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	Controlled synthesized natroalunite microtubes applied for cadmium(II) and phosphate co-removal 期刊论文 OAI收割 JOURNAL OF HAZARDOUS MATERIALS, 2016, 卷号: 314, 期号: 无, 页码: 249-259 作者: Xu, Huan; Zhu, Baisheng; Ren, Xuemei; Shao, Dadong; Tan, Xiaoli 收藏 \| 浏览/下载：25/0 \| 提交时间：2017/07/17 Mutual Effects Natroalunite Microtubes Addition Sequences Cadmium(Ii) Phosphate
	Effect of pH, humic acid and addition sequences on Eu(III) sorption onto gamma-Al2O3 study by batch and time resolved laser fluorescence spectroscopy 期刊论文 OAI收割 CHEMICAL ENGINEERING JOURNAL, 2016, 卷号: 287, 期号: 无, 页码: 313-320 作者: Wang, Xiangxue; Chen, Zhongshan; Tan, Xiaoli; Hayat, Tasawar; Ahmad, Bashir 收藏 \| 浏览/下载：27/0 \| 提交时间：2017/07/20 Al2o3 Eu(Iii) Humic Acid Addition Sequences Trlfs
	Efficient human action recognition using accumulated motion image and support vector machines (EI CONFERENCE) 会议论文 OAI收割 International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2011, November 19, 2011 - November 23, 2011, Suzhou, China 作者: Zhang X.; Zhang J.; Zhang J.; Zhang X.; Zhang X. 收藏 \| 浏览/下载：67/0 \| 提交时间：2013/03/25 Vision-based human action recognition provides an advanced interface and research in this field of human action recognition has been actively carried out. This paper describes a scheme for recognizing human actions from a video sequences. The proposed method is an extension of the Motion History Image(MHI) method based on the ordinal measure of accumulated motion which is robust to variations of appearances. We define the accumulated motion image(AMI) using image differences firstly. Then the AMI of the video sequencesis resized to a MN regulation following the standard of training phases. Finally we employ Support Vector Machine(SVM) as a classifier to distinguish the current activity in target video sequences. In a word our proposed algorithm not only outperforms the state of art on public available KTH data set and Weizmann data set but also proves practical to some real world applications in addition this method is computationally simple and able to achieve a satisfactory accuracy.
	Layered fast correlation tracking algorithm combined with target feature (EI CONFERENCE) 会议论文 OAI收割 4th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, November 19, 2008 - November 21, 2008, Chengdu, China 作者: Guo L.-H. 收藏 \| 浏览/下载：21/0 \| 提交时间：2013/03/25 A new correlation tracking algorithm layered fast correlation tracking algorithm combined with target feature is proposed for target tracking in image sequences. Based on traditional correlation tracking algorithm according to resolution of real-time image the proposed algorithm chooses the designated layer image. At the same time the proposed algorithm uses a new search method combined with the target features to predict matching position which can improve the matching precision and reduce computational complexity. In addition the experimental results indicate that the proposed algorithm can overcome the influence of gray mutation and satisfy the requirement of real-time. 2009 SPIE.
	Real time tracking by LOPF algorithm with mixture model (EI CONFERENCE) 会议论文 OAI收割 MIPPR 2007: Automatic Target Recognition and Image Analysis; and Multispectral Image Acquisition, November 15, 2007 - November 17, 2007, Wuhan, China Meng B.; Zhu M.; Han G.; Wu Z. 收藏 \| 浏览/下载：24/0 \| 提交时间：2013/03/25 A new particle filter-the Local Optimum Particle Filter (LOPF) algorithm is presented for tracking object accurately and steadily in visual sequences in real time which is a challenge task in computer vision field. In order to using the particles efficiently we first use Sobel algorithm to extract the profile of the object. Then we employ a new Local Optimum algorithm to auto-initialize some certain number of particles from these edge points as centre of the particles. The main advantage we do this in stead of selecting particles randomly in conventional particle filter is that we can pay more attentions on these more important optimum candidates and reduce the unnecessary calculation on those negligible ones in addition we can overcome the conventional degeneracy phenomenon in a way and decrease the computational costs. Otherwise the threshold is a key factor that affecting the results very much. So here we adapt an adaptive threshold choosing method to get the optimal Sobel result. The dissimilarities between the target model and the target candidates are expressed by a metric derived from the Bhattacharyya coefficient. Here we use both the counter cue to select the particles and the color cur to describe the targets as the mixture target model. The effectiveness of our scheme is demonstrated by real visual tracking experiments. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident when the target encountering the occlusion in real video where the standard particle filter usually fails.