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Portrait image segmentation based on improved grabcut algorithm 6th

Portrait image segmentation based on improved grabcut algori...

6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Li, Shuai Zheng, Xiaohui Chen, Xianjun Zhan, Yongsong Key Laboratory of Cloud Computing and Complex System Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Experiment Center of Information Science Guilin University of Electronic Technology Guilin Guangxi541004 China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin Guangxi541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin Guangxi541004 China Information Engineering School Haikou College of Economics Haikou Hainan571127

ISBN: (纸本)9783319204680

Traditional computer portrait caricature system mainly take the method that exaggerate and deform real images directly, that lead the facial image background also been deformed when exaggerate facial image. If in pretreatment stage, we segmented the characters and background of the input image, and then do the subsequent processing, the problem may be solved. But for better portrait caricature effects, we need an excellent segmentation algorithm. So, we propose an improved Grabcut image segmentation algorithm and use it to extract the prospect character image for exaggeration and deformation. In practical application, we separate deform and exaggerate the foreground characters image with TPS method, then fuse it with the original or new background picture, get the final image. Application proves, the method solves the background deformation problem well, and improves the quality and rate of image segmentation, caricature synthesis effect reality and natural. © Springer International Publishing Switzerland 2015.

关键词： Image segmentation

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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DV-hop localization algorithm with cuckoo search

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Sensor Letters 2014年第2期12卷 444-447页

作者： Sun, Bin Cui, Zhihua Dai, Chaohua Chen, Weirong Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu China School of Electric Engineering Southwest Jiaotong University Sichuan China

How to locate the nodes is an important problem for wireless sensor network. To solve it, many algorithms are proposed, and DV-Hop is one famous example. However, the location accuracy is not well for DV-Hop. Therefore, in this paper, cuckoo search (CS), a novel intelligent optimization algorithm, is incorporated into the methodology of DV-Hop to improve the performance. Simulation results show the proposed hybrid algorithm achieves better performance when compared with the standard DV-Hop. Copyright © 2014 American Scientific Publishers.

关键词： Sensor nodes

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Artificial plant optimization algorithm with dynamic local search for optimal coverage configuration

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Sensor Letters 2014年第1期12卷 118-122页

作者： Cui, Zhihua Liu, Wenjin Dai, Chaohua Chen, Weirong Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi 030024 China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu 210093 China School of Electric Engineering Southwest Jiaotong University Sichuan 610031 China

Artificial plant optimization algorithm (APOA) is a novel stochastic population-based evolutionary algorithm by simulating the plant growing process. However, the exploitation capability of APOA is not enough. Therefore, in this paper, a new dynamic local search strategy is designed and incorporated into methodology of APOA. To illustrate the efficiency, this new variant is applied to solve the optimal coverage configuration problem in wireless sensor networks, simulation results show the validity of this variant. Copyright © 2014 American Scientific Publishers.

关键词： Stochastic systems

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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RNA secondary structure prediction with guided binary-coded cuckoo search

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Journal of Bionanoscience 2014年第5期8卷 391-394页

作者： Sun, Bin Zhu, Zhuanghua Cui, Zhihua Dai, Chaohua Chen, Weirong Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technology Shanxi030024 China Economic Information Department Shanxi Finance and Taxation College Shanxi030024 China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu210093 China School of Electric Engineering Southwest Jiaotong University Sichuan610031 China

Cuckoo search algorithm (CS) is a new intelligent optimization algorithm inspired by the brood parasitism phenomenon. Due to the simple concepts and easy implementation, it has been applied to many applications. In this paper, a new variant, guided binary-coded cuckoo search with binarycoded manner, is designed by incorporating one guided search direction to enhance the local search capability. To investigate the performance, we apply it to predict the RNA secondary structure problem. Ten famous sequences are selected and compared with Mfold, simulation results show the validity. Copyright © 2014 American Scientific Publishers.

关键词： RNA

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Multi-granularity dynamic analysis of complex software networks

Multi-granularity dynamic analysis of complex software netwo...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Bing Li Weifeng Pan Jinhu Lü State Key Laboratory Software Engineering and School Computer and Complex Network Research Center Wuhan University of China Wuhan China State Key Laboratory Software Engineering Wuhan University of China Wuhan China School Electrical Computer Engineering RMIT University Melbourne Australia Acad. Mathematics System Science Chinese Academy and Sciences Beijing China

software systems represent one of the most complex man-made systems. In this paper, we analyze the evolution of Object-Oriented (OO) software using complex network theory from a multi-granularity perspective. First, the software net works are constructed for a multi-version software system at different levels of granularity. Then, some parameters used in complex network theory are introduced to study the topological characteristics of these software networks. By investigating the parameters' values in consecutive software networks, we have a better understanding about software evolution. A case study on an open source OO project, Azureus, is conducted as an example to illustrate our approach. It uncovers some underlying dynamic characteristics of OO systems. These results provide a different dimension to our understanding of software system dynamics and also are very useful for the design and development of OO software systems.

关键词： Logic gates Communities US Department of Defense

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Repulsive Particle Swarm Optimization Based on New Diversity

Repulsive Particle Swarm Optimization Based on New Diversity

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The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

作者： Guochao Niu Baodi Chen Jianchao Zeng Software Engineering Institute Xidian University Xi'an 710071China Software Engineering Institute Xidian University Xi'an 710071China Complex System and Computation Complex System and Computational Intelligence Laboratory Taiyuan University of Science and Technolo

ISBN: (纸本)9781424451814;9781424451821

To avoid the problem of premature convergence, a new diversity-guided Particle Swarm Optimizer (PSO), namely MARPSO is proposed, which is a modification of attractive and repulsive PSO (ARPSO), suggested by Riget and Vesterstorm[1]. A novel measure of population diversity function is presented and a new concept of the particles best flight direction is introduced. The simulation test results of four classic functions show that: compared with Standard PSO (BPSO) and ARPSO, MARPSO can effectively increase the diversity of swarm, while maintain a higher convergence speed.

关键词： Particle Swarm Optimization Population Diversity Global Search Particle's Best Flight Direction

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