机构

• 自动化研究所 [4]
• 计算技术研究所 [2]
• 长春光学精密机械与物... [1]
• 沈阳自动化研究所 [1]
• 合肥物质科学研究院 [1]

采集方式

• OAI收割 [9]

内容类型

• 期刊论文 [7]
• 会议论文 [2]

发表日期

• 2021 [1]
• 2020 [2]
• 2019 [1]
• 2018 [1]
• 2016 [1]
• 2015 [1]
• 更多

学科主题

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浏览/检索结果: 共9条，第1-9条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择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作者升序作者降序 FSD-SLAM: a fast semi-direct SLAM algorithm 期刊论文  OAI收割 COMPLEX & INTELLIGENT SYSTEMS, 2021, 页码: 12 作者:  Dong, Xiang;  Cheng, Long;  Peng, Hu;  Li, Teng   |  收藏  |  浏览/下载：52/0  |  提交时间：2021/04/21 SLAM  Feature enhancement  Pose estimation  Incremental dynamic covariance scaling  Point cloud integration   Multi-Objective Matrix Normalization for Fine-Grained Visual Recognition 期刊论文  OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 4996-5009 作者:  Min, Shaobo;  Yao, Hantao;  Xie, Hongtao;  Zha, Zheng-Jun;  Zhang, Yongdong   |  收藏  |  浏览/下载：46/0  |  提交时间：2020/06/02 Visualization  Graphics processing units  Feature extraction  Convergence  Optimization  Covariance matrices  Training  Fine-grained visual recognition  bilinear pooling  matrix normalization  multi-objective optimization   Deep Heterogeneous Hashing for Face Video Retrieval 期刊论文  OAI收割 IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 卷号: 29, 页码: 1299-1312 作者:  Qiao, Shishi;  Wang, Ruiping;  Shan, Shiguang;  Chen, Xilin   |  收藏  |  浏览/下载：29/0  |  提交时间：2020/12/10 Face  Covariance matrices  Task analysis  Binary codes  Kernel  Manifolds  Feature extraction  Face video retrieval  deep heterogeneous hashing  Riemannian kernel mapping  structured matrix backpropagation   A Novel Sign Language Recognition Framework Using Hierarchical Grassmann Covariance Matrix 期刊论文  OAI收割 IEEE TRANSACTIONS ON MULTIMEDIA, 2019, 卷号: 21, 期号: 11, 页码: 2806-2814 作者:  Wang, Hanjie;  Chai, Xiujuan;  Chen, Xilin   |  收藏  |  浏览/下载：29/0  |  提交时间：2020/12/10 Covariance matrices  Hidden Markov models  Manifolds  Assistive technology  Feature extraction  Gesture recognition  Correlation  Sign language recognition  grassmann covariance matrix  grassmann manifold  belief propagation  sentence spotting   Cross-covariance regularized autoencoders for nonredundant sparse feature representation 期刊论文  OAI收割 NEUROCOMPUTING, 2018, 卷号: 316, 页码: 49-58 作者:  Chen, Jie;  Wu, ZhongCheng;  Zhang, Jun;  Li, Fang;  Li, WenJing   |  收藏  |  浏览/下载：52/0  |  提交时间：2019/12/11 Autoencoder  Cross-covariance  Deep learning  Feature representation  Receptive fields   Accurate and robust feature-based homography estimation using HALF-SIFT and feature localization error weighting 期刊论文  OAI收割 Journal of Visual Communication and Image Representation, 2016, 卷号: 40, 页码: 288-299 作者:  Zhao CY(赵春阳);  Zhao HC(赵怀慈) 收藏  |  浏览/下载：35/0  |  提交时间：2016/08/14 Homography estimation  Feature localization error  Covariance weighted MLESAC  Covariance weighted Levenberg-Marquardt   A new region descriptor for multi-modal medical image registration and region detection 会议论文  OAI收割 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 25-29 Aug. 2015 作者:  Wan, Xiaonan;  Yu, Dongdong;  Yang, Feng;  Yang, Caiyun;  Leng, Chengcai 收藏  |  浏览/下载：29/0  |  提交时间：2016/04/19 Image registration, Feature extraction, Covariance matrices   Block covariance based l(1) tracker with a subtle template dictionary 期刊论文  OAI收割 PATTERN RECOGNITION, 2013, 卷号: 46, 期号: 7, 页码: 1750-1761 作者:  Zhang, Xiaoqin;  Li, Wei;  Hu, Weiming;  Ling, Haibin;  Maybank, Steve 收藏  |  浏览/下载：25/0  |  提交时间：2015/08/12 Visual tracking  Sparse representation  Block division  Covariance feature  Template update   A matching algorithm on statistical properties of Harris corner (EI CONFERENCE) 会议论文  OAI收割 2011 International Conference on Information and Automation, ICIA 2011, June 6, 2011 - June 8, 2011, Shenzhen, China 作者:  He B. 收藏  |  浏览/下载：31/0  |  提交时间：2013/03/25 The fundamental goal of target recognition and video tracking is to match target template with source image. Most matching methods are based on image intensity or multi-feature points. And the latter method is more popular for its high accuracy and small calculation. Image Registration Based on Feature Points focus on effective feature extraction of image points and paradigm. Harris corner in the image rotation  gray  noise and viewpoint change conditions  has an ideal match results  is more recent application of one feature point. This paper extract the Harris corner deviation and covariance firstly  experiments show that the two features exclusive  then applied them to image registration for the first time. A set of actual images have shown  this proposed method not only overcomes the complicated background  gray uneven distribution problems  but also pan and zoom the image has a good resistance. 2011 IEEE.