Control and Evaluation of Event Cameras Output Sharpness via Bias

TL;DR

This paper investigates how adjusting sensor bias settings in event cameras affects output sharpness, using image gradient analysis to quantify the impact and linking it to the underlying circuit model.

Contribution

It provides a detailed analysis of bias setting effects on event camera output quality and introduces a method to evaluate sharpness changes through gradient magnitude.

Findings

Bias settings significantly influence event stream sharpness.

Event output quality can be controlled via sensor bias adjustments.

A circuit model explains the bias effects on sensing array behavior.

Abstract

Event cameras also known as neuromorphic sensors are relatively a new technology with some privilege over the RGB cameras. The most important one is their difference in capturing the light changes in the environment, each pixel changes independently from the others when it captures a change in the environment light. To increase the users degree of freedom in controlling the output of these cameras, such as changing the sensitivity of the sensor to light changes, controlling the number of generated events and other similar operations, the camera manufacturers usually introduce some tools to make sensor level changes in camera settings. The contribution of this research is to examine and document the effects of changing the sensor settings on the sharpness as an indicator of quality of the generated stream of event data. To have a qualitative understanding this stream of event is converted to frames, then the average image gradient magnitude as an index of the number of edges and accordingly sharpness is calculated for these frames. Five different bias settings are explained and the effect of their change in the event output is surveyed and analyzed. In addition, the operation of the event camera sensing array is explained with an analogue circuit model and the functions of the bias foundations are linked with this model.