QQ客服 官方微博 反馈留言

浏览/检索结果: 共11条，第1-10条 帮助 限定条件 专题：湖南大学    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks. 期刊论文 Computers in biology and medicine, 2019, 卷号: Vol.108, 页码: 1-8 作者:  Ashkan Abbasi;  Amirhassan Monadjemi;  Leyuan Fang;  Hossein Rabbani;  Yi Zhang 收藏  |  浏览/下载：5/0  |  提交时间：2019/12/17 Fully convolutional network (FCN)  Image denoising  Multi-input FCN  Optical coherence tomography (OCT)   Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks 期刊论文 Computers in biology and medicine, 2019, 卷号: Vol.108, 页码: 1-8 作者:  Ashkan Abbasi;  Amirhassan Monadjemi;  Leyuan Fang;  Hossein Rabbani;  Yi Zhang 收藏  |  浏览/下载：9/0  |  提交时间：2019/12/17 Fully convolutional network (FCN)  Multi-input FCN  Image denoising  Optical coherence tomography (OCT)   Adversarial Reconstruction-Classification Networks for PolSAR Image Classification 期刊论文 Remote Sensing, 2019, 卷号: Vol.11 No.4, 页码: 415 作者:  Yanqiao Chen;  Yangyang Li;  Licheng Jiao;  Cheng Peng;  Xiangrong Zhang 收藏  |  浏览/下载：12/0  |  提交时间：2019/12/17 polarimetric synthetic aperture radar (PolSAR)  fully convolutional networks (FCN)  sliding window fully convolutional networks (SFCN)  reconstruction-classification networks (RCN)  adversarial reconstruction-classification networks (ARCN)   Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks 期刊论文 COMPUTERS IN BIOLOGY AND MEDICINE, 2019, 卷号: Vol.108, 页码: 1-8 作者:  Abbasi, A;  Monadjemi, A;  Fang, LY;  Rabbani, H;  Zhang, Y 收藏  |  浏览/下载：9/0  |  提交时间：2019/12/17 Fully convolutional network (FCN)  Multi-input FCN  Image denoising  Optical coherence tomography (OCT)   Scale-Free Convolutional Neural Network for Remote Sensing Scene Classification 期刊论文 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 卷号: Vol.57 No.9, 页码: 6916-6928 作者:  Xie, J;  He, NJ;  Fang, LY;  Plaza, A 收藏  |  浏览/下载：3/0  |  提交时间：2019/12/17 Free-scale convolutional neural networks (CNNs)  fully connected layers (FCLs)  remote sensing scene classification   Scale-Free Convolutional Neural Network for Remote Sensing Scene Classification 期刊论文 IEEE Transactions on Geoscience and Remote Sensing, 2019, 卷号: Vol.57 No.9, 页码: 6916-6928 作者:  Jie Xie;  Nanjun He;  Leyuan Fang;  Antonio Plaza 收藏  |  浏览/下载：7/0  |  提交时间：2019/12/17 Remote sensing  Feature extraction  Iron  Data models  Image color analysis  Kernel  Semantics  Free-scale convolutional neural networks (CNNs)  fully connected layers (FCLs)  remote sensing scene classification   Locally Shared Features: An Efficient Alternative to Conditional Random Field for Semantic Segmentation 期刊论文 IEEE Access, 2019, 卷号: Vol.7, 页码: 2263-2272 作者:  Zhengeng Yang;  Hongshan Yu;  Wei Sun;  Zhihong Mao;  Mingui Sun 收藏  |  浏览/下载：12/0  |  提交时间：2019/12/13 Semantics  Image  segmentation  Spatial  resolution  Training  Kernel  Urban  areas  Semantic  segmentation  fully  convolutional  networks  feature  learning  context  exploitation   Three-dimensional optical coherence tomography image denoising through multi-input fully-convolutional networks 期刊论文 Computers in Biology and Medicine, 2019 作者:  Ashkan Abbasi;  Amirhassan Monadjemi;  Leyuan Fang;  Hossein Rabbani;  Yi Zhang 收藏  |  浏览/下载：19/0  |  提交时间：2019/12/13 Fully  convolutional  network  (FCN)  Multi-input  FCN  Image  denoising  Optical  coherence  tomography  (OCT)   Locally Shared Features: An Efficient Alternative to Conditional Random Field for Semantic Segmentation 期刊论文 IEEE ACCESS, 2019, 卷号: Vol.7, 页码: 2263-2272 作者:  Yang, ZG;  Yu, HS;  Sun, W;  Mao, ZH;  Sun, MG 收藏  |  浏览/下载：3/0  |  提交时间：2019/12/17 Semantic segmentation  fully convolutional networks  feature learning  context exploitation   Fully convolutional networks for action recognition 期刊论文 IET Computer Vision, 2017, 卷号: Vol.11 No.8, 页码: 744-749 作者:  Yu, Sheng;  Cheng, Yun;  Xie, Li;  Li, Shao-Zi 收藏  |  浏览/下载：1/0  |  提交时间：2019/12/31