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A new, feasible, and convenient method based on semantic segmentation and deep learning for hemoglobin monitoring 期刊论文
FRONTIERS IN MEDICINE, 2023, 卷号: 10, 页码: 9
作者:  Hu, Xiao-Yan;  Li, Yu-Jie;  Shu, Xin;  Song, Ai-Lin;  Liang, Hao;  Sun, Yi-Zhu;  Wu, Xian-Feng;  Li, Yong-Shuai;  Tan, Li-Fang;  Yang, Zhi-Yong;  Yang, Chun-Yong;  Xu, Lin-Quan;  Chen, Yu-Wen;  Yi, Bin
收藏  |  浏览/下载：11/0  |  提交时间：2023/12/25
continuous hemoglobin monitoring  deep learning  semantic segmentation  mask R-CNN  MobileNetV3  
Segment-then-Segment: Context-Preserving Crop-Based Segmentation for Large Biomedical Images 期刊论文
SENSORS, 2023, 卷号: 23, 期号: 2, 页码: 16
作者:  Bencevic, Marin;  Qiu, Yuming;  Galic, Irena;  Pizurica, Aleksandra
收藏  |  浏览/下载：75/0  |  提交时间：2023/03/02
biomedical images  convolutional neural networks  medical image segmentation  semantic segmentation  
Two-stage hemoglobin prediction based on prior causality 期刊论文
FRONTIERS IN PUBLIC HEALTH, 2022, 卷号: 10, 页码: 12
作者:  Chen, Yuwen;  Zhong, Kunhua;  Zhu, Yiziting;  Sun, Qilong
收藏  |  浏览/下载：27/0  |  提交时间：2023/02/07
non-invasive  prediction  causal knowledge  hemoglobin  segmentation  
Identifying and mapping individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning 期刊论文
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2020, 卷号: 169, 页码: 280-291
作者:  Zhang, Ce;  Atkinson, Peter M.;  George, Charles;  Wen, Zhaofei;  Diazgranados, Mauricio;  Gerard, France
收藏  |  浏览/下载：54/0  |  提交时间：2021/02/24
Multi-scale deep learning  Residual U-Net  Scale sequence  Semantic segmentation  Paramos  
Robust correlation filter tracking with deep semantic supervision 期刊论文
IET IMAGE PROCESSING, 2019, 卷号: 13, 期号: 5, 页码: 754-760
作者:  Wang, Wei;  Chen, Zhaoming;  Douadji, Lyes;  Shi, Mingquan
Adobe PDF(3083Kb)  |  收藏  |  浏览/下载：234/0  |  提交时间：2019/06/24
particle filtering (numerical methods)  learning (artificial intelligence)  target tracking  convolutional neural nets  robust correlation filter tracking  high tracking performance  tracking failure  deep semantic supervision tracking framework  redetection tracking mechanism  particle filtering resampling  CF tracker  deep convolutional neural network  tracking frames  target occlusion  handcrafted features  real-time performance  OTB2013 benchmark datasets  OTB2015 benchmark datasets  
Extensive exploration of comprehensive vehicle attributes using D-CNN with weighted multi-attribute strategy 期刊论文
IET INTELLIGENT TRANSPORT SYSTEMS, 2018, 卷号: 12, 期号: 3, 页码: 186-193
作者:  Yan, Zhuo;  Feng, Youji;  Cheng, Cheng;  Fu, Jianting;  Zhou, Xiangdong;  Yuan, Jiahu
Adobe PDF(3776Kb)  |  收藏  |  浏览/下载：237/0  |  提交时间：2018/06/04
Object Recognition  Feedforward Neural Nets  Learning (Artificial Intelligence)  Comprehensive Vehicle Attributes  D-cnn  Weighted Multiattribute Strategy  Deep Convolutional Neural Network  Surveillance Images  Vehicle Model Recognition  Make Recognition  Mtl Methods  Multitask Learning  Compcars Vehicle Dataset  Manufacturer Recognition  
Semantic classification for hyperspectral image by integrating distance measurement and relevance vector machine 期刊论文
MULTIMEDIA SYSTEMS, 2017, 卷号: 23, 期号: 1, 页码: 95-104
作者:  Liu, Jun;  Zhou, Xiran;  Huang, Junyi;  Liu, Shuguang;  Li, Huali;  Wen, Shan;  Liu, Junchen
Adobe PDF(1838Kb)  |  收藏  |  浏览/下载：78/0  |  提交时间：2018/03/05
Semantic classification  Hyperspectral image  Relevance vector machine  Multi-distance learning with multiple dimensions  
Generic normal cloud model 期刊论文
INFORMATION SCIENCES, 2014, 卷号: 280, 页码: 1-15
作者:  Wang, Guoyin;  Xu, Changlin;  Li, Deyi
Adobe PDF(1819Kb)  |  收藏  |  浏览/下载：500/0  |  提交时间：2018/03/15
Computational Cognition  Cloud Model  Generic Forward Cloud Transformation  Generic Backward Cloud Transformation  
Fashion Parsing With Weak Color-Category Labels 期刊论文
IEEE TRANSACTIONS ON MULTIMEDIA, 2014, 卷号: 16, 期号: 1, 页码: 253-265
作者:  Liu, Si;  Feng, Jiashi;  Domokos, Csaba;  Xu, Hui;  Huang, Junshi;  Hu, Zhenzhen;  Yan, Shuicheng
Adobe PDF(2479Kb)  |  收藏  |  浏览/下载：476/0  |  提交时间：2018/03/15
Fashion parsing  Markov random fields  weakly-supervised learning