromorphic event-based dynamic vision sensors (DVS) have much faster sampling rates and a higher dynamic range than frame-based imagers. However, they are sensitive to background activity (BA) events which are unwanted. we propose a new criterion with little computation overhead for defining real events and BA events by utilizing the global space and time information rather than the local information by Gaussian convolution, which can be also used as a filter. We denote the filter as GF. We demonstrate GF on three datasets, each recorded by a different DVS with different output size. The experimental results show that our filter produces the clearest frames compared with baseline filters and run fast.
