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.
翻译:事件感应器也称为神经形态感应器,它相对而言是一种新技术,比 RGB 摄像头具有一定的特权。 最重要的一项研究是,在捕捉环境中的光变化方面,它们的差异在于捕捉环境中的光的变化,当它捕捉到环境光的变化时,每个像素的变化与其它变化不同。为了提高用户控制这些摄像头输出的自由度,例如改变感应器对光变化的敏感度,控制生成事件的数量和其他类似操作,摄像头制造商通常引入一些工具,在相机设置中使感应级别发生变化。这项研究的贡献是检查和记录感测器设置改变对清晰度的影响,作为所生成事件数据流质量的指标。为了对事件流的质量有质的理解,然后将这种事件流转换为框架,将平均图像梯度作为边缘数的指数,并据此计算这些框架的锐度。对五个不同的偏差设置进行了解释,并对事件输出的变化效果进行了研究和分析。此外,事件摄影感测阵的操作用模拟电路模型加以解释,偏见根的功能与这个模型相联系。