Event sensing is a major component in bio-inspired flight guidance and
control systems. We explore the usage of event cameras for predicting
time-to-contact (TTC) with the surface during ventral landing. This is achieved
by estimating divergence (inverse TTC), which is the rate of radial optic flow,
from the event stream generated during landing. Our core contributions are a
novel contrast maximisation formulation for event-based divergence estimation,
and a branch-and-bound algorithm to exactly maximise contrast and find the
optimal divergence value. GPU acceleration is conducted to speed up the global
algorithm. Another contribution is a new dataset containing real event streams
from ventral landing that was employed to test and benchmark our method. Owing
to global optimisation, our algorithm is much more capable at recovering the
true divergence, compared to other heuristic divergence estimators or
event-based optic flow methods. With GPU acceleration, our method also achieves
competitive runtimes.

使用事件相机和 GPU 加速，本研究提出一种用于预测生物发酵飞行朝向表面时刻距的新方法，包括一种新的对比度最大化公式和一种分支界定算法，并构建了一个新的真实事件数据集来测试和评估该方法的性能

全局最优事件驱动下降估计用于腹面降落

Globally Optimal Event-Based Divergence Estimation for Ventral Landing

Widefield calcium imaging has recently emerged as a powerful experimental
technique to record coordinated large-scale brain activity. These measurements
present a unique opportunity to characterize spatiotemporal coherent structures
that underlie neural activity across many regions of the brain. In this work,
we leverage analytic techniques from fluid dynamics to develop a visualization
framework that highlights features of flow across the cortex, mapping wave
fronts that may be correlated with behavioral events. First, we transform the
time series of widefield calcium images into time-varying vector fields using
optic flow. Next, we extract concise diagrams summarizing the dynamics, which
we refer to as FLOW (flow lines in optical widefield imaging) portraits. These
FLOW portraits provide an intuitive map of dynamic calcium activity, including
regions of initiation and termination, as well as the direction and extent of
activity spread. To extract these structures, we use the finite-time Lyapunov
exponent (FTLE) technique developed to analyze time-varying manifolds in
unsteady fluids. Importantly, our approach captures coherent structures that
are poorly represented by traditional modal decomposition techniques. We
demonstrate the application of FLOW portraits on three simple synthetic
datasets and two widefield calcium imaging datasets, including cortical waves
in the developing mouse and spontaneous cortical activity in an adult mouse.

本文将流体力学的分析技术应用于宽场钙成像数据的处理，将其转换为时间变量下的向量场，使用 optic flow 提取 cortex 中的活动规律，并结合流体力学的有限时间 Lyapunov 指数技术，得出了一幅直观的动态钙活动地图。

使用 FLOW：可视化光学宽场钙成像中的时空动态

Go with the FLOW: Visualizing spatiotemporal dynamics in optical  widefield calcium imaging

Event cameras are paradigm-shifting novel sensors that report asynchronous,
per-pixel brightness changes called 'events' with unparalleled low latency.
This makes them ideal for high speed, high dynamic range scenes where
conventional cameras would fail. Recent work has demonstrated impressive
results using Convolutional Neural Networks (CNNs) for video reconstruction and
optic flow with events. We present strategies for improving training data for
event based CNNs that result in 20-40% boost in performance of existing
state-of-the-art (SOTA) video reconstruction networks retrained with our
method, and up to 15% for optic flow networks. A challenge in evaluating event
based video reconstruction is lack of quality ground truth images in existing
datasets. To address this, we present a new High Quality Frames (HQF) dataset,
containing events and ground truth frames from a DAVIS240C that are
well-exposed and minimally motion-blurred. We evaluate our method on HQF +
several existing major event camera datasets.

本文介绍了一种用于提高事件基于 CNNs 的训练数据的策略，可为视频重建网络和光流网络带来 20-40％的性能提升，并提出了一种新的高质量数据集 HQF 以解决事件基于视频重建缺乏质量地真实图像的问题。