中国科学院机构知识库网格系统: 检索

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	Ground Moving Target Detection With Adaptive Data Reconstruction and Improved Pseudo-Skeleton Decomposition 期刊论文 OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 卷号: 62, 页码: 14 作者: He, Xiongpeng; Liu, Kun; Gu, Tong; Liao, Guisheng; Zhu, Shengqi \| 收藏 \| 浏览/下载：18/0 \| 提交时间：2024/12/06 Clutter Object detection Sparse matrices Principal component analysis Matrix decomposition Image reconstruction Estimation Data reconstruction (DR) ground moving target indication (GMTI) joint-pixel model pseudo-skeleton decomposition (IPSD) robust principal component analysis (RPCA)
	Cross stage partial connections based weighted Bi-directional feature pyramid and enhanced spatial transformation network for robust object detection 期刊论文 OAI收割 NEUROCOMPUTING, 2022, 卷号: 513, 页码: 70-82 作者: Lu, Yan-Feng; Yu, Qian; Gao, Jing-Wen; Li, Yi; Zou, Jun-Cheng \| 收藏 \| 浏览/下载：117/0 \| 提交时间：2022/11/14 Robust object detection Structural deformation Image detection Spatial transformation
	Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection 期刊论文 OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 卷号: 57, 期号: 8, 页码: 5535-5548 作者: Zhang, Yuanlin; Yuan, Yuan; Feng, Yachuang; Lu, Xiaoqiang \| 收藏 \| 浏览/下载：229/0 \| 提交时间：2019/08/13 Convolutional neural networks (CNNs) hierarchical robust CNN (HRCNN) hierarchical spatial semantic (HSS) object detection remote sensing images (RSIs) rotation and scaling robust enhancement (RSRE)
	Small target detection based on reweighted infrared patch-image model 期刊论文 OAI收割 IET IMAGE PROCESSING, 2018, 卷号: 12, 期号: 1, 页码: 70-79 作者: Guo, Jun; Wu, Yiquan; Dai, Yimian \| 收藏 \| 浏览/下载：76/0 \| 提交时间：2018/12/12 Object Detection Infrared Imaging Principal Component Analysis Small Target Detection Reweighted Infrared Patch-image Model Infrared Small Target Detection Sparse Background Edges Background Estimation Reweighted Nuclear Norm Nontarget Sparse Points Reweighted Robust Principal Component Analysis Problem Inexact Augmented Lagrangian Multiplier Method Background Clutter Suppression Reweighted l(1) Norm
	Study particle filter tracking and detection algorithms based on DSP signal processors (EI CONFERENCE) 会议论文 OAI收割 2010 International Conference on Computer, Mechatronics, Control and Electronic Engineering, CMCE 2010, August 24, 2010 - August 26, 2010, Changchun, China Dong Y.; Chuan W. 收藏 \| 浏览/下载：21/0 \| 提交时间：2013/03/25 In Video tracking detection and tracking usually need two algorithms. The process is complex and need much time which detection and tracking are. In this paper a hybrid valued sequential state vector is formulated. The state vector is characterized by information of target appearance flag and of location. Particle filter-based method implements detection and tracking at one time. In order to reduce process time and think of pixel position in tracking field feature histogram of luminance is as observe vector and used posterior estimate. In this paper the luminance component is derived and target is recognized and tracked through image processor based on DSP in order to implementing real-time. The experimental results confirm that method can detect and track the object in real-time successfully when the number of particles is 160. The method is robust for rolling scale and partial occlusion. 2010 IEEE.
	Study on color image tracking and detection algorithms based on particle filter (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2009: Advances in Infrared Imaging and Applications, June 17, 2009 - June 19, 2009, Beijing, China Wu C.; Sun H.-J.; Yang D. 收藏 \| 浏览/下载：20/0 \| 提交时间：2013/03/25 In Video tracking detection and tracking need two algorithms. The process is complex and need much time which detection and tracking is. In this paper a hybrid valued sequential state vector is formulated. The state vector is characterized by information of target appearance and of location. Particle filter-based method implements detection and tracking. In order to reduce process time and think of pixel position in tracking field feature histogram of color-based is as observe vector and used posterior estimate. The experimental results confirm that method can detect and track object in 17.68ms successfully when the number of particles is 160. The method is robust for rolling scale and partial occlusion. 2009 SPIE.