IEEE Trans Image Process. 2017 May;26(5):2192-2202. doi: 10.1109/TIP.2017.2661702. Epub 2017 Feb 6.
This paper introduces a method to compute the FFT of a visual scene at a high temporal precision of around 1- [Formula: see text] output from an asynchronous event-based camera. Event-based cameras allow to go beyond the widespread and ingrained belief that acquiring series of images at some rate is a good way to capture visual motion. Each pixel adapts its own sampling rate to the visual input it receives and defines the timing of its own sampling points in response to its visual input by reacting to changes of the amount of incident light. As a consequence, the sampling process is no longer governed by a fixed timing source but by the signal to be sampled itself, or more precisely by the variations of the signal in the amplitude domain. Event-based cameras acquisition paradigm allows to go beyond the current conventional method to compute the FFT. The event-driven FFT algorithm relies on a heuristic methodology designed to operate directly on incoming gray level events to update incrementally the FFT while reducing both computation and data load. We show that for reasonable levels of approximations at equivalent frame rates beyond the millisecond, the method performs faster and more efficiently than conventional image acquisition. Several experiments are carried out on indoor and outdoor scenes where both conventional and event-driven FFT computation is shown and compared.
本文介绍了一种方法，可以在 1- [Formula: see text] 的高时间精度下计算视觉场景的 FFT，输出来自异步事件相机。事件相机打破了人们广泛而根深蒂固的观念，即按照一定的速率获取一系列图像是捕捉视觉运动的一种好方法。每个像素都根据其接收到的视觉输入来调整自己的采样率，并通过对入射光量变化的反应来定义自己的采样点的时间，从而实现自适应采样。因此，采样过程不再由固定的定时源控制，而是由要采样的信号本身控制，或者更准确地说，由信号在幅度域中的变化控制。事件相机采集范式超越了当前计算 FFT 的常规方法。事件驱动的 FFT 算法依赖于一种启发式方法，旨在直接对输入的灰度级事件进行操作，以增量方式更新 FFT，同时减少计算和数据负载。我们表明，在等效帧率超过毫秒的情况下，对于合理的近似水平，该方法比传统的图像采集更快、更高效。在室内和室外场景中进行了多项实验，展示和比较了传统和事件驱动的 FFT 计算。
