Adaptive Unsupervised Learning-Based 3D Spatiotemporal Filter for Event-Driven Cameras.

ABSTRACT: In the evolving landscape of robotics and visual navigation, event cameras have gained important traction, notably for their exceptional dynamic range, efficient power consumption, and low latency. Despite these advantages, conventional processing methods oversimplify the data into 2 dimensions, neglecting critical temporal information. To overcome this limitation, we propose a novel method that treats events as 3D time-discrete signals. Drawing inspiration from the intricate biological filtering systems inherent to the human visual apparatus, we have developed a 3D spatiotemporal filter based on unsupervised machine learning algorithm. This filter effectively reduces noise levels and performs data size reduction, with its parameters being dynamically adjusted based on population activity.

SUBMITTER: Miled MB 

PROVIDER: S-EPMC10981976 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature