# On the Theory of Bias Tuning in Event Cameras

arXiv: https://arxiv.org/abs/2501.18788 · HTML: https://mlsanity.com/paper/2501.18788/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 14 of 14)

- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [Moving Object Detection for Event-based vision using Graph Spectral Clustering](https://mlsanity.com/paper/2109.14979/) (2021)
- [FA-Harris: A Fast and Asynchronous Corner Detector for Event Cameras](https://mlsanity.com/paper/1906.10925/) (2019)
- [Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking](https://mlsanity.com/paper/2002.05583/) (2020)
- [Shining light on the DVS pixel: A tutorial and discussion about biasing and optimization](https://mlsanity.com/paper/2304.04706/) (2023)
- [Feedback control of event cameras](https://mlsanity.com/paper/2105.00409/) (2021)
- [Unraveling the paradox of intensity-dependent DVS pixel noise](https://mlsanity.com/paper/2109.08640/) (2021)
- [Optimal biasing and physical limits of DVS event noise](https://mlsanity.com/paper/2304.04019/) (2023)
- [Memory-Efficient Graph Convolutional Networks for Object Classification and Detection with Event Cameras](https://mlsanity.com/paper/2307.14124/) (2023)
- [Control and Evaluation of Event Cameras Output Sharpness via Bias](https://mlsanity.com/paper/2210.13929/) (2022)
- [Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras](https://mlsanity.com/paper/2403.16425/) (2024)
- [Probabilistic Approach for Detection of High-Frequency Periodic Signals using an Event Camera](https://mlsanity.com/paper/2205.04691/) (2022)
- [Quantifying Noise of Dynamic Vision Sensor](https://mlsanity.com/paper/2404.01948/) (2024)
- [Optimal OnTheFly Feedback Control of Event Sensors](https://mlsanity.com/paper/2408.12976/) (2024)

## Built on by (most cited first, 1 of 1)

- [Event Driven Clustering Algorithm](https://mlsanity.com/paper/2602.00115/) (2026)

## Abstract (arXiv)

This paper lays the foundation of a theory for bias tuning in neuromorphic cameras, a novel sensing technology also known as "event cameras". We begin by formulating the high-level effect of the sensitivity biases on the camera's event rate in mathematical terms. We then show that, as a corollary of the Poincare-Miranda theorem, the commonly used tuning principles of rate budgeting and polarity balancing lead to a unique configuration of the sensitivity biases. As a corollary, we show how by adopting these principles, the multi-variable bias-tuning problem reduces to a two-parameter problem that can be resolved experimentally.