A self-supervised spiking neural network framework estimates optical flow from asynchronous underwater event streams without labeled data, achieving competitive accuracy with high computational efficiency.
Ultrafast dynamic defect inspection with computational neuromorphic imaging
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A dual-modal frame-event approach delivers real-time high-frame-rate binarization for silhouettes in dynamic scenes on CPU-only hardware by exploiting neuromorphic event data to reduce motion blur.
citing papers explorer
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Aquatic Neuromorphic Optical Flow
A self-supervised spiking neural network framework estimates optical flow from asynchronous underwater event streams without labeled data, achieving competitive accuracy with high computational efficiency.
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See Silhouettes in Motion with Neuromorphic Vision
A dual-modal frame-event approach delivers real-time high-frame-rate binarization for silhouettes in dynamic scenes on CPU-only hardware by exploiting neuromorphic event data to reduce motion blur.