  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.

Computer Vision and Pattern Recognition