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	Visual group target tracking algorithm based on MeanShift-PCA-PF 会议论文 OAI收割 Hybrid, Xi'an, China, 2023-04-21 作者: Li, Jianing; Tian, Yan; Guo, Min; Zuo, Kaige; Wang, Xin \| 收藏 \| 浏览/下载：18/0 \| 提交时间：2023/11/09 group targets target tracking clustering detection particle filtering
	An adaptive group LASSO approach for domain selection in functional generalized linear models 期刊论文 OAI收割 JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2022, 卷号: 219, 页码: 13-32 作者: Sun, Yifan; Wang, Qihua \| 收藏 \| 浏览/下载：42/0 \| 提交时间：2022/06/21 Functional data Adaptive LASSO Group LASSO Null region detection B-spline
	Detection of Fake Reviews Using Group Model 期刊论文 OAI收割 MOBILE NETWORKS & APPLICATIONS, 2020, 页码: 13 作者: Li, Yuejun; Wang, Fangxin; Zhang, Shuwu; Niu, Xiaofei \| 收藏 \| 浏览/下载：36/0 \| 提交时间：2021/01/06 Fake review detection Opinion spamming Review group detection Reviewer group Reviewer collusion
	A Slimmer Network with Polymorphic and Group Attention Modules for More Efficient Object Detection in Aerial Images 期刊论文 OAI收割 REMOTE SENSING, 2020, 卷号: 12, 期号: 22, 页码: 30 作者: Guo, Wei; Li, Weihong; Li, Zhenghao; Gong, Weiguo; Cui, Jinkai \| 收藏 \| 浏览/下载：32/0 \| 提交时间：2021/02/24 aerial images object detection channel pruning polymorphic module (PM) group attention module (GAM)
	Patch-based topic model for group detection 期刊论文 OAI收割 SCIENCE CHINA-INFORMATION SCIENCES, 2017, 卷号: 60, 期号: 11 作者: Chen, Mulin; Wang, Qi; Li, Xuelong \| 收藏 \| 浏览/下载：27/0 \| 提交时间：2017/12/26 Group Detection Collective Behavior Crowd Analysis Latent Topic
	Fusing cross-media for topic detection by dense keyword groups 期刊论文 OAI收割 NEUROCOMPUTING, 2015, 卷号: 169, 页码: 169-179 作者: Zhang, Weigang; Chen, Tianlong; Li, Guorong; Pang, Junbiao; Huang, Qingming \| 收藏 \| 浏览/下载：32/0 \| 提交时间：2019/12/13 Topic detection Cross-media Dense keyword group Near-duplicate keyframe Web video
	Learning Symmetry Features for Face Detection Based on Sparse Group Lasso 会议论文 OAI收割 Jinan, China, 2013年11月16-17日作者: Qi Li; Zhenan Sun; Ran He(赫然); Tieniu Tan; Li, Qi \| 收藏 \| 浏览/下载：15/0 \| 提交时间：2016/06/22 Face Detection Sparse Group Lasso Minimal Redundancy Maximal Relevance
	The application of diagnostic equipment in the Tokamak fusion reaction (EI CONFERENCE) 会议论文 OAI收割 2011 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments, November 6, 2011 - November 9, 2011, Beijing, China Zhang B.-S.; Chang J.; Gong X.-Z.; Gan J.-F.; Feng S.-L. 收藏 \| 浏览/下载：18/0 \| 提交时间：2013/03/25 This paper introduces the infrared optical system in the Tokamak fusion reaction device. In this optical system 3.the relay group lenses. This paper describes the decrease of the modulation transfer function (MTF) when the temperature changes and how to compensate the decrease of the MTF in order to maintain the image quality in a high level. As a result the traditional optical structure can't meet the requirements 2.the Cassegrain system the image quality of this optical system can reach the requirements when the temperature changes. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE). because the length of the infrared optical system in the Tokamak is very long. The design of optical system in the detection facility includes three parts:1.the combination of the concave aspheric mirror and flat mirror
	A segment detection method based on improved Hough transform (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China 作者: Yao Z.-J. 收藏 \| 浏览/下载：25/0 \| 提交时间：2013/03/25 Hough transform is recognized as a powerful tool in shape analysis which gives good results even in the presence of noise and the disconnection of edge. However 3. applying the standard Hough transform equation to every point of the input image edge 4. according to the local threshold 6. merging the segments whose extreme points are near. Experiment results show the approach not only can recognize regular geometric object but also can extract the segment feature of real targets in complex environment. So the proposed method can be used in the target detection of complicated scenes traditional Hough transform can only detect the lines 2. quantizing the parameter space and extracting a group of maximums according to the global threshold eliminating spurious peaks which are caused by the spreading effects and will improve the precision of tracking. cannot give the endpoints and length of the line segments and it is vulnerable to the quantization errors. Based on the analysis of its limitations Hough transform has been improved in order to detect line segment feature of targets. The algorithm aims to avoid the loss of spatial information as well as to eliminate the spurious peaks and fix on the line segments endpoints accurately 5. fixing on the endpoints of the segments according to the dynamic clustering rule which can expediently be used for the description and classification of regular objects. The method consists of 6 steps: 1. setting up the image parameter and line-segment spaces