Recent advances in retinal neuroscience have fueled various hardware and algorithmic efforts to develop retina-inspired solutions for computer vision tasks. In this work, we focus on a fundamental visual feature within the mammalian retina, Object Motion Sensitivity (OMS). Using DVS data from EV-IMO dataset, we analyze the performance of an algorithmic implementation of OMS circuitry for motion segmentation in presence of ego-motion. This holistic analysis considers the underlying constraints arising from the hardware circuit implementation. We present novel CMOS circuits that implement OMS functionality inside image sensors, while providing run-time re-configurability for key algorithmic parameters. In-sensor technologies for dynamical environment adaptation are crucial for ensuring high system performance. Finally, we verify the functionality and re-configurability of the proposed CMOS circuit designs through Cadence simulations in 180nm technology. In summary, the presented work lays foundation for hardware-algorithm re-engineering of known biological circuits to suit application needs.

翻译：视网膜神经科学的最新进展推动了多种硬件与算法研究，致力于开发受视网膜启发的计算机视觉解决方案。本研究聚焦于哺乳动物视网膜中的一项基础视觉特征——目标运动敏感性（OMS）。利用EV-IMO数据集中的DVS数据，我们分析了在存在自身运动情况下OMS电路算法实现对于运动分割的性能。该整体性分析考虑了硬件电路实现所产生的底层约束。我们提出了一种新型CMOS电路，可在图像传感器内部实现OMS功能，同时为关键算法参数提供运行时重配置能力。面向动态环境适应的传感器内技术对于确保系统高性能至关重要。最后，我们通过180nm工艺下的Cadence仿真验证了所提出CMOS电路设计的功能性与重配置能力。综上所述，本研究为已知生物电路的硬件-算法重构以适应应用需求奠定了基础。