Neuromorphic (event-based) image sensors draw inspiration from the human-retina to create an electronic device that can process visual stimuli in a way that closely resembles its biological counterpart. These sensors process information significantly different than the traditional RGB sensors. Specifically, the sensory information generated by event-based image sensors are orders of magnitude sparser compared to that of RGB sensors. The first generation of neuromorphic image sensors, Dynamic Vision Sensor (DVS), are inspired by the computations confined to the photoreceptors and the first retinal synapse. In this work, we highlight the capability of the second generation of neuromorphic image sensors, Integrated Retinal Functionality in CMOS Image Sensors (IRIS), which aims to mimic full retinal computations from photoreceptors to output of the retina (retinal ganglion cells) for targeted feature-extraction. The feature of choice in this work is Object Motion Sensitivity (OMS) that is processed locally in the IRIS sensor. We study the capability of OMS in solving the ego-motion problem of the event-based cameras. Our results show that OMS can accomplish standard computer vision tasks with similar efficiency to conventional RGB and DVS solutions but offers drastic bandwidth reduction. This cuts the wireless and computing power budgets and opens up vast opportunities in high-speed, robust, energy-efficient, and low-bandwidth real-time decision making.

翻译：神经形态（基于事件）图像传感器受人类视网膜启发，构建出一种以高度仿生方式处理视觉刺激的电子器件。这类传感器的信息处理方式与传统RGB传感器存在本质差异：其产生的感官信息稀疏度比RGB传感器高出数个数量级。第一代神经形态图像传感器——动态视觉传感器（DVS）——灵感来源于光感受器与第一级视网膜突触所局限的计算。本研究聚焦第二代神经形态图像传感器——互补金属氧化物半导体图像传感器集成视网膜功能（IRIS）——的能力，该传感器旨在模拟从光感受器到视网膜输出（视网膜神经节细胞）的完整视网膜计算过程，实现目标特征提取。本文选定的特征是由IRIS传感器局部处理的物体运动敏感性（OMS）。我们研究了OMS在解决基于事件的相机自运动问题中的能力。实验结果表明，OMS能以与传统RGB和DVS方案相近的效率完成标准计算机视觉任务，但带宽消耗显著降低。这有效削减了无线传输与计算功耗预算，为高速、鲁棒、节能且低带宽的实时决策开辟了广阔前景。