论文标题
使用事件摄像机检测高频周期性信号的概率方法
Probabilistic Approach for Detection of High-Frequency Periodic Signals using an Event Camera
论文作者
论文摘要
事件摄像机受到生物眼的启发，是一种新型的异步技术，在视觉信息获取时构成了范式转移。与经典摄像机相比，该范式使事件摄像机可以更自然地捕获像素尺寸的快速运动。 在本文中，我们提出了一种新的异步事件驱动算法，用于使用事件摄像机检测高频像素大小的周期性信号。为了有效地处理事件摄像机的异步信息，开发这种新算法是必不可少的，并且是研究界的主要挑战，以利用其特殊属性和潜力。 事实证明，为了满足新范式而开发的这种算法与概率未经处理的理论问题有关：让$ 0 \leqτ_{1} \leqτ_{2} \ leq \ leq \ cdots \ cdots \ cdots \ cdots \ cdots \ leq感{m} {m} \ leq1 $，来自一个已知分配。也让$ε，δ\ in \ mathbb {r} $和$ d \ in \ mathbb {n} $。关于具有超过$ d $的概率$φ（m，d）的概率可以说什么，相邻$τ_{i} $ - 对它们之间的距离为$δ$，最多可误差$ε$？这个问题提醒订单统计数据，它表明了新的可视化范式如何也是开发数学新领域和问题的机会。
Being inspired by the biological eye, event camera is a novel asynchronous technology that pose a paradigm shift in acquisition of visual information. This paradigm enables event cameras to capture pixel-size fast motions much more naturally compared to classical cameras. In this paper we present a new asynchronous event-driven algorithm for detection of high-frequency pixel-size periodic signals using an event camera. Development of such new algorithms, to efficiently process the asynchronous information of event cameras, is essential and being a main challenge in the research community, in order to utilize its special properties and potential. It turns out that this algorithm, that was developed in order to satisfy the new paradigm, is related to an untreated theoretical problem in probability: let $0\leqτ_{1}\leqτ_{2}\leq\cdots\leqτ_{m}\leq1$, originated from an unknown distribution. Let also $ε,δ\in\mathbb{R}$, and $d\in\mathbb{N}$. What can be said about the probability $Φ(m,d)$ of having more than $d$ adjacent $τ_{i}$-s pairs that the distance between them is $δ$, up to an error $ε$ ? This problem, that reminds the area of order statistic, shows how the new visualization paradigm is also an opportunity to develop new areas and problems in mathematics.
