TY - CHAP
T1 - In-Sensor Visual Devices for Perception and Inference
AU - Liu, Yanan
AU - Ni, Hepeng
AU - Yuwen, Chao
AU - Yang, Xinyu
AU - Ming, Yuhang
AU - Zhong, Huixin
AU - Lu, Yao
AU - Ran, Liang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.
PY - 2023
Y1 - 2023
N2 - The traditional machine vision systems use separate architectures for perception, memory, and processing. This approach may hinder the growing demand for high image processing rates and low power consumption. On the other hand, in-sensor computing performs signal processing at the pixel level, directly utilizing collected analogue signals without sending them to other processors. This means that in-sensor computing may offer a solution for achieving highly efficient and low-power consumption visual signal processing. This can be achieved by integrating sensing, storage, and computation onto focal planes with novel circuit designs or new materials. This chapter aims to describe the proposed image processing algorithms and neural networks of in-sensor computing, as well as their applications in machine vision and robotics. The goal of this chapter is to help developers, researchers, and users of unconventional visual sensors understand their functioning and applications, especially in the context of autonomous driving.
AB - The traditional machine vision systems use separate architectures for perception, memory, and processing. This approach may hinder the growing demand for high image processing rates and low power consumption. On the other hand, in-sensor computing performs signal processing at the pixel level, directly utilizing collected analogue signals without sending them to other processors. This means that in-sensor computing may offer a solution for achieving highly efficient and low-power consumption visual signal processing. This can be achieved by integrating sensing, storage, and computation onto focal planes with novel circuit designs or new materials. This chapter aims to describe the proposed image processing algorithms and neural networks of in-sensor computing, as well as their applications in machine vision and robotics. The goal of this chapter is to help developers, researchers, and users of unconventional visual sensors understand their functioning and applications, especially in the context of autonomous driving.
UR - http://www.scopus.com/inward/record.url?scp=85175176319&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-4287-9_1
DO - 10.1007/978-981-99-4287-9_1
M3 - Chapter
AN - SCOPUS:85175176319
T3 - Advances in Computer Vision and Pattern Recognition
SP - 1
EP - 35
BT - Advances in Computer Vision and Pattern Recognition
PB - Springer Science and Business Media Deutschland GmbH
ER -