Temporal information in event streams plays a critical role in event-based video frame interpolation as it provides temporal context cues complementary to images. Most previous event-based methods first transform the unstructured event data to structured data formats through voxelisation, and then employ advanced CNNs to extract temporal information.
To overcome calibration limitations, we propose eWand, a new method that uses blinking LEDs inside opaque spheres instead of a printed or displayed pattern. Our method provides a faster, easier-to-use extrinsic calibration approach that maintains high accuracy for both event- and frame-based cameras.
In this paper a new optical-computational method is introduced to unveil images of targets whose visibility is severely obscured by light scattering in dense, turbid media.
In this concept study, the processing steps required for this are discussed and suggestions for suitable processing methods are given. On the basis of a small dataset, a clustering and filtering-based labeling approach is proposed, which is a promising option for the preparation of larger DVS insect monitoring datasets.
We introduce EventLFM, a straightforward and cost-effective system that overcomes these challenges by integrating an event camera with Fourier light field microscopy (LFM), a state-of-the-art single-shot 3D wide-field imaging technique. We further develop a simple and robust event-driven LFM reconstruction algorithm that can reliably reconstruct 3D dynamics from the unique spatiotemporal measurements captured by EventLFM.