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均值移动算法收敛点处性质研究

Research on the Property of the Mean-shift at the Convergent Point
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摘要 均值移动算法是一种统计迭代算法,目前在图像处理中得到了广泛的应用,但是对其性质的分析仍然不完善。文章主要研究对均值移动点存在的条件、均值移动步长处密度梯度为零的充要条件。 Mean shift is an effective iterative algorithm.It has been widely used in the image processing and computer vision,but whose properties are not been perfectly proved.The existent condition of the mean-shift dot and the necessary and sufficient condition of the density gradient which is equal to zero at the mean-shift dot are researched in the paper.
作者 帅高山 黄彦
机构地区 海军驻宜昌地区军事代表室 中国船舶重工集团公司第
出处 《计算机与数字工程》 2010年第4期18-22,共5页 Computer & Digital Engineering
关键词 均值移动 稳定性 密度递增性 密度梯度 mean-shift stability increase on the density density gradient
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
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  • 1彭宁嵩,杨杰,刘志,张风超.Mean-Shift跟踪算法中核函数窗宽的自动选取[J].软件学报,2005,16(9):1542-1550. 被引量：165
  • 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.
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计算机与数字工程
2010年 第4期

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