# GNEP Based Dynamic Segmentation and Motion Estimation for Neuromorphic Imaging

arXiv: https://arxiv.org/abs/2307.02595 · HTML: https://mlsanity.com/paper/2307.02595/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [Focus Is All You Need: Loss Functions For Event-based Vision](https://mlsanity.com/paper/1904.07235/) (2019)
- [Event-Based Motion Segmentation by Motion Compensation](https://mlsanity.com/paper/1904.01293/) (2019)
- [Event-based Motion Segmentation with Spatio-Temporal Graph Cuts](https://mlsanity.com/paper/2012.08730/) (2020)
- [Globally Optimal Contrast Maximisation for Event-based Motion Estimation](https://mlsanity.com/paper/2002.10686/) (2020)
- [SpikeMS: Deep Spiking Neural Network for Motion Segmentation](https://mlsanity.com/paper/2105.06562/) (2021)
- [Simultaneous Optical Flow and Segmentation (SOFAS) using Dynamic Vision Sensor](https://mlsanity.com/paper/1805.12326/) (2018)
- [Independent Motion Detection with Event-driven Cameras](https://mlsanity.com/paper/1706.08713/) (2017)
- [An Existence Result for Hierarchical Stackelberg v/s Stackelberg Games](https://mlsanity.com/paper/1401.0186/) (2013)
- [Bilevel Inverse Problems in Neuromorphic Imaging](https://mlsanity.com/paper/2302.02052/) (2023)

## 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.