期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

SOFT IMAGE SEGMENTATION BASED ON CENTER-FREE FUZZY CLUSTERING 被引量：2

SOFT IMAGE SEGMENTATION BASED ON CENTER-FREE FUZZY CLUSTERING
下载PDF
导出
摘要 Image segmentation remains one of the major challenges in image analysis.And soft image segmentation has been widely used due to its good effect.Fuzzy clustering algorithms are very popular in soft segmentation.A new soft image segmentation method based on center-free fuzzy clustering is proposed.The center-free fuzzy clustering is the modified version of the classical fuzzy C-means ( FCM ) clustering.Different from traditional fuzzy clustering , the center-free fuzzy clustering does not need to calculate the cluster center , so it can be applied to pairwise relational data.In the proposed method , the mean-shift method is chosen for initial segmentation firstly , then the center-free clustering is used to merge regions and the final segmented images are obtained at last.Experimental results show that the proposed method is better than other image segmentation methods based on traditional clustering. Image segmentation remains one of the major challenges in image analysis. And soft image segmentation has been widely used due to its good effect. Fuzzy clustering algorithms are very popular in soft segmentation. A new soft image segmentation method based on center-free fuzzy clustering is proposed. The center-free fuzzy clus- tering is the modified version of the classical fuzzy C-means （FCM） clustering. Different from traditional fuzzy clustering, the center-free fuzzy clustering does not need to calculate the cluster center, so it can be applied to pair- wise relational data. In the proposed method, the mean-shift method is chosen for initial segmentation firstly, then the center-free clustering is used to merge regions and the final segmented images are obtained at last. Experimen- tal results show that the proposed method is better than other image segmentation methods based on traditional clustering.
作者 马儒宁 朱燕 丁军娣
机构地区 College of Science School of Computer Science and Technology
出处 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2013年第1期67-76,共10页 南京航空航天大学学报（英文版）
基金 Supported by the National Natural Science Foundation of China(61103058,61233011)
关键词 soft image segmentationl fuzzy clusteringl center-free clusteringI region merging soft image segmentation fuzzy clustering center-free clustering region merging
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献18

  • 1Senthilkumaran N, Rajesh R. Image segmentation-A survey of soft computing approaches[C],//2009 International Conference on Advances in Recent Technologies in Communication and Computing (Artcom 2009). Kottayam, Kerala, India:IEEE,2009 : 844-846.
  • 2Naz S, Majeed H, Irshad H. Image segmentation using fuzzy clustering: A survey[C]// 2010 6th International Conference on Emerging Technologies (ICET). Islamabad:IEEE, 2010 : 181-186.
  • 3Prewer D, Kitchen L. Soft image segmentation by weighted linked pyramid [J]. Pattern Recognition Letters, 2001, 22(2):123-132.
  • 4Udupa J K, Saha P K, Lotufo R A. Relative fuzzy connectedness and object definition: Theory, algorithms, and applications in image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(11): 1485-1500.
  • 5Posirca I, Chen Y M, Barcelos C Z. A new stochastic variational PDE model for soft Mumford-Shah segmentation[J]. Journal of Mathematical Analysis and Applications, 2011, 384(1):104-114.
  • 6Fuhua C,Yunmei C. A stochastic variational model for multi-phase soft segmentation with bias correction [J]. Advanced Modeling and Optimization, 2010, 12 (3): 339-345.
  • 7Barcelos C A Z, Chen Y M, Chen F H, etal. Asoft multiphase segmentation model via Gaussian mixture [C]//2009 16th IEEE International Conference on Image Processing. Cairo, Eqypt: IEEE, 2009:3997- 4000.
  • 8Tang H, Dillenseger J L, Bao X D, et al. A vectorial image soft segmentation method based on neighborhood weighted Gaussian mixture model[J]. Computerized Medical Imaging and Graphics, 2009, 33 (8): 644-650.
  • 9Wang Zhen, Yang Meng. A fast clustering algorithm in image segmentation[C]//2010 2nd International Conference on Computer Engineering and Technology (ICCET). Chengdu, China: IEEE, 2010: V6-592-V6- 594.
  • 10Hung Ming-chuan, Yang Don-lin. An efficient fuzzy C-means clustering algorithm[C]//Proceedings IEEE International Conference on Data Mining. California, USA:IEEE, 2001:225-232.

同被引文献26

  • 1胡正磊,孙进平,袁运能,毛士艺.基于小波边缘提取和脊线跟踪技术的SAR图像河流检测算法[J].电子与信息学报,2007,29(3):524-527. 被引量：16
  • 2章育晋.图像工程:中册[M].3版.北京:清华大学出版社,2012:1-80.
  • 3ANDREOPOULOS A,TSOTSOS J K.50 Years of Object Recognition:Directions Forward[J].Computer Vision and Image Understanding,2013,117(8):827-891.
  • 4ARBELAEZ P,MAIRE M,FOWLKES C,et al.Contour detection and hierarchical image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(5):898-916.
  • 5PENG B,ZHANG L,ZHANG D.A survey of graph theoretical approaches to image segmentation[J].Pattern Recognition,2013,46(3):1020-1038.
  • 6ABUTALEB A S.Automatic Thresholding of GrayLevel Pictures Using Two-Dimensional Entropies[J].Computer Vision,Graphics,and Image Processing,1989,47(1):22-32.
  • 7BRINK A D.Thresholding of Digital Images Using Two-Dimensional Entropies[J].Pattern Recognition,1992,25(8):803-808.
  • 8CHANG C I,DU Y,WANG J,et al.Survey and Comparative Analysis of Entropy and Relative Entropy Thresholding Techniques[J].IEEE Proceedings Vision,Image Signal Process,2006,153(6):837-850.
  • 9SEZGIN M,SANKUR B.Survey over image thresholding techniques and quantitative performance evaluation[J].Journal of Electronic Imaging,2004,13(1):146-168.
  • 10SEZGIN M.blt_image_references[EB/OL].[2015-05-15].http://www.mehmetsezgin.net.

引证文献2

二级引证文献7

Transactions of Nanjing University of Aeronautics and Astronautics
2013年 第1期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部