CASIA OpenIR

浏览/检索结果: 共16条,第1-10条 帮助

限定条件    
已选(0)清除 条数/页:   排序方式:
Mass Image Synthesis in Mammogram with Contextual Information Based on GANs 期刊论文
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2021, 卷号: 202, 期号: 2021, 页码: 9
作者:  Shen, Tianyu;  Hao, Kunkun;  Gou, Chao;  Wang, Fei-Yue
Adobe PDF(2029Kb)  |  收藏  |  浏览/下载:286/49  |  提交时间:2021/05/17
medical image synthesis  generative adversarial network  mammogram  mass detection  
Joint image-to-image translation with denoising using enhanced generative adversarial networks 期刊论文
SIGNAL PROCESSING-IMAGE COMMUNICATION, 2021, 卷号: 91, 页码: 9
作者:  Yan, Lan;  Zheng, Wenbo;  Wang, Fei-Yue;  Gou, Chao
Adobe PDF(4437Kb)  |  收藏  |  浏览/下载:265/41  |  提交时间:2021/03/01
Image-to-image translation  Generative adversarial networks  Image enhancement  Image denoising  
Data Augmented Deep Behavioral Cloning for Urban Traffic Control Operations Under a Parallel Learning Framework 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2021, 卷号: 23, 期号: 6, 页码: 5128-5137
作者:  Li, Xiaoshuang;  Ye, Peijun;  Jin, Junchen;  Zhu, Fenghua;  Wang, Fei-Yue
Adobe PDF(2319Kb)  |  收藏  |  浏览/下载:267/51  |  提交时间:2022/01/27
Generative adversarial networks  Data models  Gallium nitride  Task analysis  Complex systems  Intelligent traffic signal operations  deep behavioral cloning  
A parallel vision approach to scene-specific pedestrian detection 期刊论文
NEUROCOMPUTING, 2020, 卷号: 394, 页码: 114-126
作者:  Zhang, Wenwen;  Wang, Kunfeng;  Liu, Yating;  Lu, Yue;  Wang, Fei-Yue
Adobe PDF(4090Kb)  |  收藏  |  浏览/下载:309/46  |  提交时间:2020/06/22
Pedestrian detection  Specific scene  Synthetic data  Video surveillance  Parallel vision  
Simultaneous Segmentation and Classification of Mass Region From Mammograms Using a Mixed-Supervision Guided Deep Model 期刊论文
IEEE SIGNAL PROCESSING LETTERS, 2020, 卷号: 27, 期号: 0, 页码: 196-200
作者:  Shen, Tianyu;  Gou, Chao;  Wang, Jiangong;  Wang, Fei-Yue
Adobe PDF(1640Kb)  |  收藏  |  浏览/下载:298/49  |  提交时间:2020/03/30
Mixed-supervision  deep learning  segmentation and classification  mammogram  
Hierarchical Fused Model With Deep Learning and Type-2 Fuzzy Learning for Breast Cancer Diagnosis 期刊论文
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2020, 卷号: 28, 期号: 12, 页码: 3204-3218
作者:  Shen, Tianyu;  Wang, Jiangong;  Gou, Chao;  Wang, Fei-Yue
Adobe PDF(3530Kb)  |  收藏  |  浏览/下载:259/60  |  提交时间:2021/03/02
Image segmentation  Biomedical imaging  Fuzzy sets  Breast cancer  Breast cancer  deep learning (DL)  fuzzy classifier (FC)  interval type-2 possibilistic fuzzy c-means (IT2PFCM)  
A Learning-Based Framework for Error Compensation in 3-D Printing 期刊论文
IEEE Transactions on Cybernetics, 2019, 卷号: 49, 期号: 11, 页码: 4042-4050
作者:  Shen Z(沈震);  Shang XQ(商秀芹);  Zhao MH(赵美华);  Xiong G(熊刚);  Wang FY(王飞跃)
浏览  |  Adobe PDF(3664Kb)  |  收藏  |  浏览/下载:336/85  |  提交时间:2019/09/25
3d Printing  Additive Manufacturing  Cyber Physical System (Cps)  Deep Learning  Error Compensation  
PredNet and CompNet: Prediction and High-Precision Compensation of In-Plane Shape Deformation for Additive Manufacturing 会议论文
, Vancouver, BC, Canada, August 22-26, 2019
作者:  Shen, Zhen;  Shang, Xiuqin;  Li, Yuqing;  Bao, Yin;  Zhang, Xipeng;  Dong, Xisong;  Wan, Li;  Xiong, Gang;  Wang, Fei-Yue
浏览  |  Adobe PDF(4106Kb)  |  收藏  |  浏览/下载:248/38  |  提交时间:2019/10/10
Detecting Traffic Information From Social Media Texts With Deep Learning Approaches 期刊论文
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2019, 卷号: 20, 期号: 8, 页码: 3049-3058
作者:  Chen, Yuanyuan;  Lv, Yisheng;  Wang, Xiao;  Li, Lingxi;  Wang, Fei-Yue
Adobe PDF(2273Kb)  |  收藏  |  浏览/下载:395/102  |  提交时间:2019/08/28
Deep learning  social transportation  traffic information detection  social media  text mining  
DeepTrend 2.0: A light-weighted multi-scale traffic prediction model using detrending 期刊论文
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2019, 卷号: 103, 页码: 142-157
作者:  Dai, Xingyuan;  Fu, Rui;  Zhao, Enmin;  Zhang, Zuo;  Lin, Yilun;  Wang, Fei-Yue;  Li, Li
Adobe PDF(5109Kb)  |  收藏  |  浏览/下载:295/26  |  提交时间:2019/09/30
Traffic prediction  Deep learning  Detrending  Multi-scale traffic prediction