ETRI-Knowledge Sharing Plaform



논문 검색
구분 SCI
연도 ~ 키워드


학술지 Low-Power Computer Vision: Status, Challenges, and Opportunities
Cited 46 time in scopus Download 13 time Share share facebook twitter linkedin kakaostory
Sergei Alyamkin, Matthew Ardi, Alexander C. Berg, Achille Brighton, Bo Chen, Yiran Chen, Hsin-Pai Cheng, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Abhinav Goel, Alexander Goncharenko, Xuyang Guo, Soonhoi Ha, Andrew Howard, Xiao Hu, Yuanjun Huang, 강동현, 김재윤, 고종국, Alexander Kondratyev, 이준혁, 이승재, 이수웅, Zichao Li, Zhiyu Liang, Juzheng Liu, Xin Liu, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Hong Hanh Nguyen, 박은병, Denis Repin, Liang Shen, Tao Sheng, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo
IEEE Journal on Emerging and Selected Topics in Circuits and Systems, v.9 no.2, pp.411-421
19HS2300, 객체추출 및 실-가상 정합 지원 모바일 AR 기술 개발, 고종국
Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile phones, many autonomous systems rely on visual data for making decisions, and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots). These systems rely on batteries, and energy efficiency is critical. This paper serves the following two main purposes. First, examine the state of the art for low-power solutions to detect objects in images. Since 2015, the IEEE Annual International Low-Power Image Recognition Challenge (LPIRC) has been held to identify the most energy-efficient computer vision solutions. This paper summarizes the 2018 winners' solutions. Second, suggest directions for research as well as opportunities for low-power computer vision.
KSP 제안 키워드
Autonomous system, Computer Vision(CV), Energy Efficiency, Image Recognition Challenge, Low-Power, Mobile robots, Visual data, energy-efficient, mobile phone, state-of-The-Art, unmanned aerial vehicle