计算机科学
人工智能
图像处理
信号处理
视觉传感器网络
视觉处理
图像传感器
机器人学
计算机视觉
机器视觉
信息处理
数字图像处理
数字信号处理
感知
计算机硬件
机器人
图像(数学)
无线传感器网络中的密钥分配
无线网络
生物
电信
神经科学
无线
作者
Yanan Liu,Rui Fan,Jianglong Guo,Hepeng Ni,M. Usman Maqbool Bhutta
标识
DOI:10.34133/icomputing.0043
摘要
Conventional machine vision systems have separate perception, memory, and processing architectures, which may exacerbate the increasing need for ultrahigh image processing rates and ultralow power consumption. In contrast, in-sensor visual computing performs signal processing at the pixel level using the collected analog signals directly, without sending data to other processors. Therefore, the in-sensor computing paradigm may hold the key to realizing extremely efficient and low power visual signal processing by integrating sensing, storage, and computation onto focal planes using either novel circuit designs or new materials. The focal-plane sensor-processor (FPSP), which is a typical in-sensor visual computing device, is a vision chip that has been developed for nearly 2 decades in domains such as image processing, computer vision, robotics, and neural networks. In contrast to conventional computer vision systems, the FPSP gives vision systems in-sensor image processing capabilities, thus decreasing system complexity, reducing power consumption, and enhancing information processing efficiency and security. Although many studies on in-sensor computing using the FPSP have been conducted since its invention, no thorough and systematic summary of these studies exists. This review explains the use of image processing algorithms, neural networks, and applications of in-sensor computing in the fields of machine vision and robotics. The objective is to assist future developers, researchers, and users of unconventional visual sensors in understanding in-sensor computing and associated applications.
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