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Supervised and Semi-supervised Methods for Abdominalm Organ Segmentation: A Review 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 887-914
作者:  Isaac Baffour Senkyire;  Zhe Liu
Adobe PDF(1308Kb)  |  收藏  |  浏览/下载：254/53  |  提交时间：2021/11/26
Abdominal organ, supervised segmentation  semi-supervised segmentation  evaluation metrics  image segmentation  machine learning  
Contrastive Self-supervised Representation Learning Using Synthetic Data 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 4, 页码: 556-567
作者:  Dong-Yu She;  Kun Xu
Adobe PDF(993Kb)  |  收藏  |  浏览/下载：209/53  |  提交时间：2021/07/20
Self-supervised learning  contrastive learning  synthetic image  convolutional neural network  representation learning  
Learning Deep RGBT Representations for Robust Person Re-identification 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 443-456
作者:  Ai-Hua Zheng;  Zi-Han Chen;  Cheng-Long Li;  Jin Tang;  Bin Luo
Adobe PDF(1832Kb)  |  收藏  |  浏览/下载：296/67  |  提交时间：2021/05/24
Person re-identification (Re-ID)  thermal infrared  generative networks  attention  deep learning  
Research on Transfer Learning of Vision-based Gesture Recognition 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 422-431
作者:  Bi-Xiao Wu;  Chen-Guang Yang;  Jun-Pei Zhong
Adobe PDF(1688Kb)  |  收藏  |  浏览/下载：298/66  |  提交时间：2021/05/24
Transfer learning  gesture recognition  red-green-blue (RGB) Camera  Leap Motion  joint distribution adaptation (JDA)  
A Comprehensive Review on Group Activity Recognition in Videos 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 334-350
作者:  Li-Fang Wu;  Qi Wang;  Meng Jian;  Yu Qiao;  Bo-Xuan Zhao
Adobe PDF(1415Kb)  |  收藏  |  浏览/下载：302/73  |  提交时间：2021/05/24
Group activity recognition (GAR)  human activity recognition  scene understanding  video analysis  computer vision  
Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 311-333
作者:  Xiao-Qin Zhang;  Run-Hua Jiang;  Chen-Xiang Fan;  Tian-Yu Tong;  Tao Wang Peng-Cheng Huang
Adobe PDF(1787Kb)  |  收藏  |  浏览/下载：309/50  |  提交时间：2021/05/24
Deep learning  visual tracking  data-invariant  data-adaptive  general components  
Why and When Can Deep-but Not Shallow-networks Avoid the Curse of Dimensionality: A Review 期刊论文
International Journal of Automation and Computing, 2017, 卷号: 14, 期号: 5, 页码: 503-519
作者:  Tomaso Poggio;  Hrushikesh Mhaskar;  Lorenzo Rosasco;  Brando Miranda;  Qianli Liao
浏览  |  Adobe PDF(1711Kb)  |  收藏  |  浏览/下载：218/36  |  提交时间：2021/02/23
Deep learning  fine-grained image classification  semantic segmentation  convolutional neural network (CNN)  recurrent neural network (RNN).  
Computational Intelligence in Remote Sensing Image Registration: A survey 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 1, 页码: 1-17
作者:  Yue Wu;  Jun-Wei Liu;  Chen-Zhuo Zhu;  Zhuang-Fei Bai;  Qi-Guang Miao;  Wen-Ping Ma;  Mao-Guo Gong
浏览  |  Adobe PDF(995Kb)  |  收藏  |  浏览/下载：293/76  |  提交时间：2021/02/23
Computational intelligence  evolutionary computation  neural network  deep learning  remote sensing image registration  
Applying Deep Learning to Individual and Community Health Monitoring Data: A Survey 期刊论文
International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 6, 页码: 643-655
作者:  Zhen-Jie Yao;  Jie Bi;  Yi-Xin Chen
浏览  |  Adobe PDF(692Kb)  |  收藏  |  浏览/下载：132/39  |  提交时间：2021/02/23
Deep learning  healthcare  electronic health records (EHR)  neural networks  survey.  
A Wide Learning Approach for Interpretable Feature Recommendation for 1-D Sensor Data in IoT Analytics 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 6, 页码: 800-811
作者:  Snehasis Banerjee;  Tanushyam Chattopadhyay;  Utpal Garain
浏览  |  Adobe PDF(891Kb)  |  收藏  |  浏览/下载：248/66  |  提交时间：2021/02/22
Feature engineering  sensor data analysis  Internet of things (IoT) analytics  interpretable learning  automation.