# Control and Evaluation of Event Cameras Output Sharpness via Bias

arXiv: https://arxiv.org/abs/2210.13929 · HTML: https://mlsanity.com/paper/2210.13929/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 7 of 7)

- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras](https://mlsanity.com/paper/2003.08282/) (2020)
- [Real-Time Face & Eye Tracking and Blink Detection using Event Cameras](https://mlsanity.com/paper/2010.08278/) (2020)
- [Event Camera Calibration of Per-pixel Biased Contrast Threshold](https://mlsanity.com/paper/2012.09378/) (2020)
- [Feedback control of event cameras](https://mlsanity.com/paper/2105.00409/) (2021)
- [Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras](https://mlsanity.com/paper/2002.11656/) (2020)
- [Utility and Feasibility of a Center Surround Event Camera](https://mlsanity.com/paper/2202.13076/) (2022)

## Built on by (most cited first, 10 of 10)

- [Enhancing Visual Place Recognition via Fast and Slow Adaptive Biasing in Event Cameras](https://mlsanity.com/paper/2403.16425/) (2024)
- [A PyTorch-Enabled Tool for Synthetic Event Camera Data Generation and Algorithm Development](https://mlsanity.com/paper/2503.09754/) (2025)
- [Heart Rate Detection Using an Event Camera](https://mlsanity.com/paper/2309.11891/) (2023)
- [Autobiasing Event Cameras](https://mlsanity.com/paper/2411.00729/) (2024)
- [Optimal OnTheFly Feedback Control of Event Sensors](https://mlsanity.com/paper/2408.12976/) (2024)
- [On the Theory of Bias Tuning in Event Cameras](https://mlsanity.com/paper/2501.18788/) (2025)
- [BiasBench: A reproducible benchmark for tuning the biases of event cameras](https://mlsanity.com/paper/2504.18235/) (2025)
- [Decisive Data using Multi-Modality Optical Sensors for Advanced Vehicular Systems](https://mlsanity.com/paper/2307.13600/) (2023)
- [Distance Estimation in Outdoor Driving Environments Using Phase-only Correlation Method with Event Cameras](https://mlsanity.com/paper/2505.17582/) (2025)
- [Autobiasing Event Cameras for Flickering Mitigation](https://mlsanity.com/paper/2511.02180/) (2025)

## Abstract (arXiv)

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.