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	A Precise Method to Calibrate Dynamic Integration Errors in Shallow- and Deep-Water Multibeam Bathymetric Data 期刊论文 OAI收割 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 页码: 14 作者: Bu, Xianhai; Mei, Sai; Yang, Fanlin; Luan, Zhendong; Xu, Fangzheng \| 收藏 \| 浏览/下载：45/0 \| 提交时间：2022/02/18 Structural beams Transducers Calibration Motion detection Tides Geology Global navigation satellite system Differential evolution (DE) georeferenced model integration errors motion sensor multibeam echo-sounder system (MBES)
	The new approach for infrared target tracking based on the particle filter algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China 作者: Sun H.; Han H.-X.; Sun H. 收藏 \| 浏览/下载：63/0 \| 提交时间：2013/03/25 Target tracking on the complex background in the infrared image sequence is hot research field. It provides the important basis in some fields such as video monitoring precision and video compression human-computer interaction. As a typical algorithms in the target tracking framework based on filtering and data connection the particle filter with non-parameter estimation characteristic have ability to deal with nonlinear and non-Gaussian problems so it were widely used. There are various forms of density in the particle filter algorithm to make it valid when target occlusion occurred or recover tracking back from failure in track procedure but in order to capture the change of the state space it need a certain amount of particles to ensure samples is enough and this number will increase in accompany with dimension and increase exponentially this led to the increased amount of calculation is presented. In this paper particle filter algorithm and the Mean shift will be combined. Aiming at deficiencies of the classic mean shift Tracking algorithm easily trapped into local minima and Unable to get global optimal under the complex background. From these two perspectives that "adaptive multiple information fusion" and "with particle filter framework combining" we expand the classic Mean Shift tracking framework.Based on the previous perspective we proposed an improved Mean Shift infrared target tracking algorithm based on multiple information fusion. In the analysis of the infrared characteristics of target basis Algorithm firstly extracted target gray and edge character and Proposed to guide the above two characteristics by the moving of the target information thus we can get new sports guide grayscale characteristics and motion guide border feature. Then proposes a new adaptive fusion mechanism used these two new information adaptive to integrate into the Mean Shift tracking framework. Finally we designed a kind of automatic target model updating strategy to further improve tracking performance. Experimental results show that this algorithm can compensate shortcoming of the particle filter has too much computation and can effectively overcome the fault that mean shift is easy to fall into local extreme value instead of global maximum value.Last because of the gray and fusion target motion information this approach also inhibit interference from the background ultimately improve the stability and the real-time of the target track. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
	图像与视频分割　学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2006 作者: 杨明辉收藏 \| 浏览/下载：29/0 \| 提交时间：2015/09/02 分水岭分割隐马尔可夫树模型全局运动模型自动运动目标检测 watershed segmentation Hidden Markov Tree Model global motion model automatic moving target detection
	视频图像运动目标分割研究学位论文 OAI收割工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2006 作者: 王嘉收藏 \| 浏览/下载：43/0 \| 提交时间：2015/09/02 视频分析运动目标分割全局运动运动分层立体视觉立体全局运动模型智能交通 Video analysis Moving object segmentation Global motion Motion layer Sterevision Stereo global motion model Intelligent traffic system