GNEP Based Dynamic Segmentation and Motion Estimation for Neuromorphic Imaging
This addresses visual processing challenges for robotics or autonomous systems using novel camera technology, though it appears incremental in method.
The paper tackles image segmentation and motion estimation using event-based cameras by introducing a Generalized Nash Equilibrium framework that leverages temporal and spatial event stream data, demonstrating efficacy through experiments.
This paper explores the application of event-based cameras in the domains of image segmentation and motion estimation. These cameras offer a groundbreaking technology by capturing visual information as a continuous stream of asynchronous events, departing from the conventional frame-based image acquisition. We introduce a Generalized Nash Equilibrium based framework that leverages the temporal and spatial information derived from the event stream to carry out segmentation and velocity estimation. To establish the theoretical foundations, we derive an existence criteria and propose a multi-level optimization method for calculating equilibrium. The efficacy of this approach is shown through a series of experiments.