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Volume 28 Issue 2
Aug.  2010
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Liu Rong, Duan Fu-qing, Liu San-yang, Wu Fu-chao. Mean Shift Based Adaptive Filtering and Its Applications to Spectra Signal Processing[J]. Journal of Electronics & Information Technology, 2006, 28(2): 312-316.
Citation: Liu Rong, Duan Fu-qing, Liu San-yang, Wu Fu-chao. Mean Shift Based Adaptive Filtering and Its Applications to Spectra Signal Processing[J]. Journal of Electronics & Information Technology, 2006, 28(2): 312-316.

Mean Shift Based Adaptive Filtering and Its Applications to Spectra Signal Processing

  • Received Date: 2004-07-27
  • Rev Recd Date: 2005-05-24
  • Publish Date: 2006-02-19
  • An adaptive bilateral filtering method based on mean shift algorithm is presented. The filter is governed by the kernel width in spatial domain, which controls the spatial extent of nearby data for filtering. Its kernel width in range domain is chosen adaptively by the local characteristic of the signal. It can remove impulsive noise and improve smoothing of non-impulsive noise with edges preserved. Comparisons with Gaussian filter and median filter were made. Applications to spectra signal processing show this method can suppress noises in spectra effectively and reduce the amount of smoothing near spectral lines.
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  • Yu L Y, Wenyuan, Allen Tannenbaum, et al.. Behavioral analysis of anisotropic diffusion in image processing. IEEE Trans on Image Processing, 1996, 5 (11): 15391553. .[2]Joachim Weickert. A review of nonlinear diffusion filtering. Proceedings of the First International Conference on Scale-Space Theory in Computer Vision, Utrecht, The Netherlands, 1997. July 02-04, 1252: 328. .[3]Tomasi C, Manduchi R. Bilateral filtering for gray and colorimages. Proc. Sixth International Conference on Computer Vision, Bombay, India, 1998, January 04 - 07: 839.846.[4]Danny Barash. A fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation. IEEE Trans on PAMI, 2002, 24(6): 844847. .[5]Fukunaga K, Hostetler L D. The estimation of the gradient of a density function with applications in pattern recognition. IEEE Trans on information Theory, 1975, 21: 3240. .[6]Comaniciu D, Meer P. Mean shift: a robust approach toward feature space anaysis. IEEE Trans. on PAMI, 2002, 24(5): 603.619.[7]Silverman B W. Density Estimation for Statistics and Data Analysis.[J].New York: Chapman Hall.1986,:-[8]Sheather S J, Jones M C. A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society Series B, 1991, 53 (3): 683.690.[9]Comaniciu D, Ramesh V, Meer P. The variable bandwidth mean shift and data-driven scale selection. Proc. Eighth International Conference on Computer Vision, Vancouver, Canada, 2001, 2: 142.149.[10]Wand M P.[J].Jones M. Kernel Smoothing. New York: Chapman Hall.1995,:-[11]周虹, 黄凌云, 罗曼丽. 一种基于Hough变换和神经网络的分层类星体识别方法.电子科学学刊, 2000, 22(4): 529.535.[12]覃冬梅. 天体光谱信号的自动识别方法研究. 博士论文, 中国科学院自动化所, 2003.
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