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MCSfM: Multi-Camera-Based Incremental Structure-From-Motion 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2023, 卷号: 32, 页码: 6441-6456
作者:  Cui, Hainan;  Gao, Xiang;  Shen, Shuhan
收藏  |  浏览/下载：26/0  |  提交时间：2024/02/22
Structure-from-motion  multi-camera based reconstruction  multi-camera calibration  
VidSfM: Robust and Accurate Structure-From-Motion for Monocular Videos 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 卷号: 31, 页码: 2449-2462
作者:  Cui, Hainan;  Tu, Diantao;  Tang, Fulin;  Xu, Pengfei;  Liu, Hongmin;  Shen, Shuhan
收藏  |  浏览/下载：242/0  |  提交时间：2022/06/06
Cameras  Image reconstruction  Videos  Simultaneous localization and mapping  Video sequences  Robustness  Scalability  Structure from motion  image reconstruction  computational geometry  computer vision  
Narrowing the Gap: Improved Detector Training With Noisy Location Annotations 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 卷号: 31, 页码: 6369-6380
作者:  Wang, Shaoru;  Gao, Jin;  Li, Bing;  Hu, Weiming
Adobe PDF(1489Kb)  |  收藏  |  浏览/下载：212/25  |  提交时间：2022/11/14
Annotations  Noise measurement  Detectors  Task analysis  Training  Object detection  Degradation  Object detection  noisy label  Bayesian estimation  teacher-student learning  
Unsupervised Structure-Texture Separation Network for Oracle Character Recognition 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2022, 卷号: 31, 页码: 3137-3150
作者:  Wang, Mei;  Deng, Weihong;  Liu, Cheng-Lin
收藏  |  浏览/下载：218/0  |  提交时间：2022/06/10
Character recognition  Bones  Adaptation models  Training  Feature extraction  Writing  Transforms  Oracle character recognition  unsupervised domain adaptation  feature disentanglement  generative adversarial network  
Urban Scene LOD Vectorized Modeling From Photogrammetry Meshes 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 7458-7471
作者:  Han, Jiali;  Zhu, Lingjie;  Gao, Xiang;  Hu, Zhanyi;  Zhou, Liyang;  Liu, Hongmin;  Shen, Shuhan
Adobe PDF(8168Kb)  |  收藏  |  浏览/下载：277/36  |  提交时间：2021/11/03
Urban reconstruction  building modeling  Markov random field  segment based modeling  
Nocal-Siam: Refining Visual Features and Response With Advanced Non-Local Blocks for Real-Time Siamese Tracking 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 2656-2668
作者:  Tan, Huibin;  Zhang, Xiang;  Zhang, Zhipeng;  Lan, Long;  Zhang, Wenju;  Luo, Zhigang
Adobe PDF(2898Kb)  |  收藏  |  浏览/下载：163/40  |  提交时间：2021/03/29
Target tracking  Visualization  Feature extraction  Real-time systems  Oceans  Convolution  Task analysis  Siamese trackers  non-local attention  supervisedly attentive  
Extremely Lightweight Skeleton-Based Action Recognition With ShiftGCN plus 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 页码: 7333-7348
作者:  Cheng, Ke;  Zhang, Yifan;  He, Xiangyu;  Cheng, Jian;  Lu, Hanqing
Adobe PDF(3205Kb)  |  收藏  |  浏览/下载：257/14  |  提交时间：2021/11/03
Skeleton-based action recognition  graph convolutional network  lightweight network  shift network  
Learning Category- and Instance-Aware Pixel Embedding for Fast Panoptic Segmentation 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2021, 卷号: 30, 期号: 0, 页码: 6013
作者:  Gao, Naiyu;  Shan, Yanhu;  Zhao, Xin;  Huang, Kaiqi
Adobe PDF(3484Kb)  |  收藏  |  浏览/下载：175/39  |  提交时间：2022/06/14
Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 期号: 29, 页码: 9532-9545
作者:  Shi, Lei;  Zhang, Yifan;  Cheng, Jian;  Lu, Hanqing
浏览  |  Adobe PDF(2849Kb)  |  收藏  |  浏览/下载：375/148  |  提交时间：2020/11/05
Skeleton-based action recognition, graph convolutional network, adaptive graph, multi-stream network.  
Multi-Domain Image-to-Image Translation via a Unified Circular Framework 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 期号: 30, 页码: 670-684
作者:  Wang, Yuxi;  Zhang, Zhaoxiang;  Hao, Wangli;  Song, Chunfeng
Adobe PDF(3399Kb)  |  收藏  |  浏览/下载：314/57  |  提交时间：2021/03/02
Task analysis  Semantics  Visualization  Generative adversarial networks  Generators  Feature extraction  Meteorology  Image-to-image transfer  sharing knowledge module  multiple domain pairs  GANs