机构

• 自动化研究所 [7]
• 西安光学精密机械研究... [2]
• 长春光学精密机械与物... [1]

采集方式

• OAI收割 [10]

内容类型

• 期刊论文 [8]
• 会议论文 [1]
• 学位论文 [1]

发表日期

• 2024 [1]
• 2022 [1]
• 2019 [1]
• 2017 [2]
• 2016 [1]
• 2015 [1]
• 更多

学科主题

筛选

浏览/检索结果: 共10条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 CGFormer: ViT-Based Network for Identifying Computer-Generated Images With Token Labeling 期刊论文  OAI收割 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2024, 卷号: 19, 页码: 235-250 作者:  Quan, Weize;  Deng, Pengfei;  Wang, Kai;  Yan, Dong-Ming   |  收藏  |  浏览/下载：20/0  |  提交时间：2024/02/22 CG image forensics  transformer  token labeling  generalization  robustness   Fine-Grained Human-Centric Tracklet Segmentation with Single Frame Supervision 期刊论文  OAI收割 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2022, 卷号: 44, 期号: 2, 页码: 610-621 作者:  Liu, Si;  Ren, Guanghui;  Sun, Yao;  Wang, Jinqiao;  Wang, Changhu   |  收藏  |  浏览/下载：29/0  |  提交时间：2022/02/16 Labeling  Object segmentation  Image segmentation  Task analysis  Semantics  Training  Face  Video object segmentation  human-centric  fine-grained  optical flow estimation   Automatic brain labeling via multi-atlas guided fully convolutional networks 期刊论文  OAI收割 Medical Image Analysis, 2019, 期号: 52, 页码: 157-168 作者:  Longwei Fang;  Lichi Zhang;  Dong Nie;  Xiaohuan Cao;  Islem Rekik   |  收藏  |  浏览/下载：70/0  |  提交时间：2019/05/05 Brain Image Labeling, Multi-atlas-based Method, Fully Convolutional Network, Patch-based Labeling   Geographic, Geometrical and Semantic Reconstruction of Urban Scene from High Resolution Oblique Aerial Image 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2017, 期号: x, 页码: x 作者:  Xiaofeng Sun;  Shuhan Shen;  Hainan Cui;  Lihua Hu;  Zhanyi Hu   |  收藏  |  浏览/下载：44/0  |  提交时间：2018/10/09 Oblique Aerial Image  Point Cloud  Urban Reconstruction  Semantic Labeling   Robust Sparse Coding for Mobile Image Labeling on the Cloud 期刊论文  OAI收割 ieee transactions on circuits and systems for video technology, 2017, 卷号: 27, 期号: 1, 页码: 62-72 作者:  Tao, Dapeng;  Cheng, Jun;  Gao, Xinbo;  Li, Xuelong;  Deng, Cheng 收藏  |  浏览/下载：29/0  |  提交时间：2017/04/05 Cloud computing  correntropy  mobile image  labeling  sparse coding   DISC: Deep Image Saliency Computing via Progressive Representation Learning 期刊论文  OAI收割 ieee transactions on neural networks and learning systems, 2016, 卷号: 27, 期号: 6, 页码: 1135-1149 作者:  Chen, Tianshui;  Lin, Liang;  Liu, Lingbo;  Luo, Xiaonan;  Li, Xuelong 收藏  |  浏览/下载：44/0  |  提交时间：2016/09/19 Convolutional neural network (CNN)  image labeling  representation learning  saliency detection   Image automatic annotation via multi-view deep representation 期刊论文  OAI收割 Journal of Visual Communication and Image Representation, 2015, 卷号: 2015, 期号: 33, 页码: 368-377 作者:  Yang Y(杨阳);  Zhang Wensheng（张文生）   |  收藏  |  浏览/下载：24/0  |  提交时间：2016/10/13 Image Annotation  Stacked Auto-encoder  Imbalance Learning  Multi-view Learning  Image Features  Semantic Gap  Deep Learning  Multi-labeling   基于局部上下文的图像内容理解研究 学位论文  OAI收割 工学博士, 中国科学院自动化研究所: 中国科学院大学, 2014 作者:  吴子丰 收藏  |  浏览/下载：72/0  |  提交时间：2015/09/02 图像内容理解  上下文知识  图像分类  图像语义分割  步态识别  Image understanding  Context  Image classification  Image labeling  Gait recognition   Target characteristic extraction algorithm based on block structure variables (EI CONFERENCE) 会议论文  OAI收割 2010 3rd International Conference on Advanced Computer Theory and Engineering, ICACTE 2010, August 20, 2010 - August 22, 2010, Chengdu, China 作者:  Zhou Y.;  Yang H.;  Yan F.;  Yan F. 收藏  |  浏览/下载：28/0  |  提交时间：2013/03/25 In image processing system  the target recognition is very crucial. An optimized algorithm based on the definition of block is presented. In this algorithm  two structure variables are self-defined to calculate the areas and centroids of targets. The labeling conflicts are resolved by tracking and correcting  which means that if there is a conflict  trace the neighbored blocks and correct the labels of them. Finally  the characteristics of blocks  which have the same label  are accumulated. This method has outstanding advantages in saving memory  compared with Pixel labeling algorithm and Run-length code algorithm. The new algorithm is simple and the results of target characteristics are easy to be analyzed and handled in the following processes. 2010 IEEE.   A novel pixon-representation for image segmentation based on Markov random field 期刊论文  OAI收割 IMAGE AND VISION COMPUTING, 2008, 卷号: 26, 期号: 11, 页码: 1507-1514 作者:  Lin, Lei;  Zhu, Litao;  Yang, Faguo;  Jiang, Tianzi 收藏  |  浏览/下载：11/0  |  提交时间：2015/11/08 image segmentation  pixon-representation  Markov random field  region labeling