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基于均值漂移的模糊C均值聚类图像分割方法 被引量:2

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摘要 本文针对模糊C均值聚类(FCM)算法在分割噪声图像和含有颜色相近区域的图像时存在的不足,提出了一种结合各向异性均值漂移的模糊C均值聚类(FCM)新算法。该算法在传统的FCM算法中引入了均值漂移(MS)算法,分割图像时利用MS算法可快速找到峰值点和图像空间信息的优点,对颜色漂移区域和细长区域均能保留更多的图像信息,同时具有较强的抗噪能力。
作者 王建存
出处 《电子技术与软件工程》 2013年第21期111-112,共2页 ELECTRONIC TECHNOLOGY & SOFTWARE ENGINEERING
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  • 1李乡儒,吴福朝,胡占义.均值漂移算法的收敛性[J].软件学报,2005,16(3):365-374. 被引量:89
  • 2Comaniciu D, Ramesh V, Meer P. Real-Time tracking of non-rigid objects using mean shift. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 2000. 142-149.
  • 3Comaniciu D, Ramesh V. Mean shift and optimal prediction for efficient object tracking. In: Mojsilovic A, Hu J, eds. Proc. of the IEEE Int'l Conf. on Image Processing (ICIP). 2000. 70-73.
  • 4Comaniciu D, Ramesh V, Meer P. The variable bandwidth mean shift and data-driven scale selection. In: Proc. of the IEEE Int'l Conf. on Computer Vision (ICCV). 2001. 438-445. http://citeseer.csail.mit.edu/comaniciu01variable.html.
  • 5Comaniciu D, Meer P. Mean shift analysis and applications. In: Proc. of the IEEE Int'l Conf. on Computer Vision (ICCV). 1999. 1197-1203. http://citeseer.ist.psu.edu/comaniciu00realtime.html.
  • 6Bradski GR. Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal, 1998. http://developer. intel.com/technology/itj/q21998/articles/art_2.htm.
  • 7Comaniciu D, Meer P. Mean shift: A robust approach toward feature space analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002,24(5):603-619.
  • 8Comaniciu D. An algorithm for data-driven bandwidth selection. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003, 25(2):281-288.
  • 9Comaniciu D. Nonparametric information fusion for motion estimation. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). 2003. 59-66. http://csdl.computer.org/comp/proceedings/cvpr/2003/1900/01/190010059abs.htm.
  • 10Comaniciu D, Ramesh V, Meer P. Kernel-Based object tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2003, 25(5):564-575.

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  • 1杨勇,郑崇勋,林盘,潘晨,顾建文.基于改进的模糊C均值聚类图像分割新算法[J].光电子.激光,2005,16(9):1118-1122. 被引量:20
  • 2Zadeh L A. Fuzzy sets[J]. Information and Control, 1965, 8(3): 338-353.
  • 3Bezdek J C. Pattern recognition with fuzzy objective function algorithms[M]. New York: Plenum Press, 1981.
  • 4Lou Xiaojun, Li Junying, Liu Haitao. Improved fuzzy C-means clustering algorithm based on cluster density[J]. Journal of Computational Information Systems,2012, 8(2): 727-737.
  • 5Ambroise C, Govaert G. Convergence of an EM-type algorithm for spatial clustering[J]. Pattern Recogni- tion Letters, 1998, 19(10): 919-927.
  • 6Ayech M W, E1 Kalti K, E1 Ayeb 13. Image segmen- tation based on adaptive fuzzy-C-means clustering [C]. Pattern Recognition (ICPR), 2010 20th Inter- national Conference, New York: IEEE, 2010 : 2306- 2309.
  • 7Roweis S. EM algorithms for PCA and SPCA[J]. In Advances in Neural Information Processing Systems, 1998, 626-632.
  • 8杨卫莉,郭雷,许钟,肖谷初,赵天云.基于区域生长和蚁群聚类的图像分割[J].计算机应用研究,2008,25(5):1579-1581. 被引量:16
  • 9蒲蓬勃,王鸽,刘太安.基于粒子群优化的模糊C-均值聚类改进算法[J].计算机工程与设计,2008,29(16):4277-4279. 被引量:18
  • 10熊平,黎妲,徐平.一种基于Matlab的CT脑组织图像提取算法[J].生物医学工程学进展,2009,30(1):17-19. 被引量:4

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