Visual Fatigue Assessment Based on Multi-task Learning | |
Wang, Danli; Wang, Xueyu; Song, Yaguang; Xing, Qian![]() ![]() | |
发表期刊 | JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY
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ISSN | 1062-3701 |
2019-11-01 | |
卷号 | 63期号:6页码:1-8 |
摘要 | In recent years, with the rapid development of stereoscopic display technology, its applications have become increasingly popular in many fields, and, meanwhile, the number of audiences is also growing. The problem of visual fatigue is becoming more and more prominent. Visual fatigue is mainly caused by vergence-accommodation conflicts. An evaluation experiment was conducted, and the electroencephalogram (EEG) data of the subjects were collected when they were watching stereoscopic content, and then the stereoscopic fatigue state of the subjects during the viewing process was analyzed. As deep learning is proved to be an effective end-to-end learning method and multi-task learning can alleviate the problem of lacking annotated data, the authors establish a user visual fatigue assessment model based on EEG by using multi-task learning, which can effectively obtain the user's visual fatigue status, so as to make the comfort designs to avoid the harm caused by user's visual fatigue. (C) 2019 Society for Imaging Science and Technology. |
DOI | 10.2352/J.ImagingSci.Technol.2019.63.6.060414 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61872363] ; National Natural Science Foundation of China[61562063] ; National Natural Science Foundation of China[61272325] ; National Natural Science Foundation of China[61672507] ; National Key Research and Development Program[2016YFB0401202] ; National Natural Science Foundation of China[61501463] ; National Natural Science Foundation of China[61501463] ; National Key Research and Development Program[2016YFB0401202] ; National Natural Science Foundation of China[61672507] ; National Natural Science Foundation of China[61272325] ; National Natural Science Foundation of China[61562063] ; National Natural Science Foundation of China[61872363] |
WOS研究方向 | Imaging Science & Photographic Technology |
WOS类目 | Imaging Science & Photographic Technology |
WOS记录号 | WOS:000508022200015 |
出版者 | I S & T-SOC IMAGING SCIENCE TECHNOLOGY |
七大方向——子方向分类 | 人工智能+科学 |
国重实验室规划方向分类 | AI For Science |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/29538 |
专题 | 复杂系统管理与控制国家重点实验室 |
通讯作者 | Wang, Danli |
作者单位 | Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wang, Danli,Wang, Xueyu,Song, Yaguang,et al. Visual Fatigue Assessment Based on Multi-task Learning[J]. JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY,2019,63(6):1-8. |
APA | Wang, Danli,Wang, Xueyu,Song, Yaguang,Xing, Qian,&Zheng, Nan.(2019).Visual Fatigue Assessment Based on Multi-task Learning.JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY,63(6),1-8. |
MLA | Wang, Danli,et al."Visual Fatigue Assessment Based on Multi-task Learning".JOURNAL OF IMAGING SCIENCE AND TECHNOLOGY 63.6(2019):1-8. |
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