中文版 | English

在结果中检索

CASIA OpenIR

浏览/检索结果: 共46条，第1-10条 帮助

限定条件    
已选(0)清除 条数/页：   排序方式：
A Model of Spray Tool and a Parameter Optimization Method for Spraying Path Planning 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 6, 页码: 1017-1031
作者:  Ruxiang Hua;  Wei Zou;   Guodong Chen;  Hongxuan Ma;  Wei Zhang
Adobe PDF(3022Kb)  |  收藏  |  浏览/下载：178/53  |  提交时间：2023/07/04
Digital camouflage  spraying robot  path optimization  parameter setting  coating thickness uniformity  
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：252/53  |  提交时间：2021/11/26
Abdominal organ, supervised segmentation  semi-supervised segmentation  evaluation metrics  image segmentation  machine learning  
STRNet: Triple-stream Spatiotemporal Relation Network for Action Recognition 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 5, 页码: 718-730
作者:  Zhi-Wei Xu;  Xiao-Jun Wu;  Josef Kittler
Adobe PDF(1129Kb)  |  收藏  |  浏览/下载：203/52  |  提交时间：2021/09/13
Action recognition  spatiotemporal relation  multi-branch fusion  long-term representation  video classification  
Dynamic System Identification of Underwater Vehicles Using Multi-output Gaussian Processes 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 5, 页码: 681-693
作者:  Wilmer Ariza Ramirez;  Juš Kocijan;  Zhi Quan Leong;  Hung Duc Nguyen;  Shantha Gamini Jayasinghe
Adobe PDF(3231Kb)  |  收藏  |  浏览/下载：228/54  |  提交时间：2021/09/13
Dependent Gaussian processes  dynamic system identification  multi-output Gaussian processes  non-parametric identification  autonomous underwater vehicle (AUV)  
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：203/49  |  提交时间：2021/07/20
Self-supervised learning  contrastive learning  synthetic image  convolutional neural network  representation learning  
A Review on Cooperative Robotic Arms with Mobile or Drones Bases 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 4, 页码: 536-555
作者:  Larona Pitso Ramalepa;  Rodrigo S. Jamisola Jr.
Adobe PDF(1192Kb)  |  收藏  |  浏览/下载：530/354  |  提交时间：2021/07/20
Cooperative arms  mobile manipulator  aerial manipulator  mobile base  drone base  cooperative tasks  
Skill Learning for Robotic Insertion Based on One-shot Demonstration and Reinforcement Learning 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 457-467
作者:  Ying Li;  De Xu
Adobe PDF(1450Kb)  |  收藏  |  浏览/下载：214/51  |  提交时间：2021/05/24
Force Jacobian matrix  one-shot demonstration  dynamic exploration strategy  insertion skill learning  reinforcement  
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：291/64  |  提交时间：2021/05/24
Transfer learning  gesture recognition  red-green-blue (RGB) Camera  Leap Motion  joint distribution adaptation (JDA)  
Deep Audio-Visual Learning: A Survey 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 351-376
作者:  Hao Zhu;  Man-Di Luo;  Rui Wang;  Ai-Hua Zheng;  Ran He
Adobe PDF(1864Kb)  |  收藏  |  浏览/下载：222/47  |  提交时间：2021/05/24
Deep audio-visual learning  audio-visual separation and localization  correspondence learning  generative models  representation learning  
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：292/69  |  提交时间：2021/05/24
Group activity recognition (GAR)  human activity recognition  scene understanding  video analysis  computer vision