On the Theory of Bias Tuning in Event Cameras

TL;DR

This paper develops a theoretical framework for bias tuning in event cameras, demonstrating that common tuning principles lead to a unique bias configuration and simplifying the tuning process to a two-parameter problem.

Contribution

It introduces a mathematical theory for bias tuning in event cameras and shows how traditional principles ensure a unique bias configuration, simplifying the tuning process.

Findings

Bias tuning principles lead to a unique configuration.

Multi-variable bias tuning reduces to a two-parameter problem.

Theoretical foundation for bias adjustment in neuromorphic cameras.

Abstract

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.