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	Adaptive pixon represented segmentation (APRS) for 3D MR brain images based on mean shift and Markov random fields 期刊论文 OAI收割 PATTERN RECOGNITION LETTERS, 2011, 卷号: 32, 期号: 7, 页码: 1036-1043 作者: Lin, Lei; Garcia-Lorenzo, Daniel; Li, Chong; Jiang, Tianzi; Barillot, Christian 收藏 \| 浏览/下载：35/0 \| 提交时间：2015/08/12 MRI segmentation Markov random field Adaptive mean shift Pixon-representation EM algorithm
	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).
	Adaptive pyramid mean shift for global real-time visual tracking 期刊论文 OAI收割 IMAGE AND VISION COMPUTING, 2010, 卷号: 28, 期号: 3, 页码: 424-437 作者: Li, Shu-Xiao; Chang, Hong-Xing; Zhu, Cheng-Fei 收藏 \| 浏览/下载：24/0 \| 提交时间：2015/08/12 Global visual tracking Fast mean shift Adaptive level Kernel-based tracking Tracking and pointing subsystem
	Adaptive deformation estimation of moving target by weight image analysis (EI CONFERENCE) 会议论文 OAI收割 2010 2nd International Conference on Future Computer and Communication, ICFCC 2010, May 21, 2010 - May 24, 2010, Wuhan, China Bai X.-G.; Dai M. 收藏 \| 浏览/下载：35/0 \| 提交时间：2013/03/25 An algorithm based on weight image analysis is proposed for adaptive deformation estimation of moving target in mean-shift tracking method. At the first we get the weight image from the target candidate region. Then we analyze the differences between the object and background. According to that the area estimation of the target can be converted into the image segmentation task. To realize the adaptive segmentation and estimation we define the threshold as the maximum variance between object and background. Combining the estimated area and covariance matrix we can estimate the width height and orientation of the object. The experimental results on three representative video sequences validate its robustness to the deformable estimation of the targets. 2010 IEEE.
	Head tracking using shapes and adaptive color histograms 期刊论文 OAI收割 JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2002, 卷号: 17, 期号: 6, 页码: 859-864 作者: 收藏 \| 浏览/下载：23/0 \| 提交时间：2015/11/08 adaptive color histogram mean shift head tracking