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CIOMP OpenIR

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Semi-Empirical Soil Organic Matter Retrieval Model With Spectral Reflectance 期刊论文
Ieee Access, 2019, 卷号: 7, 页码: 134164-134172
作者:  J.Yuan;  C.H.Hu;  C.X.Yan;  Z.Z.Li;  S.B.Chen;  S.R.Wang
浏览  |  Adobe PDF(805Kb)  |  收藏  |  浏览/下载：403/82  |  提交时间：2020/08/24
Soil organic matter retrieval,reflectance,semi-empirical model,KM,machine learning-methods,moisture retrieval,calibration,prediction,carbon,interpolation,spectroscopy,texture,Computer Science,Engineering,Telecommunications  
Underwater Object Recognition Based on Deep Encoding-Decoding Network 期刊论文
Journal of Ocean University of China, 2019, 卷号: 18, 期号: 2, 页码: 376-382
作者:  X.H.Wang;  J.H.Ouyang;  D.Y.Li;  G.Zhang
浏览  |  Adobe PDF(464Kb)  |  收藏  |  浏览/下载：273/67  |  提交时间：2020/08/24
deep learning,transfer learning,encoding-decoding,underwater object,object recognition,Oceanography  
A Novel Medical Image Edge Detection Method Based on Reinforcement Learning and Ant Colony Optimization 期刊论文
Journal of Medical Imaging and Health Informatics, 2019, 卷号: 9, 期号: 1, 页码: 175-182
作者:  X.H.Wang;  J.H.Ouyang;  Y.G.Zhu;  H.B.Yu;  H.L.Li
浏览  |  Adobe PDF(5477Kb)  |  收藏  |  浏览/下载：303/98  |  提交时间：2020/08/24
Medical Imaging,Image Processing,Ant Colony Optimization,Reinforcement Learning,algorithm,aco,Mathematical & Computational Biology,Radiology, Nuclear Medicine &,Medical Imaging  
Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales 期刊论文
Remote Sensing, 2019, 卷号: 11, 期号: 22, 页码: 22
作者:  W.X.Zhu;  Z.G.Sun;  J.B.Peng;  Y.H.Huang;  J.Li;  J.Q.Zhang
浏览  |  Adobe PDF(9111Kb)  |  收藏  |  浏览/下载：253/106  |  提交时间：2020/08/24
unmanned aerial vehicle,above-ground biomass,LiDAR,crop height,machine learning,canopy height,multispectral data,SfM point clouds,leaf-area index,crop surface models,winter-wheat,  
An improved material removal model for robot polishing based on neural networks 期刊论文
Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2019, 卷号: 48, 期号: 3
作者:  Y.Yu;  L.Kong;  H.Zhang;  M.Xu;  L.Wang
caj(1946Kb)  |  收藏  |  浏览/下载：237/64  |  提交时间：2020/08/24
Learning algorithms,Deep neural networks,Learning systems,Polishing,Robots  
TLD particle swarm optimization target tracking using a sample deletion mechanism 期刊论文
Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2019, 卷号: 27, 期号: 5, 页码: 1206-1217
作者:  S.-Q.Guo;  T.Zhang;  X.-K.Miao
caj(780Kb)  |  收藏  |  浏览/下载：183/69  |  提交时间：2020/08/24
Target tracking,Clutter (information theory,Computational efficiency,Efficiency,Learning algorithms  
Scene Restoration and Semantic Classification Network Using Depth Map and Discrete Pooling Technology 期刊论文
Zidonghua Xuebao/Acta Automatica Sinica, 2019, 卷号: 45, 期号: 11, 页码: 2178-2186
作者:  J.-H.Lin;  Y.Yao;  Y.Wang
浏览  |  Adobe PDF(971Kb)  |  收藏  |  浏览/下载：240/86  |  提交时间：2020/08/24
Deep neural networks,Classification (of information),Computer vision,Convolution,Deep learning,Image reconstruction,Neural networks,Semantic Web,Semantics  
Hard negative generation for identity-disentangled facial expression recognition 期刊论文
Pattern Recognition, 2019, 卷号: 88, 页码: 1-12
作者:  X.F.Liu;  B.Kumar;  P.Jia;  J.You
浏览  |  Adobe PDF(3296Kb)  |  收藏  |  浏览/下载：417/92  |  提交时间：2020/08/24
Hard negative generation,Adaptive metric learning,Face normalization,Facial expression recognition,Computer Science,Engineering  
Semantic segmentation based on DeepLabV3+ and superpixel optimization 期刊论文
Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 2019, 卷号: 27, 期号: 12, 页码: 2722-2729
作者:  F.-L.Ren;  X.He;  Z.-H.Wei;  Y.Lu;  M.-Y.Li
浏览  |  Adobe PDF(2102Kb)  |  收藏  |  浏览/下载：240/59  |  提交时间：2020/08/24
Image segmentation,Clustering algorithms,Deep learning,Iterative methods,Semantics,Superpixels  
Image Retrieval Based on Learning to Rank and Multiple Loss 期刊论文
Isprs International Journal of Geo-Information, 2019, 卷号: 8, 期号: 9, 页码: 22
作者:  L.L.Fan;  H.W.Zhao;  H.Y.Zhao;  P.P.Liu;  H.S.Hu
浏览  |  Adobe PDF(5998Kb)  |  收藏  |  浏览/下载：172/35  |  提交时间：2020/08/24
multiple loss function,computer vision,deep image retrieval,learning,to rank,deep learning,object retrieval