Bimodal objects, such as the checkerboard pattern used in camera calibration,
markers for object tracking, and text on road signs, to name a few, are
prevalent in our daily lives and serve as a visual form to embed information
that can be easily recognized by vision systems. While binarization from
intensity images is crucial for extracting the embedded information in the
bimodal objects, few previous works consider the task of binarization of blurry
images due to the relative motion between the vision sensor and the
environment. The blurry images can result in a loss in the binarization quality
and thus degrade the downstream applications where the vision system is in
motion. Recently, neuromorphic cameras offer new capabilities for alleviating
motion blur, but it is non-trivial to first deblur and then binarize the images
in a real-time manner. In this work, we propose an event-based binary
reconstruction method that leverages the prior knowledge of the bimodal
target's properties to perform inference independently in both event space and
image space and merge the results from both domains to generate a sharp binary
image. We also develop an efficient integration method to propagate this binary
image to high frame rate binary video. Finally, we develop a novel method to
naturally fuse events and images for unsupervised threshold identification. The
proposed method is evaluated in publicly available and our collected data
sequence, and shows the proposed method can outperform the SOTA methods to
generate high frame rate binary video in real-time on CPU-only devices.

提出了一个基于事件的二进制重建方法，利用双模态目标的属性先后在事件空间和图像空间中执行推理，并合并两个领域的结果以生成清晰的二进制图像。还开发了一种有效的集成方法，将该二进制图像传播到高帧速率的二进制视频中。最后，提出了一种新方法，用于自动识别阈值，将事件和图像自然融合起来。在公开可用和自己收集的数据序列中评估了该方法，并表明在仅使用 CPU 设备的实时环境中，该方法能够优于目前最先进的方法生成高帧速率的二进制视频。