Our model outperforms by a large margin feed-forward event-based architectures. Moreover, our method does not require any reconstruction of intensity images from events, showing that training directly from raw events is possible, more efficient, and more accurate than passing through an intermediate intensity image.
In this work, we present experimental results of a high-speed label-free imaging cytometry system that seamlessly merges the high-capturing rate and data sparsity of an event-based CMOS camera with lightweight photonic neuromorphic processing. The results confirm that neuromorphic sensing and neuromorphic computing can be efficiently merged to a unified bio-inspired system, offering a holistic enhancement in emerging bio-imaging applications.
In this work, we detail the method for generating accurate S-curves by applying an appropriate stimulus and sensor configuration to decouple 2nd-order effects from the parameter being studied. We use an EVS pixel simulation to demonstrate how noise and other physical constraints can lead to error in the measurement, and develop two techniques that are robust enough to obtain accurate estimates.
We introduce a sensor fusion framework to combine single-photon avalanche diodes (SPADs) with event cameras to improve the reconstruction of high-speed, low-light scenes while reducing the high bandwidth cost associated with using every SPAD frame. Our evaluation, on both synthetic and real sensor data, demonstrates significant enhancements (> 5 dB PSNR) in reconstructing low-light scenes at high temporal resolution (100 kHz) compared to conventional cameras. Event-SPAD fusion shows great promise for real-world applications, such as robotics or medical imaging.
This paper introduces a visual real-time insect monitoring approach capable of detecting and tracking tiny and fast-moving objects in cluttered wildlife conditions using an RGB-DVS stereo-camera system. Our study suggests that DVS-based sensing can be used for visual insect monitoring by enabling reliable real-time insect detection in wildlife conditions while significantly reducing the necessity for data storage, manual labour and energy.