机构

• 沈阳自动化研究所 [4]
• 西安光学精密机械研究... [4]
• 长春光学精密机械与物... [2]
• 遥感与数字地球研究所 [1]

采集方式

• OAI收割 [11]

内容类型

• 会议论文 [11]

发表日期

• 2023 [1]
• 2022 [1]
• 2021 [1]
• 2020 [2]
• 2018 [1]
• 2017 [1]
• 更多

学科主题

筛选

浏览/检索结果: 共11条，第1-10条 帮助 限定条件 内容类型：会议论文     条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择提交时间升序提交时间降序题名升序题名降序作者升序作者降序发表日期升序发表日期降序 Research on data association and detection algorithm in point target tracking 会议论文  OAI收割 Beijing, China, 2023-07-25 作者:  He, Xiaokun;  Li, Peng;  Liu, Wen   |  收藏  |  浏览/下载：1/0  |  提交时间：2024/02/07 Point target  Multi-feature fusion  Data association  Point target detection   Infrared dim target detecting algorithm based on multi-feature and spatio-temporal fusion 会议论文  OAI收割 Shanghai, China, 2021-10-28 作者:  Bai, Mei;  Zhang, Jian;  Zhao, Hui   |  收藏  |  浏览/下载：35/0  |  提交时间：2022/03/18 Infrared Dim Target Detection  TOP-HAT  Improved-PM  Multi-feature Fusion  SCR   Physical characteristics and spillage detection Using multifeature fusion 会议论文  OAI收割 Ottawa, ON, Canada, 2021-09-08 作者:  Liu, Caiyu;  Zhou, Zuofeng;  Wu, Qingquan   |  收藏  |  浏览/下载：34/0  |  提交时间：2022/01/27 spillage detection  multi-feature fusion  density clustering   Unsupervised variational auto-encoder hash algorithm based on multi-channel feature fusion 会议论文  OAI收割 Osaka, Japan, 2020-05-19 作者:  Wang, Huanting;  Qu, Bo;  Lu, Xiaoqiang;  Chen, Yaxiong   |  收藏  |  浏览/下载：14/0  |  提交时间：2020/08/21 Multi-channel feature fusion  Unsupervised hashing algorithm  VAE  Image retrieval   Single target tracking algorithm based on multi-feature fusion 会议论文  OAI收割 Xiamen, China, August 25-27, 2020 作者:  Yue, Yang;  Wang, Guogang;  Liu YP(刘云鹏)   |  收藏  |  浏览/下载：9/0  |  提交时间：2021/03/14 Target tracking  Multi-feature fusion  Correlation filtering  Color features   Sub-blocks segmentation based on multi-feature fusion 会议论文  OAI收割 Beijing, China, May 22-24, 2018 作者:  Hui B(惠斌);  Chang Z(常铮);  Luo HB(罗海波);  Chen HY(陈宏宇);  Jiao AB(焦安波)   |  收藏  |  浏览/下载：22/0  |  提交时间：2018/12/24 Computer Vision  Target Tracking  Deformable Model  Multi-feature Fusion  Sub-blocks Segmentation  Edge Direction Dispersion Degree   Face detection based on multi task learning and multi layer feature fusion 会议论文  OAI收割 Dalian, China, October 21-22, 2017 作者:  Zhang YA(张延安);  Wang HY(王宏玉);  Xu F(徐方)   |  收藏  |  浏览/下载：20/0  |  提交时间：2018/07/30 Face Detection  Facial Feature Point Location  Deep Convolution Neural Network  Multi Task Learning  Feature Fusion   Mean-Shift Tracking Algorithm Based on Adaptive Fusion of Multi-feature 会议论文  OAI收割 Conference on Applied Optics and Photonics (AOPC) - Image Processing and Analysis, Beijing, MAY 05-07, 2015 作者:  Yang K(杨凯);  Xiao YH(肖阳辉);  Wang ED(王恩德);  Feng JH(冯俊惠) 收藏  |  浏览/下载：16/0  |  提交时间：2015/11/18 mean-shift  multi-feature fusion  affine illumination model  histogram of corner feature   Using bidirectional binary particle swarm optimization for feature selection in feature-level fusion recognition system (EI CONFERENCE) 会议论文  OAI收割 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, May 25, 2009 - May 27, 2009, Xi'an, China 作者:  Wang D.;  Wang Y.;  Wang Y.;  Wang Y.;  Wang Y. 收藏  |  浏览/下载：16/0  |  提交时间：2013/03/25 In feature-level fusion recognition system  the other is optimizing system sensor design to get outstanding cost performance. So feature selection become usually necessary to reduce dimensionality of the combination of multi-sensor features and improve system performance in system design. In general  there are two main missions. One is improving the recognition correct rate as soon as possible  the optimization is usually applied to feature selection because of its computational feasibility and validity. For further improving recognition accuracy and reducing selected feature dimensions  this paper presents a more rational and accurate optimization  Bidirectional Binary Particle Swarm Optimization (BBPSO) algorithm for feature selection in feature-level fusion target recognition system. In addition  we introduce a new evaluating function as criterion function in BBPSO feature selection method. At the last  we utilized Leave-One-Out method to validate the proposed method. The experiment results show that the proposed algorithm improves classification accuracy by two percentage points  while the selected feature dimensions are less one dimension than original Particle Swarm Optimization approach with 16 original feature dimensions. 2009 IEEE.   Application of multi-sensors parallel fusion system in photoelectric tracing (EI CONFERENCE) 会议论文  OAI收割 2008 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Applications, November 16, 2008 - November 19, 2008, Beijing, China Cheng G.-Y.; Cai S.; Gao H.-B.; Zhang S.-M.; Qiao Y.-F. 收藏  |  浏览/下载：15/0  |  提交时间：2013/03/25 To solve the real-time and reliability problem of tracking servo-control system in optoelectronic theodolite  a multisensors parallel processing system was proposed. Misdistances of three different wavebands were imported into system  and then prediction was done in DSP1 to get the actual position information. Data fusion was accomplished in PPGA imported by multi channel buffer serial port. The compound position information was used to control the theodolite. The results were compared with external guide data in DSP2 to implement correction of above calculation  and then were imported to epistemic machine through PXI interface. The simulation experiment of each calculation unit showed that this system could solve the real-time problem of feature level data fusion. The simulation result showed that the system can satisfy the real-time requirement with 1.25ms in theodolite with three imaging systems  while sampling frequency of photoelectric encoder was 800 Hz. 2009 SPIE.