IEEE Trans Neural Netw Learn Syst. 2023 Apr;34(4):1742-1753. doi: 10.1109/TNNLS.2021.3061122. Epub 2023 Apr 4.
Event cameras as bioinspired vision sensors have shown great advantages in high dynamic range and high temporal resolution in vision tasks. Asynchronous spikes from event cameras can be depicted using the marked spatiotemporal point processes (MSTPPs). However, how to measure the distance between asynchronous spikes in the MSTPPs still remains an open issue. To address this problem, we propose a general asynchronous spatiotemporal spike metric considering both spatiotemporal structural properties and polarity attributes for event cameras. Technically, the conditional probability density function is first introduced to describe the spatiotemporal distribution and polarity prior in the MSTPPs. Besides, a spatiotemporal Gaussian kernel is defined to capture the spatiotemporal structure, which transforms discrete spikes into the continuous function in a reproducing kernel Hilbert space (RKHS). Finally, the distance between asynchronous spikes can be quantified by the inner product in the RKHS. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art methods and achieves significant improvement in computational efficiency. Especially, it is able to better depict the changes involving spatiotemporal structural properties and polarity attributes.
事件相机作为生物启发的视觉传感器，在视觉任务中的高动态范围和高时间分辨率方面显示出了巨大的优势。事件相机的异步尖峰可以使用标记时空点过程 (MSTPP) 来描述。然而，如何测量 MSTPP 中异步尖峰之间的距离仍然是一个悬而未决的问题。为了解决这个问题，我们提出了一种通用的异步时空尖峰度量方法，该方法同时考虑了事件相机的时空结构特性和极性属性。从技术上讲，首先引入条件概率密度函数来描述 MSTPP 中的时空分布和极性先验。此外，定义了一个时空高斯核来捕获时空结构，它将离散的尖峰转换为再生核希尔伯特空间 (RKHS) 中的连续函数。最后，通过 RKHS 中的内积来量化异步尖峰之间的距离。实验结果表明，所提出的方法优于最新方法，并在计算效率方面取得了显著提高。特别是，它能够更好地描述涉及时空结构特性和极性属性的变化。
