机构

• 长春光学精密机械与... [13]

采集方式

• OAI收割 [13]

内容类型

• 期刊论文 [13]

发表日期

• 2019 [13]

学科主题

筛选

浏览/检索结果: 共13条，第1-10条 帮助 限定条件 发表日期：2019    专题：长春光学精密机械与物理研究所    第一署名单位    第一作者单位    通讯作者单位     条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序发表日期升序发表日期降序题名升序题名降序作者升序作者降序 Object tracking framework with Siamese network and re-detection mechanism 期刊论文  OAI收割 Eurasip Journal on Wireless Communications and Networking, 2019, 卷号: 2019, 期号: 1, 页码: 14 作者:  D.Q.Li;  Y.Yu;  X.L.Chen   |  收藏  |  浏览/下载：23/0  |  提交时间：2020/08/24 Object tracking,Siamese network,Re-detection mechanism,Generative,model,High-confidence update,visual tracking,Engineering,Telecommunications   Large-scale piston error detection technology for segmented optical mirrors via convolutional neural networks 期刊论文  OAI收割 Optics Letters, 2019, 卷号: 44, 期号: 5, 页码: 1170-1173 作者:  D.Q.Li;  S.Y.Xu;  D.Wang;  D.J.Yan   |  收藏  |  浏览/下载：15/0  |  提交时间：2020/08/24 Optics   Phase diversity algorithm with high noise robust based on deep denoising convolutional neural network 期刊论文  OAI收割 Optics Express, 2019, 卷号: 27, 期号: 16, 页码: 22846-22854 作者:  D.Q.Li;  S.Y.Xu;  D.Wang;  D.J.Yan   |  收藏  |  浏览/下载：17/0  |  提交时间：2020/08/24 Optics   Single Shot Anchor Refinement Network for Oriented Object Detection in Optical Remote Sensing Imagery 期刊论文  OAI收割 Ieee Access, 2019, 卷号: 7, 页码: 87150-87161 作者:  S.Z.Bao;  X.Zhong;  R.F.Zhu;  X.N.Zhang;  Z.Q.Li   |  收藏  |  浏览/下载：20/0  |  提交时间：2020/08/24 Convolutional neural network (CNN),remote sensing,oriented object,detection,anchor refinement,ship detection   Siamese Network Using Adaptive Background Superposition Initialization for Real-Time Object Tracking 期刊论文  OAI收割 Ieee Access, 2019, 卷号: 7, 页码: 119454-119464 作者:  J.N.Zhu;  T.Chen;  J.T.Cao   |  收藏  |  浏览/下载：16/0  |  提交时间：2020/08/24 Adaptive background superposition initialization,channel attention,module,object tracking,Siamese network,Computer Science,Engineering,Telecommunications   Combining background subtraction algorithms with convolutional neural network 期刊论文  OAI收割 Journal of Electronic Imaging, 2019, 卷号: 28, 期号: 1, 页码: 6 作者:  D.D.Zeng;  M.Zhu;  A.Kuijper   |  收藏  |  浏览/下载：13/0  |  提交时间：2020/08/24 foreground object detection,convolutional neural network,CDnet 2014,dataset,video surveillance,object detection,Engineering,Optics,Imaging Science & Photographic Technology   Underwater Object Recognition Based on Deep Encoding-Decoding Network 期刊论文  OAI收割 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   |  收藏  |  浏览/下载：36/0  |  提交时间：2020/08/24 deep learning,transfer learning,encoding-decoding,underwater object,object recognition,Oceanography   Scene classification of high-resolution remote sensing images based on IMFNet 期刊论文  OAI收割 Journal of Applied Remote Sensing, 2019, 卷号: 13, 期号: 4, 页码: 21 作者:  X.Zhang;  Y.C.Wang;  N.Zhang;  D.D.Xu;  B.Chen   |  收藏  |  浏览/下载：20/0  |  提交时间：2020/08/24 image processing,remote sensing,artificial intelligence,pattern,recognition,scene classification,convolutional neural-networks,deep,Environmental Sciences & Ecology,Remote Sensing,Imaging Science &,Photographic Technology   Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet 期刊论文  OAI收割 Applied Sciences-Basel, 2019, 卷号: 9, 期号: 10, 页码: 26 作者:  X.Zhang;  Y.C.Wang;  N.Zhang;  D.D.Xu;  B.Chen   |  收藏  |  浏览/下载：22/0  |  提交时间：2020/08/24 convolutional neural network,ResNet,semantic information,remote,sensing images,scene classification,TensorFlow,satellite images,deep,representation,network,features,scale,Chemistry,Engineering,Materials Science,Physics   Inversion Restoring Algorithm for Whiskbroom Scanning Images Synthesized with Deep Convolutional Neural Network 期刊论文  OAI收割 Guangxue Xuebao/Acta Optica Sinica, 2019, 卷号: 39, 期号: 12 作者:  C.Xu;  G.Jin;  X.Yang;  T.Xu;  L.Chang   |  收藏  |  浏览/下载：15/0  |  提交时间：2020/08/24 Image enhancement,Cameras,Convolution,Deep neural networks,Deterioration,Distortion (waves),Geometry,Image quality,Image reconstruction,Network architecture,Neural networks,Pixels,Quality control,Remote sensing,Restoration,Scanning,Space optics