GNEP Based Dynamic Segmentation and Motion Estimation for Neuromorphic Imaging
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 11 of its 28 references that are in the map. First posted on arXiv 5 Jul 2023.
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References · 11
- 2019Event-based Vision: A Surveycited by 1,013
- 2018A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimationcited by 178
- 2019Focus Is All You Need: Loss Functions For Event-based Visioncited by 83
- 2019Event-Based Motion Segmentation by Motion Compensationcited by 78
- 2020Event-based Motion Segmentation with Spatio-Temporal Graph Cutscited by 50
- 2020Globally Optimal Contrast Maximisation for Event-based Motion Estimationcited by 33
- 2021SpikeMS: Deep Spiking Neural Network for Motion Segmentationcited by 29
- 2018Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensorcited by 21
- 2017Independent Motion Detection with Event-driven Camerascited by 8
- 2013An Existence Result for Hierarchical Stackelberg v/s Stackelberg Gamescited by 6
- 2023Bilevel Inverse Problems in Neuromorphic Imagingcited by 4
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