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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xue Lin Dongsheng Fang Fangfang Li Ke Wang Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China School of Information Science and Engineering University of Jinan Jinan China Institute of Electronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing China School of Information Science and Engineering Henan University of Technology Zhengzhou China

Nonlocal interferometric phase filtering methods achieve excellent performance in both noise reduction and texture preservation, even in the case of complicated topography and low coherence. The main limitation of the nonlocal methods is the computational burden. This paper proposed a nonlocal phase filtering strategy for the practical InSAR system, which combine the nonlocal algorithm with the traditional method to improve the efficiency.

关键词： Filtering Coherence Noise reduction Frequency estimation Estimation Algorithm design and analysis Surfaces

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A New Logistic Map Based Chaotic Biogeography-based Optimization Approach for Cluster Analysis

A New Logistic Map Based Chaotic Biogeography-based Optimiza...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang School of Computer Science and Technology Nanjing Normal University Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Department of Electrical Engineering The city college of New YorkCUNY Department of Mechanical and Control Engineering Kyushu Institute of Technology School of Information Engineering Yangzhou University School of Physics and Electronic Engineering Taizhou University School of Electronic Information Shanghai Dianji University School of Energy and Mechanical Engineering Nanjing Normal University Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： chaotic biogeography-based optimization cluster analysis centroid-based clustering simulation particle swarm optimization

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A new logistic map based chaotic biogeography-based optimization approach for cluster analysis

A new logistic map based chaotic biogeography-based optimiza...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization (CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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A survey on video-based vehicle behavior analysis algorithms

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Journal of Multimedia 2012年第3期7卷 223-230页

作者： Wu, Jian Cui, Zhi-Ming Chen, Jian-Ming Zhang, Guang-Ming The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China Jiangsu Yihe Traffic Engineering Co. Ltd Suzhou 215002 China

Analysis of the Vehicle Behavior is mainly to analyze and identify the vehicles' motion pattern, and describe it by the use of natural language. It is a considerable challenge to analyze and describe the vehicles' behavior in a complex scene. This paper first hackles the development history of the intelligent transportation system and analysis of vehicles' behavior, and then conducts an indepth analysis of current situation of vehicle behavior analysis from the video processing, video analysis and video understanding, summarizes the achieved results and the key technical problems, and prospects the future development of vehicle behavior analysis. © 2012 ACADEMY PUBLISHER.

关键词： Video signal processing

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Adaptive detection of moving vehicle based on on-line clustering

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Journal of Computers 2011年第10期6卷 2045-2052页

作者： Wu, Jian Xia, Jie Chen, Jian-Ming Cui, Zhi-Ming The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China

Detection of moving vehicles plays a very important role in intelligent Transport. Aiming at the deficiency of moving vehicle detection, we proposed the adaptive detection method of moving vehicles based on the online clustering. First extracts background adaptively using a new online clustering algorithm which does not need to set any parameters when extract the background image. Then adaptively select the background updating rate according to the road disturbance of background and illumination changes after background building is completed. Finally, realize correct and complete moving object segmentation through foreground detection using background difference. Experimental results show that the proposed method is able to detect moving target accurately in the transport video, it not only has good self-adaptability and real-time but also insensitive to the light changes and background interference. © 2011 ACADEMY PUBLISHER.

关键词： Clustering algorithms

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Moving object classification method based on SOM and K-means

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Journal of Computers 2011年第8期6卷 1654-1661页

作者： Wu, Jian Xia, Jie Chen, Jian-ming Cui, Zhi-ming The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China

We do research on moving object classification in traffic video. Our aim is to classify the moving objects into pedestrians, bicycles and vehicles. Due to the advantage of self-organizing feature map (SOM), an unsupervised learning algorithm, which is simple and self organization, and the common usage of K-means clustering method, this paper combines SOM with K-means to do classification of moving objects in traffic video, constructs a system including four parts, and proposes a method based on bidirectional comparison of centroid to do tracking, and an improved method to obtain initial background when using background subtraction method to detect motion of moving objects. Experimental results show the effectiveness and robustness of the proposed approach. © 2011 ACADEMY PUBLISHER.

关键词： Self organizing maps

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A new quadtree-based terrain LOD algorithm

Journal of Software

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Journal of Software 2010年第7期5卷 769-776页

作者： Wu, Jian Yang, Yuan-feng Gong, Sheng-rong Cui, Zhi-ming Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China

Terrain LOD algorithm is a dynamic and local dough sheet subduction algorithm. On the basis of the research on traditional quadtree algorithm, this paper proposed a new terrain LOD algorithm using quadtree. On deviation computing standard, besides static deviation and view distance, motion vector and observation vector are introduced, which made the result of deviation computing close to the accurate deviation value. On crack elimination, a new crack elimination algorithm is proposed, which increases the list of crack polygon specially and eliminates all the cracks by one time rendering. So, the judge of crack vertex is not required in the course of terrain block segmentation and rendering. The experimental result shows that our algorithm is practical and effective, which can meet the need of real-time rendering for massive terrain data. © 2010 ACADEMY PUBLISHER.

关键词： Cracks

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Gray cerebrovascular image skeleton extraction algorithm using level set model

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Journal of Multimedia 2010年第3期5卷 208-215页

作者： Wu, Jian Zhang, Guang-ming Xia, Jie Cui, Zhi-ming Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou 215006 China The Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China

The ambiguity and complexity of medical cerebrovascular image makes the skeleton gained by conventional skeleton algorithm discontinuous, which is sensitive at the weak edges, with poor robustness and too many burrs. This paper proposes a cerebrovascular image skeleton extraction algorithm based on Level Set model, using Euclidean distance field and improved gradient vector flow to obtain two different energy functions. The first energy function controls the obtain of topological nodes for the beginning of skeleton curve. The second energy function controls the extraction of skeleton surface. This algorithm avoids the locating and classifying of the skeleton connection points which guide the skeleton extraction. Because all its parameters are gotten by the analysis and reasoning, no artificial interference is needed. © 2010 ACADEMY PUBLISHER.

关键词： Extraction

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Identifying query interfaces of deep web entries automatically

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Journal of Computational information Systems 2009年第3期5卷 1477-1485页

作者： Cui, Zhiming Fang, Wei Huang, Li Xian, Xuefeng Zhang, Shukui Jiangsu Provincial Key Laboratory of Computer Information Processing Technology Soochow University Suzhou 215006 China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 China

A large number of high-quality web information is deeply hidden in the Web, which can not be indexed by conventional search engines, be called Deep Web. Because query interface is the only entrance to the Deep Web, we should find Deep Web query entries firstly. In this paper, we propose a new strategy that automatically and accurately identifies Deep Web entries based on Maximum Entropy and Bayes classifier from features that extracted from web form, link and page. Our experiments over real web pages show that the new approach can classify Deep Web entries accurately and efficiently. 1553-9105/ Copyright © 2009 Binary information Press.

关键词： Search engines

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