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A new continuous max-flow algorithm for multiphase image segmentation using super-level set functions
Affiliation:1. School of Mathematical Sciences, Beijing Normal University, Laboratory of Mathematics and Complex Systems, Ministry Education, Beijing 100875, PR China;2. Department of Mathematics, University of Bergen, Johaness Brunsgate 12, 5007 Bergen, Norway;3. Department of Mathematics, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong;1. Institute of Computer and Communication Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, ROC;2. Department of Electrical Engineering, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan, ROC;3. Department of Computer Science and Information Engineering, Da-Yeh University, No. 168, University Road, Da-Tsuen, Changhua County 515, Taiwan, ROC;1. Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia;2. Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Melaka, Malaysia;1. School of Mechanical and Electronic Engineering, Jingdezhen Ceramic Institute, Jiangxi 333403, China;2. School of Information Science and Technology, Sun Yat-Sen University, Guangdong 510275, China;3. School of Electronic and Information Engineering, South China University of Technology, Guangdong 510006, China;1. School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, PR China;2. Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology, Nanjing 210094, PR China
Abstract:
Keywords:Image segmentation  Global minimization  Graph cut  Continuous max-flow  Super-level set functions  Convex relaxation  Augmented Lagrangian method  Potts model
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